---
_id: '14924'
abstract:
- lang: eng
text: "The stochastic heavy ball method (SHB), also known as stochastic gradient
descent (SGD) with Polyak's momentum, is widely used in training neural networks.
However, despite the remarkable success of such algorithm in practice, its theoretical
characterization remains limited. In this paper, we focus on neural networks with
two and three layers and provide a rigorous understanding of the properties of
the solutions found by SHB: \\emph{(i)} stability after dropping out part of the
neurons, \\emph{(ii)} connectivity along a low-loss path, and \\emph{(iii)} convergence
to the global optimum.\r\nTo achieve this goal, we take a mean-field view and
relate the SHB dynamics to a certain partial differential equation in the limit
of large network widths. This mean-field perspective has inspired a recent line
of work focusing on SGD while, in contrast, our paper considers an algorithm with
momentum. More specifically, after proving existence and uniqueness of the limit
differential equations, we show convergence to the global optimum and give a quantitative
bound between the mean-field limit and the SHB dynamics of a finite-width network.
Armed with this last bound, we are able to establish the dropout-stability and
connectivity of SHB solutions."
acknowledgement: D. Wu and M. Mondelli are partially supported by the 2019 Lopez-Loreta
Prize. V. Kungurtsev was supported by the OP VVV project CZ.02.1.01/0.0/0.0/16_019/0000765
"Research Center for Informatics".
alternative_title:
- TMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Diyuan
full_name: Wu, Diyuan
id: 1a5914c2-896a-11ed-bdf8-fb80621a0635
last_name: Wu
- first_name: Vyacheslav
full_name: Kungurtsev, Vyacheslav
last_name: Kungurtsev
- first_name: Marco
full_name: Mondelli, Marco
id: 27EB676C-8706-11E9-9510-7717E6697425
last_name: Mondelli
orcid: 0000-0002-3242-7020
citation:
ama: 'Wu D, Kungurtsev V, Mondelli M. Mean-field analysis for heavy ball methods:
Dropout-stability, connectivity, and global convergence. In: Transactions on
Machine Learning Research. ML Research Press; 2023.'
apa: 'Wu, D., Kungurtsev, V., & Mondelli, M. (2023). Mean-field analysis for
heavy ball methods: Dropout-stability, connectivity, and global convergence. In
Transactions on Machine Learning Research. ML Research Press.'
chicago: 'Wu, Diyuan, Vyacheslav Kungurtsev, and Marco Mondelli. “Mean-Field Analysis
for Heavy Ball Methods: Dropout-Stability, Connectivity, and Global Convergence.”
In Transactions on Machine Learning Research. ML Research Press, 2023.'
ieee: 'D. Wu, V. Kungurtsev, and M. Mondelli, “Mean-field analysis for heavy ball
methods: Dropout-stability, connectivity, and global convergence,” in Transactions
on Machine Learning Research, 2023.'
ista: 'Wu D, Kungurtsev V, Mondelli M. 2023. Mean-field analysis for heavy ball
methods: Dropout-stability, connectivity, and global convergence. Transactions
on Machine Learning Research. , TMLR, .'
mla: 'Wu, Diyuan, et al. “Mean-Field Analysis for Heavy Ball Methods: Dropout-Stability,
Connectivity, and Global Convergence.” Transactions on Machine Learning Research,
ML Research Press, 2023.'
short: D. Wu, V. Kungurtsev, M. Mondelli, in:, Transactions on Machine Learning
Research, ML Research Press, 2023.
date_created: 2024-02-02T11:21:56Z
date_published: 2023-02-28T00:00:00Z
date_updated: 2024-09-10T13:03:20Z
day: '28'
department:
- _id: MaMo
external_id:
arxiv:
- '2210.06819'
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2210.06819
month: '02'
oa: 1
oa_version: Published Version
project:
- _id: 059876FA-7A3F-11EA-A408-12923DDC885E
name: Prix Lopez-Loretta 2019 - Marco Mondelli
publication: Transactions on Machine Learning Research
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
status: public
title: 'Mean-field analysis for heavy ball methods: Dropout-stability, connectivity,
and global convergence'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14946'
abstract:
- lang: eng
text: "We present a unified framework for studying the identifiability of\r\nrepresentations
learned from simultaneously observed views, such as different\r\ndata modalities.
We allow a partially observed setting in which each view\r\nconstitutes a nonlinear
mixture of a subset of underlying latent variables,\r\nwhich can be causally related.
We prove that the information shared across all\r\nsubsets of any number of views
can be learned up to a smooth bijection using\r\ncontrastive learning and a single
encoder per view. We also provide graphical\r\ncriteria indicating which latent
variables can be identified through a simple\r\nset of rules, which we refer to
as identifiability algebra. Our general\r\nframework and theoretical results unify
and extend several previous works on\r\nmulti-view nonlinear ICA, disentanglement,
and causal representation learning.\r\nWe experimentally validate our claims on
numerical, image, and multi-modal data\r\nsets. Further, we demonstrate that the
performance of prior methods is\r\nrecovered in different special cases of our
setup. Overall, we find that access\r\nto multiple partial views enables us to
identify a more fine-grained\r\nrepresentation, under the generally milder assumption
of partial observability."
acknowledgement: "This work was initiated at the Second Bellairs Workshop on Causality
held at the Bellairs Research Institute, January 6–13, 2022; we thank all workshop
participants for providing a stimulating research environment. Further, we thank
Cian Eastwood, Luigi Gresele, Stefano Soatto, Marco Bagatella, and A. René Geist
for helpful discussion. GM is a member of the Machine Learning Cluster of Excellence,
EXC number 2064/1 – Project number 390727645. JvK and GM acknowledge support from
the German Federal Ministry of Education and Research (BMBF) through the Tübingen
AI Center (FKZ: 01IS18039B). The research of DX and SM was supported by the Air
Force Office of Scientific Research under award number FA8655-22-1-7155. Any opinions,
findings, and conclusions or recommendations expressed in\r\nthis material are those
of the author(s) and do not necessarily reflect the views of the United States Air
Force. We also thank SURF for the support in using the Dutch National Supercomputer
Snellius. DY was supported by an Amazon fellowship and the International Max Planck
Research School for Intelligent Systems (IMPRS-IS). Work done outside of Amazon.
SL was supported by an IVADO excellence PhD scholarship and by Samsung Electronics
Co., Ldt."
article_number: '2311.04056'
article_processing_charge: No
arxiv: 1
author:
- first_name: Dingling
full_name: Yao, Dingling
id: d3e02e50-48a8-11ee-8f62-c108061797fa
last_name: Yao
- first_name: Danru
full_name: Xu, Danru
last_name: Xu
- first_name: Sébastien
full_name: Lachapelle, Sébastien
last_name: Lachapelle
- first_name: Sara
full_name: Magliacane, Sara
last_name: Magliacane
- first_name: Perouz
full_name: Taslakian, Perouz
last_name: Taslakian
- first_name: Georg
full_name: Martius, Georg
last_name: Martius
- first_name: Julius von
full_name: Kügelgen, Julius von
last_name: Kügelgen
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: Yao D, Xu D, Lachapelle S, et al. Multi-view causal representation learning
with partial observability. arXiv. doi:10.48550/arXiv.2311.04056
apa: Yao, D., Xu, D., Lachapelle, S., Magliacane, S., Taslakian, P., Martius, G.,
… Locatello, F. (n.d.). Multi-view causal representation learning with partial
observability. arXiv. https://doi.org/10.48550/arXiv.2311.04056
chicago: Yao, Dingling, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz
Taslakian, Georg Martius, Julius von Kügelgen, and Francesco Locatello. “Multi-View
Causal Representation Learning with Partial Observability.” ArXiv, n.d.
https://doi.org/10.48550/arXiv.2311.04056.
ieee: D. Yao et al., “Multi-view causal representation learning with partial
observability,” arXiv. .
ista: Yao D, Xu D, Lachapelle S, Magliacane S, Taslakian P, Martius G, Kügelgen
J von, Locatello F. Multi-view causal representation learning with partial observability.
arXiv, 2311.04056.
mla: Yao, Dingling, et al. “Multi-View Causal Representation Learning with Partial
Observability.” ArXiv, 2311.04056, doi:10.48550/arXiv.2311.04056.
short: D. Yao, D. Xu, S. Lachapelle, S. Magliacane, P. Taslakian, G. Martius, J.
von Kügelgen, F. Locatello, ArXiv (n.d.).
date_created: 2024-02-07T14:28:34Z
date_published: 2023-11-07T00:00:00Z
date_updated: 2024-02-12T08:07:33Z
day: '07'
department:
- _id: FrLo
doi: 10.48550/arXiv.2311.04056
external_id:
arxiv:
- '2311.04056'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2311.04056
month: '11'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Multi-view causal representation learning with partial observability
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14948'
abstract:
- lang: eng
text: "The extraction of modular object-centric representations for downstream tasks\r\nis
an emerging area of research. Learning grounded representations of objects\r\nthat
are guaranteed to be stable and invariant promises robust performance\r\nacross
different tasks and environments. Slot Attention (SA) learns\r\nobject-centric
representations by assigning objects to \\textit{slots}, but\r\npresupposes a
\\textit{single} distribution from which all slots are randomly\r\ninitialised.
This results in an inability to learn \\textit{specialized} slots\r\nwhich bind
to specific object types and remain invariant to identity-preserving\r\nchanges
in object appearance. To address this, we present\r\n\\emph{\\textsc{Co}nditional
\\textsc{S}lot \\textsc{A}ttention} (\\textsc{CoSA})\r\nusing a novel concept
of \\emph{Grounded Slot Dictionary} (GSD) inspired by\r\nvector quantization.
Our proposed GSD comprises (i) canonical object-level\r\nproperty vectors and
(ii) parametric Gaussian distributions, which define a\r\nprior over the slots.
We demonstrate the benefits of our method in multiple\r\ndownstream tasks such
as scene generation, composition, and task adaptation,\r\nwhilst remaining competitive
with SA in popular object discovery benchmarks."
acknowledgement: "This work was supported by supported by UKRI (grant agreement no.
EP/S023356/1), in the UKRI\r\nCentre for Doctoral Training in Safe and Trusted AI
via A. Kori."
article_number: '2307.09437'
article_processing_charge: No
arxiv: 1
author:
- first_name: Avinash
full_name: Kori, Avinash
last_name: Kori
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Fabio De Sousa
full_name: Ribeiro, Fabio De Sousa
last_name: Ribeiro
- first_name: Francesca
full_name: Toni, Francesca
last_name: Toni
- first_name: Ben
full_name: Glocker, Ben
last_name: Glocker
citation:
ama: Kori A, Locatello F, Ribeiro FDS, Toni F, Glocker B. Grounded object centric
learning. arXiv. doi:10.48550/arXiv.2307.09437
apa: Kori, A., Locatello, F., Ribeiro, F. D. S., Toni, F., & Glocker, B. (n.d.).
Grounded object centric learning. arXiv. https://doi.org/10.48550/arXiv.2307.09437
chicago: Kori, Avinash, Francesco Locatello, Fabio De Sousa Ribeiro, Francesca Toni,
and Ben Glocker. “Grounded Object Centric Learning.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2307.09437.
ieee: A. Kori, F. Locatello, F. D. S. Ribeiro, F. Toni, and B. Glocker, “Grounded
object centric learning,” arXiv. .
ista: Kori A, Locatello F, Ribeiro FDS, Toni F, Glocker B. Grounded object centric
learning. arXiv, 2307.09437.
mla: Kori, Avinash, et al. “Grounded Object Centric Learning.” ArXiv, 2307.09437,
doi:10.48550/arXiv.2307.09437.
short: A. Kori, F. Locatello, F.D.S. Ribeiro, F. Toni, B. Glocker, ArXiv (n.d.).
date_created: 2024-02-07T14:47:04Z
date_published: 2023-07-18T00:00:00Z
date_updated: 2024-02-12T08:13:12Z
day: '18'
department:
- _id: FrLo
doi: 10.48550/arXiv.2307.09437
external_id:
arxiv:
- '2307.09437'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2307.09437
month: '07'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Grounded object centric learning
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14949'
abstract:
- lang: eng
text: Many approaches have been proposed to use diffusion models to augment training
datasets for downstream tasks, such as classification. However, diffusion models
are themselves trained on large datasets, often with noisy annotations, and it
remains an open question to which extent these models contribute to downstream
classification performance. In particular, it remains unclear if they generalize
enough to improve over directly using the additional data of their pre-training
process for augmentation. We systematically evaluate a range of existing methods
to generate images from diffusion models and study new extensions to assess their
benefit for data augmentation. Personalizing diffusion models towards the target
data outperforms simpler prompting strategies. However, using the pre-training
data of the diffusion model alone, via a simple nearest-neighbor retrieval procedure,
leads to even stronger downstream performance. Our study explores the potential
of diffusion models in generating new training data, and surprisingly finds that
these sophisticated models are not yet able to beat a simple and strong image
retrieval baseline on simple downstream vision tasks.
acknowledgement: The authors would like to thank Varad Gunjal and Vishaal Udandarao.
MFB thanks the International Max Planck Research School for Intelligent Systems
(IMPRS-IS).
alternative_title:
- TMLR
article_processing_charge: No
article_type: original
author:
- first_name: Max
full_name: Burg, Max
last_name: Burg
- first_name: Florian
full_name: Wenzel, Florian
last_name: Wenzel
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Osama
full_name: Makansi, Osama
last_name: Makansi
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Chris
full_name: Russell, Chris
last_name: Russell
citation:
ama: Burg M, Wenzel F, Zietlow D, et al. Image retrieval outperforms diffusion models
on data augmentation. Journal of Machine Learning Research. 2023.
apa: Burg, M., Wenzel, F., Zietlow, D., Horn, M., Makansi, O., Locatello, F., &
Russell, C. (2023). Image retrieval outperforms diffusion models on data augmentation.
Journal of Machine Learning Research. ML Research Press.
chicago: Burg, Max, Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco
Locatello, and Chris Russell. “Image Retrieval Outperforms Diffusion Models on
Data Augmentation.” Journal of Machine Learning Research. ML Research Press,
2023.
ieee: M. Burg et al., “Image retrieval outperforms diffusion models on data
augmentation,” Journal of Machine Learning Research. ML Research Press,
2023.
ista: Burg M, Wenzel F, Zietlow D, Horn M, Makansi O, Locatello F, Russell C. 2023.
Image retrieval outperforms diffusion models on data augmentation. Journal of
Machine Learning Research.
mla: Burg, Max, et al. “Image Retrieval Outperforms Diffusion Models on Data Augmentation.”
Journal of Machine Learning Research, ML Research Press, 2023.
short: M. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell,
Journal of Machine Learning Research (2023).
date_created: 2024-02-07T14:57:39Z
date_published: 2023-12-10T00:00:00Z
date_updated: 2024-02-12T08:30:21Z
day: '10'
ddc:
- '000'
department:
- _id: FrLo
file:
- access_level: open_access
checksum: af87ddea7908923426365347b9c87ba7
content_type: application/pdf
creator: ptazenko
date_created: 2024-02-07T14:57:32Z
date_updated: 2024-02-07T14:57:32Z
file_id: '14950'
file_name: Burg_et_al_2023_Image_retrieval_outperforms.pdf
file_size: 27325153
relation: main_file
file_date_updated: 2024-02-07T14:57:32Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openreview.net/forum?id=xflYdGZMpv
month: '12'
oa: 1
oa_version: Published Version
publication: Journal of Machine Learning Research
publication_identifier:
eissn:
- 2835-8856
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
status: public
title: Image retrieval outperforms diffusion models on data augmentation
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14952'
abstract:
- lang: eng
text: "While different neural models often exhibit latent spaces that are alike
when exposed to semantically related data, this intrinsic similarity is not always
immediately discernible. Towards a better understanding of this phenomenon, our
work shows how representations learned from these neural modules can be translated
between different pre-trained networks via simpler transformations than previously
thought. An advantage of this approach is the ability to\r\nestimate these transformations
using standard, well-understood algebraic procedures that have closed-form solutions.
Our method directly estimates a transformation between two given latent spaces,
thereby enabling effective stitching of encoders and decoders without additional
training. We extensively validate the adaptability of this translation procedure
in different\r\nexperimental settings: across various trainings, domains, architectures
(e.g., ResNet, CNN, ViT), and in multiple downstream tasks (classification, reconstruction).
Notably, we show how it is possible to zero-shot stitch text encoders and vision
decoders, or vice-versa, yielding surprisingly good classification performance
in this multimodal setting."
acknowledgement: "This work is supported by the ERC grant no.802554 (SPECGEO), PRIN
2020 project no.2020TA3K9N (LEGO.AI), and PNRR MUR project PE0000013-FAIR. Francesco\r\nLocatello
did not contribute to this work at Amazon."
article_number: '2311.00664'
article_processing_charge: No
arxiv: 1
author:
- first_name: Valentino
full_name: Maiorca, Valentino
last_name: Maiorca
- first_name: Luca
full_name: Moschella, Luca
last_name: Moschella
- first_name: Antonio
full_name: Norelli, Antonio
last_name: Norelli
- first_name: Marco
full_name: Fumero, Marco
last_name: Fumero
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Emanuele
full_name: Rodolà, Emanuele
last_name: Rodolà
citation:
ama: Maiorca V, Moschella L, Norelli A, Fumero M, Locatello F, Rodolà E. Latent
space translation via semantic alignment. arXiv. doi:10.48550/arXiv.2311.00664
apa: Maiorca, V., Moschella, L., Norelli, A., Fumero, M., Locatello, F., & Rodolà,
E. (n.d.). Latent space translation via semantic alignment. arXiv. https://doi.org/10.48550/arXiv.2311.00664
chicago: Maiorca, Valentino, Luca Moschella, Antonio Norelli, Marco Fumero, Francesco
Locatello, and Emanuele Rodolà. “Latent Space Translation via Semantic Alignment.”
ArXiv, n.d. https://doi.org/10.48550/arXiv.2311.00664.
ieee: V. Maiorca, L. Moschella, A. Norelli, M. Fumero, F. Locatello, and E. Rodolà,
“Latent space translation via semantic alignment,” arXiv. .
ista: Maiorca V, Moschella L, Norelli A, Fumero M, Locatello F, Rodolà E. Latent
space translation via semantic alignment. arXiv, 2311.00664.
mla: Maiorca, Valentino, et al. “Latent Space Translation via Semantic Alignment.”
ArXiv, 2311.00664, doi:10.48550/arXiv.2311.00664.
short: V. Maiorca, L. Moschella, A. Norelli, M. Fumero, F. Locatello, E. Rodolà,
ArXiv (n.d.).
date_created: 2024-02-07T15:08:55Z
date_published: 2023-11-01T00:00:00Z
date_updated: 2024-02-12T09:40:23Z
day: '01'
department:
- _id: FrLo
doi: 10.48550/arXiv.2311.00664
external_id:
arxiv:
- '2311.00664'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2311.00664
month: '11'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Latent space translation via semantic alignment
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14953'
abstract:
- lang: eng
text: This paper provides statistical sample complexity bounds for score-matching
and its applications in causal discovery. We demonstrate that accurate estimation
of the score function is achievable by training a standard deep ReLU neural network
using stochastic gradient descent. We establish bounds on the error rate of recovering
causal relationships using the score-matching-based causal discovery method of
Rolland et al. [2022], assuming a sufficiently good estimation of the score function.
Finally, we analyze the upper bound of score-matching estimation within the score-based
generative modeling, which has been applied for causal discovery but is also of
independent interest within the domain of generative models.
acknowledgement: 'We are thankful to the reviewers for providing constructive feedback
and Kun Zhang and Dominik Janzing for helpful discussion on the special case of
deterministic children. This work was supported by Hasler Foundation Program: Hasler
Responsible AI (project number 21043). This work was supported by the Swiss National
Science Foundation (SNSF) under grant number 200021_205011. Francesco Locatello
did not contribute to this work at Amazon. '
article_number: '2310.18123'
article_processing_charge: No
arxiv: 1
author:
- first_name: Zhenyu
full_name: Zhu, Zhenyu
last_name: Zhu
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Volkan
full_name: Cevher, Volkan
last_name: Cevher
citation:
ama: 'Zhu Z, Locatello F, Cevher V. Sample complexity bounds for score-matching:
Causal discovery and generative modeling. arXiv. doi:10.48550/arXiv.2310.18123'
apa: 'Zhu, Z., Locatello, F., & Cevher, V. (n.d.). Sample complexity bounds
for score-matching: Causal discovery and generative modeling. arXiv. https://doi.org/10.48550/arXiv.2310.18123'
chicago: 'Zhu, Zhenyu, Francesco Locatello, and Volkan Cevher. “Sample Complexity
Bounds for Score-Matching: Causal Discovery and Generative Modeling.” ArXiv,
n.d. https://doi.org/10.48550/arXiv.2310.18123.'
ieee: 'Z. Zhu, F. Locatello, and V. Cevher, “Sample complexity bounds for score-matching:
Causal discovery and generative modeling,” arXiv. .'
ista: 'Zhu Z, Locatello F, Cevher V. Sample complexity bounds for score-matching:
Causal discovery and generative modeling. arXiv, 2310.18123.'
mla: 'Zhu, Zhenyu, et al. “Sample Complexity Bounds for Score-Matching: Causal Discovery
and Generative Modeling.” ArXiv, 2310.18123, doi:10.48550/arXiv.2310.18123.'
short: Z. Zhu, F. Locatello, V. Cevher, ArXiv (n.d.).
date_created: 2024-02-07T15:11:11Z
date_published: 2023-10-27T00:00:00Z
date_updated: 2024-02-12T09:45:58Z
day: '27'
department:
- _id: FrLo
doi: 10.48550/arXiv.2310.18123
external_id:
arxiv:
- '2310.18123'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2310.18123
month: '10'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: 'Sample complexity bounds for score-matching: Causal discovery and generative
modeling'
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14954'
abstract:
- lang: eng
text: "When domain knowledge is limited and experimentation is restricted by ethical,
financial, or time constraints, practitioners turn to observational causal discovery
methods to recover the causal structure, exploiting the statistical properties
of their data. Because causal discovery without further assumptions is an ill-posed
problem, each algorithm comes with its own set of\r\nusually untestable assumptions,
some of which are hard to meet in real datasets. Motivated by these considerations,
this paper extensively benchmarks the empirical performance of recent causal discovery
methods on observational i.i.d. data generated under different background conditions,
allowing for violations of the critical assumptions required by each selected
approach. Our experimental findings show that score matching-based methods demonstrate\r\nsurprising
performance in the false positive and false negative rate of the inferred graph
in these challenging scenarios, and we provide theoretical insights into their
performance. This work is also the first effort to benchmark the stability of
causal discovery algorithms with respect to the values of their hyperparameters.
Finally, we hope this paper will set a new standard for the evaluation of causal
discovery methods and can serve as an accessible entry point for practitioners
interested in the field, highlighting the empirical implications of different
algorithm choices."
acknowledgement: "We thank Kun Zhang and Carl-Johann Simon-Gabriel for the insightful
discussions. This work\r\nhas been supported by AFOSR, grant n. FA8655-20-1-7035.
FM is supported by Programma\r\nOperativo Nazionale ricerca e innovazione 2014-2020.
FM partially contributed to this work during an internship at Amazon Web Services
with FL. FL partially contributed while at AWS."
article_number: '2310.13387'
article_processing_charge: No
arxiv: 1
author:
- first_name: Francesco
full_name: Montagna, Francesco
last_name: Montagna
- first_name: Atalanti A.
full_name: Mastakouri, Atalanti A.
last_name: Mastakouri
- first_name: Elias
full_name: Eulig, Elias
last_name: Eulig
- first_name: Nicoletta
full_name: Noceti, Nicoletta
last_name: Noceti
- first_name: Lorenzo
full_name: Rosasco, Lorenzo
last_name: Rosasco
- first_name: Dominik
full_name: Janzing, Dominik
last_name: Janzing
- first_name: Bryon
full_name: Aragam, Bryon
last_name: Aragam
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: Montagna F, Mastakouri AA, Eulig E, et al. Assumption violations in causal
discovery and the robustness of score matching. arXiv. doi:10.48550/arXiv.2310.13387
apa: Montagna, F., Mastakouri, A. A., Eulig, E., Noceti, N., Rosasco, L., Janzing,
D., … Locatello, F. (n.d.). Assumption violations in causal discovery and the
robustness of score matching. arXiv. https://doi.org/10.48550/arXiv.2310.13387
chicago: Montagna, Francesco, Atalanti A. Mastakouri, Elias Eulig, Nicoletta Noceti,
Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, and Francesco Locatello. “Assumption
Violations in Causal Discovery and the Robustness of Score Matching.” ArXiv,
n.d. https://doi.org/10.48550/arXiv.2310.13387.
ieee: F. Montagna et al., “Assumption violations in causal discovery and
the robustness of score matching,” arXiv. .
ista: Montagna F, Mastakouri AA, Eulig E, Noceti N, Rosasco L, Janzing D, Aragam
B, Locatello F. Assumption violations in causal discovery and the robustness of
score matching. arXiv, 2310.13387.
mla: Montagna, Francesco, et al. “Assumption Violations in Causal Discovery and
the Robustness of Score Matching.” ArXiv, 2310.13387, doi:10.48550/arXiv.2310.13387.
short: F. Montagna, A.A. Mastakouri, E. Eulig, N. Noceti, L. Rosasco, D. Janzing,
B. Aragam, F. Locatello, ArXiv (n.d.).
date_created: 2024-02-07T15:11:56Z
date_published: 2023-10-20T00:00:00Z
date_updated: 2024-02-12T09:51:15Z
day: '20'
department:
- _id: FrLo
doi: 10.48550/arXiv.2310.13387
external_id:
arxiv:
- '2310.13387'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2310.13387
month: '10'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Assumption violations in causal discovery and the robustness of score matching
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14958'
abstract:
- lang: eng
text: Causal representation learning (CRL) aims at identifying high-level causal
variables from low-level data, e.g. images. Current methods usually assume that
all causal variables are captured in the high-dimensional observations. In this
work, we focus on learning causal representations from data under partial observability,
i.e., when some of the causal variables are not observed in the measurements,
and the set of masked variables changes across the different samples. We introduce
some initial theoretical results for identifying causal variables under partial
observability by exploiting a sparsity regularizer, focusing in particular on
the linear and piecewise linear mixing function case. We provide a theorem that
allows us to identify the causal variables up to permutation and element-wise
linear transformations in the linear case and a lemma that allows us to identify
causal variables up to linear transformation in the piecewise case. Finally, we
provide a conjecture that would allow us to identify the causal variables up to
permutation and element-wise linear transformations also in the piecewise linear
case. We test the theorem and conjecture on simulated data, showing the effectiveness
of our method.
acknowledgement: "This work was initiated at the Second Bellairs Workshop on Causality
held at the Bellairs Research Institute, January 6–13, 2022; we thank all workshop
participants for providing a stimulating research environment. The research of DX
and SM was supported by the Air Force Office of Scientific Research under award
number FA8655-22-1-7155. Any opinions, findings, and conclusions or recommendations
expressed in this material are those of the author(s) and do not necessarily reflect
the views of the United States Air Force. We also thank SURF for the support in
using the Dutch National Supercomputer Snellius. DY was supported by an Amazon fellowship
and the International Max Planck Research School for Intelligent Systems (IMPRS-IS).
Work done outside of Amazon. SL was supported by an IVADO excellence PhD scholarship
and by Samsung Electronics Co., Ldt. JvK acknowledges support from the German Federal
Ministry of Education and Research (BMBF)\r\nthrough the Tübingen AI Center (FKZ:
01IS18039B).\r\n"
article_number: '54'
article_processing_charge: No
author:
- first_name: Danru
full_name: Xu, Danru
last_name: Xu
- first_name: Dingling
full_name: Yao, Dingling
id: d3e02e50-48a8-11ee-8f62-c108061797fa
last_name: Yao
- first_name: Sebastien
full_name: Lachapelle, Sebastien
last_name: Lachapelle
- first_name: Perouz
full_name: Taslakian, Perouz
last_name: Taslakian
- first_name: Julius
full_name: von Kügelgen, Julius
last_name: von Kügelgen
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Sara
full_name: Magliacane, Sara
last_name: Magliacane
citation:
ama: 'Xu D, Yao D, Lachapelle S, et al. A sparsity principle for partially observable
causal representation learning. In: Causal Representation Learning Workshop
at NeurIPS 2023. OpenReview; 2023.'
apa: 'Xu, D., Yao, D., Lachapelle, S., Taslakian, P., von Kügelgen, J., Locatello,
F., & Magliacane, S. (2023). A sparsity principle for partially observable
causal representation learning. In Causal Representation Learning Workshop
at NeurIPS 2023. New Orleans, LA, United States: OpenReview.'
chicago: Xu, Danru, Dingling Yao, Sebastien Lachapelle, Perouz Taslakian, Julius
von Kügelgen, Francesco Locatello, and Sara Magliacane. “A Sparsity Principle
for Partially Observable Causal Representation Learning.” In Causal Representation
Learning Workshop at NeurIPS 2023. OpenReview, 2023.
ieee: D. Xu et al., “A sparsity principle for partially observable causal
representation learning,” in Causal Representation Learning Workshop at NeurIPS
2023, New Orleans, LA, United States, 2023.
ista: 'Xu D, Yao D, Lachapelle S, Taslakian P, von Kügelgen J, Locatello F, Magliacane
S. 2023. A sparsity principle for partially observable causal representation learning.
Causal Representation Learning Workshop at NeurIPS 2023. CRL: Causal Representation
Learning Workshop at NeurIPS, 54.'
mla: Xu, Danru, et al. “A Sparsity Principle for Partially Observable Causal Representation
Learning.” Causal Representation Learning Workshop at NeurIPS 2023, 54,
OpenReview, 2023.
short: D. Xu, D. Yao, S. Lachapelle, P. Taslakian, J. von Kügelgen, F. Locatello,
S. Magliacane, in:, Causal Representation Learning Workshop at NeurIPS 2023, OpenReview,
2023.
conference:
end_date: 2023-12-15
location: New Orleans, LA, United States
name: 'CRL: Causal Representation Learning Workshop at NeurIPS'
start_date: 2023-12-15
date_created: 2024-02-07T15:17:51Z
date_published: 2023-12-05T00:00:00Z
date_updated: 2024-02-13T08:59:27Z
day: '05'
ddc:
- '000'
department:
- _id: FrLo
file:
- access_level: open_access
checksum: 484efc27bda75ed6666044989695d9b6
content_type: application/pdf
creator: dernst
date_created: 2024-02-13T08:50:53Z
date_updated: 2024-02-13T08:50:53Z
file_id: '14982'
file_name: 2023_CRL_Xu.pdf
file_size: 552357
relation: main_file
success: 1
file_date_updated: 2024-02-13T08:50:53Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openreview.net/forum?id=Whr6uobelR
month: '12'
oa: 1
oa_version: Published Version
publication: Causal Representation Learning Workshop at NeurIPS 2023
publication_status: published
publisher: OpenReview
quality_controlled: '1'
status: public
title: A sparsity principle for partially observable causal representation learning
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14961'
abstract:
- lang: eng
text: "The use of simulated data in the field of causal discovery is ubiquitous
due to the scarcity of annotated real data. Recently, Reisach et al., 2021 highlighted
the emergence of patterns in simulated linear data, which displays increasing
marginal variance in the casual direction. As an ablation in their experiments,
Montagna et al., 2023 found that similar patterns may emerge in\r\nnonlinear models
for the variance of the score vector $\\nabla \\log p_{\\mathbf{X}}$, and introduced
the ScoreSort algorithm. In this work, we formally define and characterize this
score-sortability pattern of nonlinear additive noise models. We find that it
defines a class of identifiable (bivariate) causal models overlapping with nonlinear
additive noise models. We\r\ntheoretically demonstrate the advantages of ScoreSort
in terms of statistical efficiency compared to prior state-of-the-art score matching-based
methods and empirically show the score-sortability of the most common synthetic
benchmarks in the literature. Our findings remark (1) the lack of diversity in
the data as an important limitation in the evaluation of nonlinear causal discovery
approaches, (2) the importance of thoroughly testing different settings within
a problem class, and (3) the importance of analyzing statistical properties in\r\ncausal
discovery, where research is often limited to defining identifiability conditions
of the model. "
article_number: '2310.14246'
article_processing_charge: No
arxiv: 1
author:
- first_name: Francesco
full_name: Montagna, Francesco
last_name: Montagna
- first_name: Nicoletta
full_name: Noceti, Nicoletta
last_name: Noceti
- first_name: Lorenzo
full_name: Rosasco, Lorenzo
last_name: Rosasco
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
citation:
ama: Montagna F, Noceti N, Rosasco L, Locatello F. Shortcuts for causal discovery
of nonlinear models by score matching. arXiv. doi:10.48550/arXiv.2310.14246
apa: Montagna, F., Noceti, N., Rosasco, L., & Locatello, F. (n.d.). Shortcuts
for causal discovery of nonlinear models by score matching. arXiv. https://doi.org/10.48550/arXiv.2310.14246
chicago: Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, and Francesco Locatello.
“Shortcuts for Causal Discovery of Nonlinear Models by Score Matching.” ArXiv,
n.d. https://doi.org/10.48550/arXiv.2310.14246.
ieee: F. Montagna, N. Noceti, L. Rosasco, and F. Locatello, “Shortcuts for causal
discovery of nonlinear models by score matching,” arXiv. .
ista: Montagna F, Noceti N, Rosasco L, Locatello F. Shortcuts for causal discovery
of nonlinear models by score matching. arXiv, 2310.14246.
mla: Montagna, Francesco, et al. “Shortcuts for Causal Discovery of Nonlinear Models
by Score Matching.” ArXiv, 2310.14246, doi:10.48550/arXiv.2310.14246.
short: F. Montagna, N. Noceti, L. Rosasco, F. Locatello, ArXiv (n.d.).
date_created: 2024-02-08T15:31:46Z
date_published: 2023-10-22T00:00:00Z
date_updated: 2024-02-12T10:03:33Z
day: '22'
department:
- _id: FrLo
doi: 10.48550/arXiv.2310.14246
external_id:
arxiv:
- '2310.14246'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2310.14246
month: '10'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Shortcuts for causal discovery of nonlinear models by score matching
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14962'
abstract:
- lang: eng
text: "In this paper, we show that recent advances in video representation learning\r\nand
pre-trained vision-language models allow for substantial improvements in\r\nself-supervised
video object localization. We propose a method that first\r\nlocalizes objects
in videos via a slot attention approach and then assigns text\r\nto the obtained
slots. The latter is achieved by an unsupervised way to read\r\nlocalized semantic
information from the pre-trained CLIP model. The resulting\r\nvideo object localization
is entirely unsupervised apart from the implicit\r\nannotation contained in CLIP,
and it is effectively the first unsupervised\r\napproach that yields good results
on regular video benchmarks."
article_number: '2309.09858'
article_processing_charge: No
arxiv: 1
author:
- first_name: Ke
full_name: Fan, Ke
last_name: Fan
- first_name: Zechen
full_name: Bai, Zechen
last_name: Bai
- first_name: Tianjun
full_name: Xiao, Tianjun
last_name: Xiao
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Zixu
full_name: Zhao, Zixu
last_name: Zhao
- first_name: Carl-Johann Simon-Gabriel
full_name: Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel
last_name: Carl-Johann Simon-Gabriel
- first_name: Mike Zheng
full_name: Shou, Mike Zheng
last_name: Shou
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Bernt
full_name: Schiele, Bernt
last_name: Schiele
- first_name: Thomas
full_name: Brox, Thomas
last_name: Brox
- first_name: Zheng
full_name: Zhang, Zheng
last_name: Zhang
- first_name: Yanwei
full_name: Fu, Yanwei
last_name: Fu
- first_name: Tong
full_name: He, Tong
last_name: He
citation:
ama: Fan K, Bai Z, Xiao T, et al. Unsupervised open-vocabulary object localization
in videos. arXiv. doi:10.48550/arXiv.2309.09858
apa: Fan, K., Bai, Z., Xiao, T., Zietlow, D., Horn, M., Zhao, Z., … He, T. (n.d.).
Unsupervised open-vocabulary object localization in videos. arXiv. https://doi.org/10.48550/arXiv.2309.09858
chicago: Fan, Ke, Zechen Bai, Tianjun Xiao, Dominik Zietlow, Max Horn, Zixu Zhao,
Carl-Johann Simon-Gabriel Carl-Johann Simon-Gabriel, et al. “Unsupervised Open-Vocabulary
Object Localization in Videos.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2309.09858.
ieee: K. Fan et al., “Unsupervised open-vocabulary object localization in
videos,” arXiv. .
ista: Fan K, Bai Z, Xiao T, Zietlow D, Horn M, Zhao Z, Carl-Johann Simon-Gabriel
C-JS-G, Shou MZ, Locatello F, Schiele B, Brox T, Zhang Z, Fu Y, He T. Unsupervised
open-vocabulary object localization in videos. arXiv, 2309.09858.
mla: Fan, Ke, et al. “Unsupervised Open-Vocabulary Object Localization in Videos.”
ArXiv, 2309.09858, doi:10.48550/arXiv.2309.09858.
short: K. Fan, Z. Bai, T. Xiao, D. Zietlow, M. Horn, Z. Zhao, C.-J.S.-G. Carl-Johann
Simon-Gabriel, M.Z. Shou, F. Locatello, B. Schiele, T. Brox, Z. Zhang, Y. Fu,
T. He, ArXiv (n.d.).
date_created: 2024-02-08T15:33:39Z
date_published: 2023-09-18T00:00:00Z
date_updated: 2024-02-12T10:12:22Z
day: '18'
department:
- _id: FrLo
doi: 10.48550/arXiv.2309.09858
extern: '1'
external_id:
arxiv:
- '2309.09858'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2309.09858
month: '09'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Unsupervised open-vocabulary object localization in videos
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14963'
abstract:
- lang: eng
text: "Unsupervised object-centric learning methods allow the partitioning of scenes\r\ninto
entities without additional localization information and are excellent\r\ncandidates
for reducing the annotation burden of multiple-object tracking (MOT)\r\npipelines.
Unfortunately, they lack two key properties: objects are often split\r\ninto parts
and are not consistently tracked over time. In fact,\r\nstate-of-the-art models
achieve pixel-level accuracy and temporal consistency\r\nby relying on supervised
object detection with additional ID labels for the\r\nassociation through time.
This paper proposes a video object-centric model for\r\nMOT. It consists of an
index-merge module that adapts the object-centric slots\r\ninto detection outputs
and an object memory module that builds complete object\r\nprototypes to handle
occlusions. Benefited from object-centric learning, we\r\nonly require sparse
detection labels (0%-6.25%) for object localization and\r\nfeature binding. Relying
on our self-supervised\r\nExpectation-Maximization-inspired loss for object association,
our approach\r\nrequires no ID labels. Our experiments significantly narrow the
gap between the\r\nexisting object-centric model and the fully supervised state-of-the-art
and\r\noutperform several unsupervised trackers."
article_number: '2309.00233'
article_processing_charge: No
arxiv: 1
author:
- first_name: Zixu
full_name: Zhao, Zixu
last_name: Zhao
- first_name: Jiaze
full_name: Wang, Jiaze
last_name: Wang
- first_name: Max
full_name: Horn, Max
last_name: Horn
- first_name: Yizhuo
full_name: Ding, Yizhuo
last_name: Ding
- first_name: Tong
full_name: He, Tong
last_name: He
- first_name: Zechen
full_name: Bai, Zechen
last_name: Bai
- first_name: Dominik
full_name: Zietlow, Dominik
last_name: Zietlow
- first_name: Carl-Johann Simon-Gabriel
full_name: Carl-Johann Simon-Gabriel, Carl-Johann Simon-Gabriel
last_name: Carl-Johann Simon-Gabriel
- first_name: Bing
full_name: Shuai, Bing
last_name: Shuai
- first_name: Zhuowen
full_name: Tu, Zhuowen
last_name: Tu
- first_name: Thomas
full_name: Brox, Thomas
last_name: Brox
- first_name: Bernt
full_name: Schiele, Bernt
last_name: Schiele
- first_name: Yanwei
full_name: Fu, Yanwei
last_name: Fu
- first_name: Francesco
full_name: Locatello, Francesco
id: 26cfd52f-2483-11ee-8040-88983bcc06d4
last_name: Locatello
orcid: 0000-0002-4850-0683
- first_name: Zheng
full_name: Zhang, Zheng
last_name: Zhang
- first_name: Tianjun
full_name: Xiao, Tianjun
last_name: Xiao
citation:
ama: Zhao Z, Wang J, Horn M, et al. Object-centric multiple object tracking. arXiv.
doi:10.48550/arXiv.2309.00233
apa: Zhao, Z., Wang, J., Horn, M., Ding, Y., He, T., Bai, Z., … Xiao, T. (n.d.).
Object-centric multiple object tracking. arXiv. https://doi.org/10.48550/arXiv.2309.00233
chicago: Zhao, Zixu, Jiaze Wang, Max Horn, Yizhuo Ding, Tong He, Zechen Bai, Dominik
Zietlow, et al. “Object-Centric Multiple Object Tracking.” ArXiv, n.d.
https://doi.org/10.48550/arXiv.2309.00233.
ieee: Z. Zhao et al., “Object-centric multiple object tracking,” arXiv.
.
ista: Zhao Z, Wang J, Horn M, Ding Y, He T, Bai Z, Zietlow D, Carl-Johann Simon-Gabriel
C-JS-G, Shuai B, Tu Z, Brox T, Schiele B, Fu Y, Locatello F, Zhang Z, Xiao T.
Object-centric multiple object tracking. arXiv, 2309.00233.
mla: Zhao, Zixu, et al. “Object-Centric Multiple Object Tracking.” ArXiv,
2309.00233, doi:10.48550/arXiv.2309.00233.
short: Z. Zhao, J. Wang, M. Horn, Y. Ding, T. He, Z. Bai, D. Zietlow, C.-J.S.-G.
Carl-Johann Simon-Gabriel, B. Shuai, Z. Tu, T. Brox, B. Schiele, Y. Fu, F. Locatello,
Z. Zhang, T. Xiao, ArXiv (n.d.).
date_created: 2024-02-08T15:34:43Z
date_published: 2023-09-01T00:00:00Z
date_updated: 2024-02-12T10:16:21Z
day: '01'
department:
- _id: FrLo
doi: 10.48550/arXiv.2309.00233
extern: '1'
external_id:
arxiv:
- '2309.00233'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.2309.00233'
month: '09'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Object-centric multiple object tracking
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14985'
abstract:
- lang: eng
text: Lead sulfide (PbS) presents large potential in thermoelectric application
due to its earth-abundant S element. However, its inferior average ZT (ZTave)
value makes PbS less competitive with its analogs PbTe and PbSe. To promote its
thermoelectric performance, this study implements strategies of continuous Se
alloying and Cu interstitial doping to synergistically tune thermal and electrical
transport properties in n-type PbS. First, the lattice parameter of 5.93 Å in
PbS is linearly expanded to 6.03 Å in PbS0.5Se0.5 with increasing Se alloying
content. This expanded lattice in Se-alloyed PbS not only intensifies phonon scattering
but also facilitates the formation of Cu interstitials. Based on the PbS0.6Se0.4
content with the minimal lattice thermal conductivity, Cu interstitials are introduced
to improve the electron density, thus boosting the peak power factor, from 3.88 μW cm−1 K−2
in PbS0.6Se0.4 to 20.58 μW cm−1 K−2 in PbS0.6Se0.4−1%Cu. Meanwhile, the lattice
thermal conductivity in PbS0.6Se0.4−x%Cu (x = 0–2) is further suppressed due to
the strong strain field caused by Cu interstitials. Finally, with the lowered
thermal conductivity and high electrical transport properties, a peak ZT ~1.1
and ZTave ~0.82 can be achieved in PbS0.6Se0.4 − 1%Cu at 300–773K, which outperforms
previously reported n-type PbS.
acknowledgement: 'The authors would like to acknowledge the strong supportof microstructure
observation from Center for HighPressure Science and Technology Advanced Research(HPSTAR).
We acknowledge the financial support fromthe National Natural Science Foundation of China:52172236,
the Fundamental Research Funds for theCentral Universities: xtr042021007, Top Young
TalentsProgramme of Xi''an Jiaotong University and NationalScience Fund for Distinguished
Young Scholars: 51925101.'
article_processing_charge: Yes
article_type: original
author:
- first_name: Zhengtao
full_name: Liu, Zhengtao
last_name: Liu
- first_name: Tao
full_name: Hong, Tao
last_name: Hong
- first_name: Liqing
full_name: Xu, Liqing
last_name: Xu
- first_name: Sining
full_name: Wang, Sining
last_name: Wang
- first_name: Xiang
full_name: Gao, Xiang
last_name: Gao
- first_name: Cheng
full_name: Chang, Cheng
id: 9E331C2E-9F27-11E9-AE48-5033E6697425
last_name: Chang
orcid: 0000-0002-9515-4277
- first_name: Xiangdong
full_name: Ding, Xiangdong
last_name: Ding
- first_name: Yu
full_name: Xiao, Yu
last_name: Xiao
- first_name: Li‐Dong
full_name: Zhao, Li‐Dong
last_name: Zhao
citation:
ama: Liu Z, Hong T, Xu L, et al. Lattice expansion enables interstitial doping to
achieve a high average ZT in n‐type PbS. Interdisciplinary Materials. 2023;2(1):161-170.
doi:10.1002/idm2.12056
apa: Liu, Z., Hong, T., Xu, L., Wang, S., Gao, X., Chang, C., … Zhao, L. (2023).
Lattice expansion enables interstitial doping to achieve a high average ZT in
n‐type PbS. Interdisciplinary Materials. Wiley. https://doi.org/10.1002/idm2.12056
chicago: Liu, Zhengtao, Tao Hong, Liqing Xu, Sining Wang, Xiang Gao, Cheng Chang,
Xiangdong Ding, Yu Xiao, and Li‐Dong Zhao. “Lattice Expansion Enables Interstitial
Doping to Achieve a High Average ZT in N‐type PbS.” Interdisciplinary Materials.
Wiley, 2023. https://doi.org/10.1002/idm2.12056.
ieee: Z. Liu et al., “Lattice expansion enables interstitial doping to achieve
a high average ZT in n‐type PbS,” Interdisciplinary Materials, vol. 2,
no. 1. Wiley, pp. 161–170, 2023.
ista: Liu Z, Hong T, Xu L, Wang S, Gao X, Chang C, Ding X, Xiao Y, Zhao L. 2023.
Lattice expansion enables interstitial doping to achieve a high average ZT in
n‐type PbS. Interdisciplinary Materials. 2(1), 161–170.
mla: Liu, Zhengtao, et al. “Lattice Expansion Enables Interstitial Doping to Achieve
a High Average ZT in N‐type PbS.” Interdisciplinary Materials, vol. 2,
no. 1, Wiley, 2023, pp. 161–70, doi:10.1002/idm2.12056.
short: Z. Liu, T. Hong, L. Xu, S. Wang, X. Gao, C. Chang, X. Ding, Y. Xiao, L. Zhao,
Interdisciplinary Materials 2 (2023) 161–170.
date_created: 2024-02-14T12:12:17Z
date_published: 2023-01-01T00:00:00Z
date_updated: 2024-02-19T10:01:26Z
day: '01'
ddc:
- '540'
department:
- _id: MaIb
doi: 10.1002/idm2.12056
file:
- access_level: open_access
checksum: 7b5e8210ef1434feb173022c6dbbee0c
content_type: application/pdf
creator: dernst
date_created: 2024-02-19T09:58:32Z
date_updated: 2024-02-19T09:58:32Z
file_id: '15015'
file_name: 2023_InterdiscMaterials_Liu.pdf
file_size: 4675941
relation: main_file
success: 1
file_date_updated: 2024-02-19T09:58:32Z
has_accepted_license: '1'
intvolume: ' 2'
issue: '1'
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
page: 161-170
publication: Interdisciplinary Materials
publication_identifier:
eissn:
- 2767-441X
publication_status: published
publisher: Wiley
quality_controlled: '1'
status: public
title: Lattice expansion enables interstitial doping to achieve a high average ZT
in n‐type PbS
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2
year: '2023'
...
---
_id: '14989'
abstract:
- lang: eng
text: "Encryption alone is not enough for secure end-to end encrypted messaging:
a server must also honestly serve public keys to users. Key transparency has been
presented as an efficient\r\nsolution for detecting (and hence deterring) a server
that attempts to dishonestly serve keys. Key transparency involves two major components:
(1) a username to public key mapping, stored and cryptographically committed to
by the server, and, (2) an outof-band consistency protocol for serving short commitments
to users. In the setting of real-world deployments and supporting production scale,
new challenges must be considered for both of these components. We enumerate these
challenges and provide solutions to address them. In particular, we design and
implement a memory-optimized and privacy-preserving verifiable data structure
for committing to the username to public key store.\r\nTo make this implementation
viable for production, we also integrate support for persistent and distributed
storage. We also propose a future-facing solution, termed “compaction”, as\r\na
mechanism for mitigating practical issues that arise from dealing with infinitely
growing server data structures. Finally, we implement a consensusless solution
that achieves the minimum requirements for a service that consistently distributes
commitments for a transparency application, providing a much more efficient protocol
for distributing small and consistent\r\ncommitments to users. This culminates
in our production-grade implementation of a key transparency system (Parakeet)
which we have open-sourced, along with a demonstration of feasibility through
our benchmarks."
acknowledgement: This work is supported by the Novi team at Meta and funded in part
by IC3 industry partners and NSF grant 1943499.
article_processing_charge: No
author:
- first_name: Harjasleen
full_name: Malvai, Harjasleen
last_name: Malvai
- first_name: Eleftherios
full_name: Kokoris Kogias, Eleftherios
id: f5983044-d7ef-11ea-ac6d-fd1430a26d30
last_name: Kokoris Kogias
- first_name: Alberto
full_name: Sonnino, Alberto
last_name: Sonnino
- first_name: Esha
full_name: Ghosh, Esha
last_name: Ghosh
- first_name: Ercan
full_name: Oztürk, Ercan
last_name: Oztürk
- first_name: Kevin
full_name: Lewi, Kevin
last_name: Lewi
- first_name: Sean
full_name: Lawlor, Sean
last_name: Lawlor
citation:
ama: 'Malvai H, Kokoris Kogias E, Sonnino A, et al. Parakeet: Practical key transparency
for end-to-end eEncrypted messaging. In: Proceedings of the 2023 Network and
Distributed System Security Symposium. Internet Society; 2023. doi:10.14722/ndss.2023.24545'
apa: 'Malvai, H., Kokoris Kogias, E., Sonnino, A., Ghosh, E., Oztürk, E., Lewi,
K., & Lawlor, S. (2023). Parakeet: Practical key transparency for end-to-end
eEncrypted messaging. In Proceedings of the 2023 Network and Distributed System
Security Symposium. San Diego, CA, United States: Internet Society. https://doi.org/10.14722/ndss.2023.24545'
chicago: 'Malvai, Harjasleen, Eleftherios Kokoris Kogias, Alberto Sonnino, Esha
Ghosh, Ercan Oztürk, Kevin Lewi, and Sean Lawlor. “Parakeet: Practical Key Transparency
for End-to-End EEncrypted Messaging.” In Proceedings of the 2023 Network and
Distributed System Security Symposium. Internet Society, 2023. https://doi.org/10.14722/ndss.2023.24545.'
ieee: 'H. Malvai et al., “Parakeet: Practical key transparency for end-to-end
eEncrypted messaging,” in Proceedings of the 2023 Network and Distributed System
Security Symposium, San Diego, CA, United States, 2023.'
ista: 'Malvai H, Kokoris Kogias E, Sonnino A, Ghosh E, Oztürk E, Lewi K, Lawlor
S. 2023. Parakeet: Practical key transparency for end-to-end eEncrypted messaging.
Proceedings of the 2023 Network and Distributed System Security Symposium. NDSS:
Network and Distributed Systems Security.'
mla: 'Malvai, Harjasleen, et al. “Parakeet: Practical Key Transparency for End-to-End
EEncrypted Messaging.” Proceedings of the 2023 Network and Distributed System
Security Symposium, Internet Society, 2023, doi:10.14722/ndss.2023.24545.'
short: H. Malvai, E. Kokoris Kogias, A. Sonnino, E. Ghosh, E. Oztürk, K. Lewi, S.
Lawlor, in:, Proceedings of the 2023 Network and Distributed System Security Symposium,
Internet Society, 2023.
conference:
end_date: 2023-03-03
location: San Diego, CA, United States
name: 'NDSS: Network and Distributed Systems Security'
start_date: 2023-02-27
date_created: 2024-02-14T14:20:40Z
date_published: 2023-03-01T00:00:00Z
date_updated: 2024-02-19T12:11:15Z
day: '01'
department:
- _id: ElKo
doi: 10.14722/ndss.2023.24545
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://eprint.iacr.org/2023/081
month: '03'
oa: 1
oa_version: Published Version
publication: Proceedings of the 2023 Network and Distributed System Security Symposium
publication_identifier:
isbn:
- '1891562835'
publication_status: published
publisher: Internet Society
quality_controlled: '1'
status: public
title: 'Parakeet: Practical key transparency for end-to-end eEncrypted messaging'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14990'
abstract:
- lang: eng
text: The software artefact to evaluate the approximation of stationary distributions
implementation.
article_processing_charge: No
author:
- first_name: Tobias
full_name: Meggendorfer, Tobias
id: b21b0c15-30a2-11eb-80dc-f13ca25802e1
last_name: Meggendorfer
orcid: 0000-0002-1712-2165
citation:
ama: 'Meggendorfer T. Artefact for: Correct Approximation of Stationary Distributions.
2023. doi:10.5281/ZENODO.7548214'
apa: 'Meggendorfer, T. (2023). Artefact for: Correct Approximation of Stationary
Distributions. Zenodo. https://doi.org/10.5281/ZENODO.7548214'
chicago: 'Meggendorfer, Tobias. “Artefact for: Correct Approximation of Stationary
Distributions.” Zenodo, 2023. https://doi.org/10.5281/ZENODO.7548214.'
ieee: 'T. Meggendorfer, “Artefact for: Correct Approximation of Stationary Distributions.”
Zenodo, 2023.'
ista: 'Meggendorfer T. 2023. Artefact for: Correct Approximation of Stationary Distributions,
Zenodo, 10.5281/ZENODO.7548214.'
mla: 'Meggendorfer, Tobias. Artefact for: Correct Approximation of Stationary
Distributions. Zenodo, 2023, doi:10.5281/ZENODO.7548214.'
short: T. Meggendorfer, (2023).
date_created: 2024-02-14T14:27:06Z
date_published: 2023-01-18T00:00:00Z
date_updated: 2024-02-27T07:19:32Z
day: '18'
ddc:
- '000'
department:
- _id: KrCh
doi: 10.5281/ZENODO.7548214
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.7548214
month: '01'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '13139'
relation: used_in_publication
status: public
status: public
title: 'Artefact for: Correct Approximation of Stationary Distributions'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14991'
abstract:
- lang: eng
text: This repository contains the data, scripts, WRF codes and files required to
reproduce the results of the manuscript "Assessing Memory in Convection Schemes
Using Idealized Tests" submitted to the Journal of Advances in Modeling Earth
Systems (JAMES).
article_processing_charge: No
author:
- first_name: Yi-Ling
full_name: Hwong, Yi-Ling
id: 1217aa61-4dd1-11ec-9ac3-f2ba3f17ee22
last_name: Hwong
orcid: 0000-0001-9281-3479
- first_name: Maxime
full_name: Colin, Maxime
last_name: Colin
- first_name: Philipp
full_name: Aglas, Philipp
id: 02eace56-97fc-11ee-b81a-f0939ca85a77
last_name: Aglas
- first_name: Caroline J
full_name: Muller, Caroline J
id: f978ccb0-3f7f-11eb-b193-b0e2bd13182b
last_name: Muller
orcid: 0000-0001-5836-5350
- first_name: Steven C.
full_name: Sherwood, Steven C.
last_name: Sherwood
citation:
ama: Hwong Y-L, Colin M, Aglas P, Muller CJ, Sherwood SC. Data-assessing memory
in convection schemes using idealized tests. 2023. doi:10.5281/ZENODO.7757041
apa: Hwong, Y.-L., Colin, M., Aglas, P., Muller, C. J., & Sherwood, S. C. (2023).
Data-assessing memory in convection schemes using idealized tests. Zenodo. https://doi.org/10.5281/ZENODO.7757041
chicago: Hwong, Yi-Ling, Maxime Colin, Philipp Aglas, Caroline J Muller, and Steven
C. Sherwood. “Data-Assessing Memory in Convection Schemes Using Idealized Tests.”
Zenodo, 2023. https://doi.org/10.5281/ZENODO.7757041.
ieee: Y.-L. Hwong, M. Colin, P. Aglas, C. J. Muller, and S. C. Sherwood, “Data-assessing
memory in convection schemes using idealized tests.” Zenodo, 2023.
ista: Hwong Y-L, Colin M, Aglas P, Muller CJ, Sherwood SC. 2023. Data-assessing
memory in convection schemes using idealized tests, Zenodo, 10.5281/ZENODO.7757041.
mla: Hwong, Yi-Ling, et al. Data-Assessing Memory in Convection Schemes Using
Idealized Tests. Zenodo, 2023, doi:10.5281/ZENODO.7757041.
short: Y.-L. Hwong, M. Colin, P. Aglas, C.J. Muller, S.C. Sherwood, (2023).
date_created: 2024-02-14T14:37:57Z
date_published: 2023-06-23T00:00:00Z
date_updated: 2024-02-27T07:26:31Z
day: '23'
ddc:
- '550'
department:
- _id: CaMu
doi: 10.5281/ZENODO.7757041
ec_funded: 1
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.7757041
month: '06'
oa: 1
oa_version: Published Version
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publisher: Zenodo
related_material:
record:
- id: '14654'
relation: used_in_publication
status: public
status: public
title: Data-assessing memory in convection schemes using idealized tests
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14992'
abstract:
- lang: eng
text: In this chapter we first review the Levy–Lieb functional, which gives the
lowest kinetic and interaction energy that can be reached with all possible quantum
states having a given density. We discuss two possible convex generalizations
of this functional, corresponding to using mixed canonical and grand-canonical
states, respectively. We present some recent works about the local density approximation,
in which the functionals get replaced by purely local functionals constructed
using the uniform electron gas energy per unit volume. We then review the known
upper and lower bounds on the Levy–Lieb functionals. We start with the kinetic
energy alone, then turn to the classical interaction alone, before we are able
to put everything together. A later section is devoted to the Hohenberg–Kohn theorem
and the role of many-body unique continuation in its proof.
alternative_title:
- Mathematics and Molecular Modeling
article_processing_charge: No
arxiv: 1
author:
- first_name: Mathieu
full_name: Lewin, Mathieu
last_name: Lewin
- first_name: Elliott H.
full_name: Lieb, Elliott H.
last_name: Lieb
- first_name: Robert
full_name: Seiringer, Robert
id: 4AFD0470-F248-11E8-B48F-1D18A9856A87
last_name: Seiringer
orcid: 0000-0002-6781-0521
citation:
ama: 'Lewin M, Lieb EH, Seiringer R. Universal Functionals in Density Functional
Theory. In: Cances E, Friesecke G, eds. Density Functional Theory. 1st
ed. MAMOMO. Springer; 2023:115-182. doi:10.1007/978-3-031-22340-2_3'
apa: Lewin, M., Lieb, E. H., & Seiringer, R. (2023). Universal Functionals in
Density Functional Theory. In E. Cances & G. Friesecke (Eds.), Density
Functional Theory (1st ed., pp. 115–182). Springer. https://doi.org/10.1007/978-3-031-22340-2_3
chicago: Lewin, Mathieu, Elliott H. Lieb, and Robert Seiringer. “Universal Functionals
in Density Functional Theory.” In Density Functional Theory, edited by
Eric Cances and Gero Friesecke, 1st ed., 115–82. MAMOMO. Springer, 2023. https://doi.org/10.1007/978-3-031-22340-2_3.
ieee: M. Lewin, E. H. Lieb, and R. Seiringer, “Universal Functionals in Density
Functional Theory,” in Density Functional Theory, 1st ed., E. Cances and
G. Friesecke, Eds. Springer, 2023, pp. 115–182.
ista: 'Lewin M, Lieb EH, Seiringer R. 2023.Universal Functionals in Density Functional
Theory. In: Density Functional Theory. Mathematics and Molecular Modeling, , 115–182.'
mla: Lewin, Mathieu, et al. “Universal Functionals in Density Functional Theory.”
Density Functional Theory, edited by Eric Cances and Gero Friesecke, 1st
ed., Springer, 2023, pp. 115–82, doi:10.1007/978-3-031-22340-2_3.
short: M. Lewin, E.H. Lieb, R. Seiringer, in:, E. Cances, G. Friesecke (Eds.), Density
Functional Theory, 1st ed., Springer, 2023, pp. 115–182.
date_created: 2024-02-14T14:44:33Z
date_published: 2023-07-19T00:00:00Z
date_updated: 2024-02-20T08:33:06Z
day: '19'
department:
- _id: RoSe
doi: 10.1007/978-3-031-22340-2_3
edition: '1'
editor:
- first_name: Eric
full_name: Cances, Eric
last_name: Cances
- first_name: Gero
full_name: Friesecke, Gero
last_name: Friesecke
external_id:
arxiv:
- '1912.10424'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.1912.10424
month: '07'
oa: 1
oa_version: Preprint
page: 115-182
publication: Density Functional Theory
publication_identifier:
eisbn:
- '9783031223402'
isbn:
- '9783031223396'
issn:
- 3005-0286
publication_status: published
publisher: Springer
quality_controlled: '1'
series_title: MAMOMO
status: public
title: Universal Functionals in Density Functional Theory
type: book_chapter
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14993'
abstract:
- lang: eng
text: "Traditional top-down approaches for global health have historically failed
to achieve social progress (Hoffman et al., 2015; Hoffman & Røttingen, 2015).
Recently, however, a more holistic, multi-level approach termed One Health (OH)
(Osterhaus et al., 2020) is being adopted. Several sets of challenges have been
identified for the implementation of OH (dos S. Ribeiro et al., 2019), including
policy and funding, education and training, and multi-actor, multi-domain, and
multi-level collaborations. These exist despite the increasing accessibility to\r\nknowledge
and digital collaborative research tools through the internet. To address some
of these challenges, we propose a general framework for grassroots community-based
means of participatory research. Additionally, we present a specific roadmap to
create a Machine Learning for Global Health community in Africa. The proposed
framework aims to enable any small group of individuals with scarce resources
to build and sustain an online community within approximately two years. We provide
a discussion on the potential impact of the proposed framework for global health
research collaborations."
acknowledgement: "Houcemeddine Turki’s contributions to this final output have been
funded through the Adapting\r\nWikidata to support clinical practice using Data
Science, Semantic Web and Machine Learning\r\nproject, which is part of the Wikimedia
Research Fund maintained by the Wikimedia Foundation in San Francisco, California,
United States of America."
article_processing_charge: No
author:
- first_name: Christopher
full_name: Currin, Christopher
id: e8321fc5-3091-11eb-8a53-83f309a11ac9
last_name: Currin
orcid: 0000-0002-4809-5059
- first_name: Mercy Nyamewaa
full_name: Asiedu , Mercy Nyamewaa
last_name: 'Asiedu '
- first_name: Chris
full_name: Fourie, Chris
last_name: Fourie
- first_name: Benjamin
full_name: Rosman, Benjamin
last_name: Rosman
- first_name: Houcemeddine
full_name: Turki, Houcemeddine
last_name: Turki
- first_name: Atnafu
full_name: Lambebo Tonja, Atnafu
last_name: Lambebo Tonja
- first_name: Jade
full_name: Abbott, Jade
last_name: Abbott
- first_name: Marvellous
full_name: Ajala, Marvellous
last_name: Ajala
- first_name: Sadiq Adewale
full_name: Adedayo, Sadiq Adewale
last_name: Adedayo
- first_name: Chris Chinenye
full_name: Emezue, Chris Chinenye
last_name: Emezue
- first_name: Daphne
full_name: Machangara, Daphne
last_name: Machangara
citation:
ama: 'Currin C, Asiedu MN, Fourie C, et al. A framework for grassroots research
collaboration in machine learning and global health. In: 1st Workshop on Machine
Learning & Global Health. OpenReview; 2023.'
apa: 'Currin, C., Asiedu , M. N., Fourie, C., Rosman, B., Turki, H., Lambebo Tonja,
A., … Machangara, D. (2023). A framework for grassroots research collaboration
in machine learning and global health. In 1st Workshop on Machine Learning
& Global Health. Kigali, Rwanda: OpenReview.'
chicago: Currin, Christopher, Mercy Nyamewaa Asiedu , Chris Fourie, Benjamin Rosman,
Houcemeddine Turki, Atnafu Lambebo Tonja, Jade Abbott, et al. “A Framework for
Grassroots Research Collaboration in Machine Learning and Global Health.” In 1st
Workshop on Machine Learning & Global Health. OpenReview, 2023.
ieee: C. Currin et al., “A framework for grassroots research collaboration
in machine learning and global health,” in 1st Workshop on Machine Learning
& Global Health, Kigali, Rwanda, 2023.
ista: 'Currin C, Asiedu MN, Fourie C, Rosman B, Turki H, Lambebo Tonja A, Abbott
J, Ajala M, Adedayo SA, Emezue CC, Machangara D. 2023. A framework for grassroots
research collaboration in machine learning and global health. 1st Workshop on
Machine Learning & Global Health. ICLR: International Conference on Learning
Representations.'
mla: Currin, Christopher, et al. “A Framework for Grassroots Research Collaboration
in Machine Learning and Global Health.” 1st Workshop on Machine Learning &
Global Health, OpenReview, 2023.
short: C. Currin, M.N. Asiedu , C. Fourie, B. Rosman, H. Turki, A. Lambebo Tonja,
J. Abbott, M. Ajala, S.A. Adedayo, C.C. Emezue, D. Machangara, in:, 1st Workshop
on Machine Learning & Global Health, OpenReview, 2023.
conference:
end_date: 2023-05-05
location: Kigali, Rwanda
name: 'ICLR: International Conference on Learning Representations'
start_date: 2023-05-05
date_created: 2024-02-14T15:11:48Z
date_published: 2023-03-02T00:00:00Z
date_updated: 2024-02-28T12:12:00Z
day: '02'
department:
- _id: TiVo
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://openreview.net/forum?id=jHY_G91R880
month: '03'
oa: 1
oa_version: Published Version
publication: 1st Workshop on Machine Learning & Global Health
publication_status: published
publisher: OpenReview
quality_controlled: '1'
status: public
title: A framework for grassroots research collaboration in machine learning and global
health
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14994'
abstract:
- lang: eng
text: This resource contains the artifacts for reproducing the experimental results
presented in the paper titled "A Flexible Toolchain for Symbolic Rabin Games under
Fair and Stochastic Uncertainties" that has been submitted in CAV 2023.
article_processing_charge: No
author:
- first_name: Rupak
full_name: Majumdar, Rupak
last_name: Majumdar
- first_name: Kaushik
full_name: Mallik, Kaushik
id: 0834ff3c-6d72-11ec-94e0-b5b0a4fb8598
last_name: Mallik
orcid: 0000-0001-9864-7475
- first_name: Mateusz
full_name: Rychlicki, Mateusz
last_name: Rychlicki
- first_name: Anne-Kathrin
full_name: Schmuck, Anne-Kathrin
last_name: Schmuck
- first_name: Sadegh
full_name: Soudjani, Sadegh
last_name: Soudjani
citation:
ama: Majumdar R, Mallik K, Rychlicki M, Schmuck A-K, Soudjani S. A flexible toolchain
for symbolic rabin games under fair and stochastic uncertainties. 2023. doi:10.5281/ZENODO.7877790
apa: Majumdar, R., Mallik, K., Rychlicki, M., Schmuck, A.-K., & Soudjani, S.
(2023). A flexible toolchain for symbolic rabin games under fair and stochastic
uncertainties. Zenodo. https://doi.org/10.5281/ZENODO.7877790
chicago: Majumdar, Rupak, Kaushik Mallik, Mateusz Rychlicki, Anne-Kathrin Schmuck,
and Sadegh Soudjani. “A Flexible Toolchain for Symbolic Rabin Games under Fair
and Stochastic Uncertainties.” Zenodo, 2023. https://doi.org/10.5281/ZENODO.7877790.
ieee: R. Majumdar, K. Mallik, M. Rychlicki, A.-K. Schmuck, and S. Soudjani, “A flexible
toolchain for symbolic rabin games under fair and stochastic uncertainties.” Zenodo,
2023.
ista: Majumdar R, Mallik K, Rychlicki M, Schmuck A-K, Soudjani S. 2023. A flexible
toolchain for symbolic rabin games under fair and stochastic uncertainties, Zenodo,
10.5281/ZENODO.7877790.
mla: Majumdar, Rupak, et al. A Flexible Toolchain for Symbolic Rabin Games under
Fair and Stochastic Uncertainties. Zenodo, 2023, doi:10.5281/ZENODO.7877790.
short: R. Majumdar, K. Mallik, M. Rychlicki, A.-K. Schmuck, S. Soudjani, (2023).
date_created: 2024-02-14T15:13:00Z
date_published: 2023-04-28T00:00:00Z
date_updated: 2024-02-27T07:39:51Z
day: '28'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.5281/ZENODO.7877790
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.7877790
month: '04'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '14758'
relation: used_in_publication
status: public
status: public
title: A flexible toolchain for symbolic rabin games under fair and stochastic uncertainties
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '14995'
abstract:
- lang: eng
text: "Lincheck is a new practical and user-friendly framework for testing concurrent
data structures on the Java Virtual Machine (JVM). It provides a simple and declarative
way to write concurrent tests. Instead of describing how to perform the test,
users specify what to test by declaring all the operations to examine; the framework
automatically handles the rest. As a result, tests written with Lincheck are concise
and easy to understand. \r\nThe artifact presents a collection of Lincheck tests
that discover new bugs in popular libraries and implementations from the concurrency
literature -- they are listed in Table 1, Section 3. To evaluate the performance
of Lincheck analysis, the collection of tests also includes those which check
correct data structures and, thus, always succeed. Similarly to Table 2, Section
3, the experiments demonstrate the reasonable time to perform a test. Finally,
Lincheck provides user-friendly output with an easy-to-follow trace to reproduce
a detected error, significantly simplifying further investigation."
article_processing_charge: No
author:
- first_name: Nikita
full_name: Koval, Nikita
id: 2F4DB10C-F248-11E8-B48F-1D18A9856A87
last_name: Koval
- first_name: Alexander
full_name: Fedorov, Alexander
id: 2e711909-896a-11ed-bdf8-eb0f5a2984c6
last_name: Fedorov
- first_name: Maria
full_name: Sokolova, Maria
last_name: Sokolova
- first_name: Dmitry
full_name: Tsitelov, Dmitry
last_name: Tsitelov
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
citation:
ama: 'Koval N, Fedorov A, Sokolova M, Tsitelov D, Alistarh D-A. Lincheck: A practical
framework for testing concurrent data structures on JVM. 2023. doi:10.5281/ZENODO.7877757'
apa: 'Koval, N., Fedorov, A., Sokolova, M., Tsitelov, D., & Alistarh, D.-A.
(2023). Lincheck: A practical framework for testing concurrent data structures
on JVM. Zenodo. https://doi.org/10.5281/ZENODO.7877757'
chicago: 'Koval, Nikita, Alexander Fedorov, Maria Sokolova, Dmitry Tsitelov, and
Dan-Adrian Alistarh. “Lincheck: A Practical Framework for Testing Concurrent Data
Structures on JVM.” Zenodo, 2023. https://doi.org/10.5281/ZENODO.7877757.'
ieee: 'N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, and D.-A. Alistarh, “Lincheck:
A practical framework for testing concurrent data structures on JVM.” Zenodo,
2023.'
ista: 'Koval N, Fedorov A, Sokolova M, Tsitelov D, Alistarh D-A. 2023. Lincheck:
A practical framework for testing concurrent data structures on JVM, Zenodo, 10.5281/ZENODO.7877757.'
mla: 'Koval, Nikita, et al. Lincheck: A Practical Framework for Testing Concurrent
Data Structures on JVM. Zenodo, 2023, doi:10.5281/ZENODO.7877757.'
short: N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, D.-A. Alistarh, (2023).
date_created: 2024-02-14T15:14:13Z
date_published: 2023-04-28T00:00:00Z
date_updated: 2024-02-27T07:46:52Z
day: '28'
ddc:
- '000'
department:
- _id: DaAl
doi: 10.5281/ZENODO.7877757
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.7877757
month: '04'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '14260'
relation: used_in_publication
status: public
status: public
title: 'Lincheck: A practical framework for testing concurrent data structures on
JVM'
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '15023'
abstract:
- lang: eng
text: Reinforcement learning has shown promising results in learning neural network
policies for complicated control tasks. However, the lack of formal guarantees
about the behavior of such policies remains an impediment to their deployment.
We propose a novel method for learning a composition of neural network policies
in stochastic environments, along with a formal certificate which guarantees that
a specification over the policy's behavior is satisfied with the desired probability.
Unlike prior work on verifiable RL, our approach leverages the compositional nature
of logical specifications provided in SpectRL, to learn over graphs of probabilistic
reach-avoid specifications. The formal guarantees are provided by learning neural
network policies together with reach-avoid supermartingales (RASM) for the graph’s
sub-tasks and then composing them into a global policy. We also derive a tighter
lower bound compared to previous work on the probability of reach-avoidance implied
by a RASM, which is required to find a compositional policy with an acceptable
probabilistic threshold for complex tasks with multiple edge policies. We implement
a prototype of our approach and evaluate it on a Stochastic Nine Rooms environment.
acknowledgement: "This work was supported in part by the ERC-2020-AdG 101020093 (VAMOS)
and the ERC-2020-\r\nCoG 863818 (FoRM-SMArt)."
article_processing_charge: No
arxiv: 1
author:
- first_name: Dorde
full_name: Zikelic, Dorde
id: 294AA7A6-F248-11E8-B48F-1D18A9856A87
last_name: Zikelic
orcid: 0000-0002-4681-1699
- first_name: Mathias
full_name: Lechner, Mathias
id: 3DC22916-F248-11E8-B48F-1D18A9856A87
last_name: Lechner
- first_name: Abhinav
full_name: Verma, Abhinav
id: a235593c-d7fa-11eb-a0c5-b22ca3c66ee6
last_name: Verma
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000-0002-2985-7724
citation:
ama: 'Zikelic D, Lechner M, Verma A, Chatterjee K, Henzinger TA. Compositional policy
learning in stochastic control systems with formal guarantees. In: 37th Conference
on Neural Information Processing Systems. ; 2023.'
apa: Zikelic, D., Lechner, M., Verma, A., Chatterjee, K., & Henzinger, T. A.
(2023). Compositional policy learning in stochastic control systems with formal
guarantees. In 37th Conference on Neural Information Processing Systems.
New Orleans, LO, United States.
chicago: Zikelic, Dorde, Mathias Lechner, Abhinav Verma, Krishnendu Chatterjee,
and Thomas A Henzinger. “Compositional Policy Learning in Stochastic Control Systems
with Formal Guarantees.” In 37th Conference on Neural Information Processing
Systems, 2023.
ieee: D. Zikelic, M. Lechner, A. Verma, K. Chatterjee, and T. A. Henzinger, “Compositional
policy learning in stochastic control systems with formal guarantees,” in 37th
Conference on Neural Information Processing Systems, New Orleans, LO, United
States, 2023.
ista: 'Zikelic D, Lechner M, Verma A, Chatterjee K, Henzinger TA. 2023. Compositional
policy learning in stochastic control systems with formal guarantees. 37th Conference
on Neural Information Processing Systems. NeurIPS: Neural Information Processing
Systems.'
mla: Zikelic, Dorde, et al. “Compositional Policy Learning in Stochastic Control
Systems with Formal Guarantees.” 37th Conference on Neural Information Processing
Systems, 2023.
short: D. Zikelic, M. Lechner, A. Verma, K. Chatterjee, T.A. Henzinger, in:, 37th
Conference on Neural Information Processing Systems, 2023.
conference:
end_date: 2023-12-16
location: New Orleans, LO, United States
name: 'NeurIPS: Neural Information Processing Systems'
start_date: 2023-12-10
date_created: 2024-02-25T09:23:24Z
date_published: 2023-12-15T00:00:00Z
date_updated: 2025-07-14T09:10:04Z
day: '15'
department:
- _id: ToHe
- _id: KrCh
ec_funded: 1
external_id:
arxiv:
- '2312.01456'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2312.01456
month: '12'
oa: 1
oa_version: Preprint
project:
- _id: 0599E47C-7A3F-11EA-A408-12923DDC885E
call_identifier: H2020
grant_number: '863818'
name: 'Formal Methods for Stochastic Models: Algorithms and Applications'
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: 37th Conference on Neural Information Processing Systems
publication_status: epub_ahead
quality_controlled: '1'
status: public
title: Compositional policy learning in stochastic control systems with formal guarantees
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...