@inproceedings{160, abstract = {We present layered concurrent programs, a compact and expressive notation for specifying refinement proofs of concurrent programs. A layered concurrent program specifies a sequence of connected concurrent programs, from most concrete to most abstract, such that common parts of different programs are written exactly once. These programs are expressed in the ordinary syntax of imperative concurrent programs using gated atomic actions, sequencing, choice, and (recursive) procedure calls. Each concurrent program is automatically extracted from the layered program. We reduce refinement to the safety of a sequence of concurrent checker programs, one each to justify the connection between every two consecutive concurrent programs. These checker programs are also automatically extracted from the layered program. Layered concurrent programs have been implemented in the CIVL verifier which has been successfully used for the verification of several complex concurrent programs.}, author = {Kragl, Bernhard and Qadeer, Shaz}, location = {Oxford, UK}, pages = {79 -- 102}, publisher = {Springer}, title = {{Layered Concurrent Programs}}, doi = {10.1007/978-3-319-96145-3_5}, volume = {10981}, year = {2018}, } @article{161, abstract = {Which properties of metabolic networks can be derived solely from stoichiometry? Predictive results have been obtained by flux balance analysis (FBA), by postulating that cells set metabolic fluxes to maximize growth rate. Here we consider a generalization of FBA to single-cell level using maximum entropy modeling, which we extend and test experimentally. Specifically, we define for Escherichia coli metabolism a flux distribution that yields the experimental growth rate: the model, containing FBA as a limit, provides a better match to measured fluxes and it makes a wide range of predictions: on flux variability, regulation, and correlations; on the relative importance of stoichiometry vs. optimization; on scaling relations for growth rate distributions. We validate the latter here with single-cell data at different sub-inhibitory antibiotic concentrations. The model quantifies growth optimization as emerging from the interplay of competitive dynamics in the population and regulation of metabolism at the level of single cells.}, author = {De Martino, Daniele and Mc, Andersson Anna and Bergmiller, Tobias and Guet, Calin C and Tkacik, Gasper}, journal = {Nature Communications}, number = {1}, publisher = {Springer Nature}, title = {{Statistical mechanics for metabolic networks during steady state growth}}, doi = {10.1038/s41467-018-05417-9}, volume = {9}, year = {2018}, } @misc{13055, abstract = {Dataset for manuscript 'Social network plasticity decreases disease transmission in a eusocial insect' Compared to previous versions: - raw image files added - correction of URLs within README.txt file }, author = {Stroeymeyt, Nathalie and Grasse, Anna V and Crespi, Alessandro and Mersch, Danielle and Cremer, Sylvia and Keller, Laurent}, publisher = {Zenodo}, title = {{Social network plasticity decreases disease transmission in a eusocial insect}}, doi = {10.5281/ZENODO.1322669}, year = {2018}, } @misc{13059, abstract = {This dataset contains a GitHub repository containing all the data, analysis, Nextflow workflows and Jupyter notebooks to replicate the manuscript titled "Fast and accurate large multiple sequence alignments with a root-to-leaf regressive method". It also contains the Multiple Sequence Alignments (MSAs) generated and well as the main figures and tables from the manuscript. The repository is also available at GitHub (https://github.com/cbcrg/dpa-analysis) release `v1.2`. For details on how to use the regressive alignment algorithm, see the T-Coffee software suite (https://github.com/cbcrg/tcoffee).}, author = {Garriga, Edgar and di Tommaso, Paolo and Magis, Cedrik and Erb, Ionas and Mansouri, Leila and Baltzis, Athanasios and Laayouni, Hafid and Kondrashov, Fyodor and Floden, Evan and Notredame, Cedric}, publisher = {Zenodo}, title = {{Fast and accurate large multiple sequence alignments with a root-to-leaf regressive method}}, doi = {10.5281/ZENODO.2025846}, year = {2018}, } @article{131, abstract = {XY systems usually show chromosome-wide compensation of X-linked genes, while in many ZW systems, compensation is restricted to a minority of dosage-sensitive genes. Why such differences arose is still unclear. Here, we combine comparative genomics, transcriptomics and proteomics to obtain a complete overview of the evolution of gene dosage on the Z-chromosome of Schistosoma parasites. We compare the Z-chromosome gene content of African (Schistosoma mansoni and S. haematobium) and Asian (S. japonicum) schistosomes and describe lineage-specific evolutionary strata. We use these to assess gene expression evolution following sex-linkage. The resulting patterns suggest a reduction in expression of Z-linked genes in females, combined with upregulation of the Z in both sexes, in line with the first step of Ohno’s classic model of dosage compensation evolution. Quantitative proteomics suggest that post-transcriptional mechanisms do not play a major role in balancing the expression of Z-linked genes. }, author = {Picard, Marion A and Cosseau, Celine and Ferré, Sabrina and Quack, Thomas and Grevelding, Christoph and Couté, Yohann and Vicoso, Beatriz}, journal = {eLife}, publisher = {eLife Sciences Publications}, title = {{Evolution of gene dosage on the Z-chromosome of schistosome parasites}}, doi = {10.7554/eLife.35684}, volume = {7}, year = {2018}, } @article{132, abstract = {Pancreas development involves a coordinated process in which an early phase of cell segregation is followed by a longer phase of lineage restriction, expansion, and tissue remodeling. By combining clonal tracing and whole-mount reconstruction with proliferation kinetics and single-cell transcriptional profiling, we define the functional basis of pancreas morphogenesis. We show that the large-scale organization of mouse pancreas can be traced to the activity of self-renewing precursors positioned at the termini of growing ducts, which act collectively to drive serial rounds of stochastic ductal bifurcation balanced by termination. During this phase of branching morphogenesis, multipotent precursors become progressively fate-restricted, giving rise to self-renewing acinar-committed precursors that are conveyed with growing ducts, as well as ductal progenitors that expand the trailing ducts and give rise to delaminating endocrine cells. These findings define quantitatively how the functional behavior and lineage progression of precursor pools determine the large-scale patterning of pancreatic sub-compartments.}, author = {Sznurkowska, Magdalena and Hannezo, Edouard B and Azzarelli, Roberta and Rulands, Steffen and Nestorowa, Sonia and Hindley, Christopher and Nichols, Jennifer and Göttgens, Berthold and Huch, Meritxell and Philpott, Anna and Simons, Benjamin}, journal = {Developmental Cell}, number = {3}, pages = {360 -- 375}, publisher = {Cell Press}, title = {{Defining lineage potential and fate behavior of precursors during pancreas development}}, doi = {10.1016/j.devcel.2018.06.028}, volume = {46}, year = {2018}, } @inproceedings{133, abstract = {Synchronous programs are easy to specify because the side effects of an operation are finished by the time the invocation of the operation returns to the caller. Asynchronous programs, on the other hand, are difficult to specify because there are side effects due to pending computation scheduled as a result of the invocation of an operation. They are also difficult to verify because of the large number of possible interleavings of concurrent computation threads. We present synchronization, a new proof rule that simplifies the verification of asynchronous programs by introducing the fiction, for proof purposes, that asynchronous operations complete synchronously. Synchronization summarizes an asynchronous computation as immediate atomic effect. Modular verification is enabled via pending asynchronous calls in atomic summaries, and a complementary proof rule that eliminates pending asynchronous calls when components and their specifications are composed. We evaluate synchronization in the context of a multi-layer refinement verification methodology on a collection of benchmark programs.}, author = {Kragl, Bernhard and Qadeer, Shaz and Henzinger, Thomas A}, issn = {18688969}, location = {Beijing, China}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik}, title = {{Synchronizing the asynchronous}}, doi = {10.4230/LIPIcs.CONCUR.2018.21}, volume = {118}, year = {2018}, } @article{134, abstract = {The current state of the art in real-time two-dimensional water wave simulation requires developers to choose between efficient Fourier-based methods, which lack interactions with moving obstacles, and finite-difference or finite element methods, which handle environmental interactions but are significantly more expensive. This paper attempts to bridge this long-standing gap between complexity and performance, by proposing a new wave simulation method that can faithfully simulate wave interactions with moving obstacles in real time while simultaneously preserving minute details and accommodating very large simulation domains. Previous methods for simulating 2D water waves directly compute the change in height of the water surface, a strategy which imposes limitations based on the CFL condition (fast moving waves require small time steps) and Nyquist's limit (small wave details require closely-spaced simulation variables). This paper proposes a novel wavelet transformation that discretizes the liquid motion in terms of amplitude-like functions that vary over space, frequency, and direction, effectively generalizing Fourier-based methods to handle local interactions. Because these new variables change much more slowly over space than the original water height function, our change of variables drastically reduces the limitations of the CFL condition and Nyquist limit, allowing us to simulate highly detailed water waves at very large visual resolutions. Our discretization is amenable to fast summation and easy to parallelize. We also present basic extensions like pre-computed wave paths and two-way solid fluid coupling. Finally, we argue that our discretization provides a convenient set of variables for artistic manipulation, which we illustrate with a novel wave-painting interface.}, author = {Jeschke, Stefan and Skrivan, Tomas and Mueller Fischer, Matthias and Chentanez, Nuttapong and Macklin, Miles and Wojtan, Christopher J}, journal = {ACM Transactions on Graphics}, number = {4}, publisher = {ACM}, title = {{Water surface wavelets}}, doi = {10.1145/3197517.3201336}, volume = {37}, year = {2018}, } @article{135, abstract = {The Fluid Implicit Particle method (FLIP) reduces numerical dissipation by combining particles with grids. To improve performance, the subsequent narrow band FLIP method (NB‐FLIP) uses a FLIP‐based fluid simulation only near the liquid surface and a traditional grid‐based fluid simulation away from the surface. This spatially‐limited FLIP simulation significantly reduces the number of particles and alleviates a computational bottleneck. In this paper, we extend the NB‐FLIP idea even further, by allowing a simulation to transition between a FLIP‐like fluid simulation and a grid‐based simulation in arbitrary locations, not just near the surface. This approach leads to even more savings in memory and computation, because we can concentrate the particles only in areas where they are needed. More importantly, this new method allows us to seamlessly transition to smooth implicit surface geometry wherever the particle‐based simulation is unnecessary. Consequently, our method leads to a practical algorithm for avoiding the noisy surface artifacts associated with particle‐based liquid simulations, while simultaneously maintaining the benefits of a FLIP simulation in regions of dynamic motion.}, author = {Sato, Takahiro and Wojtan, Christopher J and Thuerey, Nils and Igarashi, Takeo and Ando, Ryoichi}, issn = {0167-7055}, journal = {Computer Graphics Forum}, number = {2}, pages = {169 -- 177}, publisher = {Wiley}, title = {{Extended narrow band FLIP for liquid simulations}}, doi = {10.1111/cgf.13351}, volume = {37}, year = {2018}, } @article{136, abstract = {Recent studies suggest that unstable, nonchaotic solutions of the Navier-Stokes equation may provide deep insights into fluid turbulence. In this article, we present a combined experimental and numerical study exploring the dynamical role of unstable equilibrium solutions and their invariant manifolds in a weakly turbulent, electromagnetically driven, shallow fluid layer. Identifying instants when turbulent evolution slows down, we compute 31 unstable equilibria of a realistic two-dimensional model of the flow. We establish the dynamical relevance of these unstable equilibria by showing that they are closely visited by the turbulent flow. We also establish the dynamical relevance of unstable manifolds by verifying that they are shadowed by turbulent trajectories departing from the neighborhoods of unstable equilibria over large distances in state space.}, author = {Suri, Balachandra and Tithof, Jeffrey and Grigoriev, Roman and Schatz, Michael}, journal = {Physical Review E}, number = {2}, publisher = {American Physical Society}, title = {{Unstable equilibria and invariant manifolds in quasi-two-dimensional Kolmogorov-like flow}}, doi = {10.1103/PhysRevE.98.023105}, volume = {98}, year = {2018}, } @article{137, abstract = {Fluorescent sensors are an essential part of the experimental toolbox of the life sciences, where they are used ubiquitously to visualize intra- and extracellular signaling. In the brain, optical neurotransmitter sensors can shed light on temporal and spatial aspects of signal transmission by directly observing, for instance, neurotransmitter release and spread. Here we report the development and application of the first optical sensor for the amino acid glycine, which is both an inhibitory neurotransmitter and a co-agonist of the N-methyl-d-aspartate receptors (NMDARs) involved in synaptic plasticity. Computational design of a glycine-specific binding protein allowed us to produce the optical glycine FRET sensor (GlyFS), which can be used with single and two-photon excitation fluorescence microscopy. We took advantage of this newly developed sensor to test predictions about the uneven spatial distribution of glycine in extracellular space and to demonstrate that extracellular glycine levels are controlled by plasticity-inducing stimuli.}, author = {Zhang, William and Herde, Michel and Mitchell, Joshua and Whitfield, Jason and Wulff, Andreas and Vongsouthi, Vanessa and Sanchez Romero, Inmaculada and Gulakova, Polina and Minge, Daniel and Breithausen, Björn and Schoch, Susanne and Janovjak, Harald L and Jackson, Colin and Henneberger, Christian}, journal = {Nature Chemical Biology}, number = {9}, pages = {861 -- 869}, publisher = {Nature Publishing Group}, title = {{Monitoring hippocampal glycine with the computationally designed optical sensor GlyFS}}, doi = {10.1038/s41589-018-0108-2}, volume = {14}, year = {2018}, } @article{139, abstract = {Genome-scale diversity data are increasingly available in a variety of biological systems, and can be used to reconstruct the past evolutionary history of species divergence. However, extracting the full demographic information from these data is not trivial, and requires inferential methods that account for the diversity of coalescent histories throughout the genome. Here, we evaluate the potential and limitations of one such approach. We reexamine a well-known system of mussel sister species, using the joint site frequency spectrum (jSFS) of synonymousmutations computed either fromexome capture or RNA-seq, in an Approximate Bayesian Computation (ABC) framework. We first assess the best sampling strategy (number of: individuals, loci, and bins in the jSFS), and show that model selection is robust to variation in the number of individuals and loci. In contrast, different binning choices when summarizing the jSFS, strongly affect the results: including classes of low and high frequency shared polymorphisms can more effectively reveal recent migration events. We then take advantage of the flexibility of ABC to compare more realistic models of speciation, including variation in migration rates through time (i.e., periodic connectivity) and across genes (i.e., genome-wide heterogeneity in migration rates). We show that these models were consistently selected as the most probable, suggesting that mussels have experienced a complex history of gene flow during divergence and that the species boundary is semi-permeable. Our work provides a comprehensive evaluation of ABC demographic inference in mussels based on the coding jSFS, and supplies guidelines for employing different sequencing techniques and sampling strategies. We emphasize, perhaps surprisingly, that inferences are less limited by the volume of data, than by the way in which they are analyzed.}, author = {Fraisse, Christelle and Roux, Camille and Gagnaire, Pierre and Romiguier, Jonathan and Faivre, Nicolas and Welch, John and Bierne, Nicolas}, journal = {PeerJ}, number = {7}, publisher = {PeerJ}, title = {{The divergence history of European blue mussel species reconstructed from Approximate Bayesian Computation: The effects of sequencing techniques and sampling strategies}}, doi = {10.7717/peerj.5198}, volume = {2018}, year = {2018}, } @article{14, abstract = {The intercellular transport of auxin is driven by PIN-formed (PIN) auxin efflux carriers. PINs are localized at the plasma membrane (PM) and on constitutively recycling endomembrane vesicles. Therefore, PINs can mediate auxin transport either by direct translocation across the PM or by pumping auxin into secretory vesicles (SVs), leading to its secretory release upon fusion with the PM. Which of these two mechanisms dominates is a matter of debate. Here, we addressed the issue with a mathematical modeling approach. We demonstrate that the efficiency of secretory transport depends on SV size, half-life of PINs on the PM, pH, exocytosis frequency and PIN density. 3D structured illumination microscopy (SIM) was used to determine PIN density on the PM. Combining this data with published values of the other parameters, we show that the transport activity of PINs in SVs would have to be at least 1000× greater than on the PM in order to produce a comparable macroscopic auxin transport. If both transport mechanisms operated simultaneously and PINs were equally active on SVs and PM, the contribution of secretion to the total auxin flux would be negligible. In conclusion, while secretory vesicle-mediated transport of auxin is an intriguing and theoretically possible model, it is unlikely to be a major mechanism of auxin transport inplanta.}, author = {Hille, Sander and Akhmanova, Maria and Glanc, Matous and Johnson, Alexander J and Friml, Jirí}, issn = {1422-0067}, journal = {International Journal of Molecular Sciences}, number = {11}, publisher = {MDPI}, title = {{Relative contribution of PIN-containing secretory vesicles and plasma membrane PINs to the directed auxin transport: Theoretical estimation}}, doi = {10.3390/ijms19113566}, volume = {19}, year = {2018}, } @inproceedings{140, abstract = {Reachability analysis is difficult for hybrid automata with affine differential equations, because the reach set needs to be approximated. Promising abstraction techniques usually employ interval methods or template polyhedra. Interval methods account for dense time and guarantee soundness, and there are interval-based tools that overapproximate affine flowpipes. But interval methods impose bounded and rigid shapes, which make refinement expensive and fixpoint detection difficult. Template polyhedra, on the other hand, can be adapted flexibly and can be unbounded, but sound template refinement for unbounded reachability analysis has been implemented only for systems with piecewise constant dynamics. We capitalize on the advantages of both techniques, combining interval arithmetic and template polyhedra, using the former to abstract time and the latter to abstract space. During a CEGAR loop, whenever a spurious error trajectory is found, we compute additional space constraints and split time intervals, and use these space-time interpolants to eliminate the counterexample. Space-time interpolation offers a lazy, flexible framework for increasing precision while guaranteeing soundness, both for error avoidance and fixpoint detection. To the best of out knowledge, this is the first abstraction refinement scheme for the reachability analysis over unbounded and dense time of affine hybrid systems, which is both sound and automatic. We demonstrate the effectiveness of our algorithm with several benchmark examples, which cannot be handled by other tools.}, author = {Frehse, Goran and Giacobbe, Mirco and Henzinger, Thomas A}, issn = {03029743}, location = {Oxford, United Kingdom}, pages = {468 -- 486}, publisher = {Springer}, title = {{Space-time interpolants}}, doi = {10.1007/978-3-319-96145-3_25}, volume = {10981}, year = {2018}, } @inproceedings{141, abstract = {Given a model and a specification, the fundamental model-checking problem asks for algorithmic verification of whether the model satisfies the specification. We consider graphs and Markov decision processes (MDPs), which are fundamental models for reactive systems. One of the very basic specifications that arise in verification of reactive systems is the strong fairness (aka Streett) objective. Given different types of requests and corresponding grants, the objective requires that for each type, if the request event happens infinitely often, then the corresponding grant event must also happen infinitely often. All ω -regular objectives can be expressed as Streett objectives and hence they are canonical in verification. To handle the state-space explosion, symbolic algorithms are required that operate on a succinct implicit representation of the system rather than explicitly accessing the system. While explicit algorithms for graphs and MDPs with Streett objectives have been widely studied, there has been no improvement of the basic symbolic algorithms. The worst-case numbers of symbolic steps required for the basic symbolic algorithms are as follows: quadratic for graphs and cubic for MDPs. In this work we present the first sub-quadratic symbolic algorithm for graphs with Streett objectives, and our algorithm is sub-quadratic even for MDPs. Based on our algorithmic insights we present an implementation of the new symbolic approach and show that it improves the existing approach on several academic benchmark examples.}, author = {Chatterjee, Krishnendu and Henzinger, Monika H and Loitzenbauer, Veronika and Oraee, Simin and Toman, Viktor}, location = {Oxford, United Kingdom}, pages = {178--197}, publisher = {Springer}, title = {{Symbolic algorithms for graphs and Markov decision processes with fairness objectives}}, doi = {10.1007/978-3-319-96142-2_13}, volume = {10982}, year = {2018}, } @inproceedings{14198, abstract = {High-dimensional time series are common in many domains. Since human cognition is not optimized to work well in high-dimensional spaces, these areas could benefit from interpretable low-dimensional representations. However, most representation learning algorithms for time series data are difficult to interpret. This is due to non-intuitive mappings from data features to salient properties of the representation and non-smoothness over time. To address this problem, we propose a new representation learning framework building on ideas from interpretable discrete dimensionality reduction and deep generative modeling. This framework allows us to learn discrete representations of time series, which give rise to smooth and interpretable embeddings with superior clustering performance. We introduce a new way to overcome the non-differentiability in discrete representation learning and present a gradient-based version of the traditional self-organizing map algorithm that is more performant than the original. Furthermore, to allow for a probabilistic interpretation of our method, we integrate a Markov model in the representation space. This model uncovers the temporal transition structure, improves clustering performance even further and provides additional explanatory insights as well as a natural representation of uncertainty. We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set. Our learned representations compare favorably with competitor methods and facilitate downstream tasks on the real world data.}, author = {Fortuin, Vincent and Hüser, Matthias and Locatello, Francesco and Strathmann, Heiko and Rätsch, Gunnar}, booktitle = {International Conference on Learning Representations}, location = {New Orleans, LA, United States}, title = {{SOM-VAE: Interpretable discrete representation learning on time series}}, year = {2018}, } @inproceedings{142, abstract = {We address the problem of analyzing the reachable set of a polynomial nonlinear continuous system by over-approximating the flowpipe of its dynamics. The common approach to tackle this problem is to perform a numerical integration over a given time horizon based on Taylor expansion and interval arithmetic. However, this method results to be very conservative when there is a large difference in speed between trajectories as time progresses. In this paper, we propose to use combinations of barrier functions, which we call piecewise barrier tube (PBT), to over-approximate flowpipe. The basic idea of PBT is that for each segment of a flowpipe, a coarse box which is big enough to contain the segment is constructed using sampled simulation and then in the box we compute by linear programming a set of barrier functions (called barrier tube or BT for short) which work together to form a tube surrounding the flowpipe. The benefit of using PBT is that (1) BT is independent of time and hence can avoid being stretched and deformed by time; and (2) a small number of BTs can form a tight over-approximation for the flowpipe, which means that the computation required to decide whether the BTs intersect the unsafe set can be reduced significantly. We implemented a prototype called PBTS in C++. Experiments on some benchmark systems show that our approach is effective.}, author = {Kong, Hui and Bartocci, Ezio and Henzinger, Thomas A}, location = {Oxford, United Kingdom}, pages = {449 -- 467}, publisher = {Springer}, title = {{Reachable set over-approximation for nonlinear systems using piecewise barrier tubes}}, doi = {10.1007/978-3-319-96145-3_24}, volume = {10981}, year = {2018}, } @inproceedings{14201, abstract = {Variational inference is a popular technique to approximate a possibly intractable Bayesian posterior with a more tractable one. Recently, boosting variational inference has been proposed as a new paradigm to approximate the posterior by a mixture of densities by greedily adding components to the mixture. However, as is the case with many other variational inference algorithms, its theoretical properties have not been studied. In the present work, we study the convergence properties of this approach from a modern optimization viewpoint by establishing connections to the classic Frank-Wolfe algorithm. Our analyses yields novel theoretical insights regarding the sufficient conditions for convergence, explicit rates, and algorithmic simplifications. Since a lot of focus in previous works for variational inference has been on tractability, our work is especially important as a much needed attempt to bridge the gap between probabilistic models and their corresponding theoretical properties.}, author = {Locatello, Francesco and Khanna, Rajiv and Ghosh, Joydeep and Rätsch, Gunnar}, booktitle = {Proceedings of the 21st International Conference on Artificial Intelligence and Statistics}, location = {Playa Blanca, Lanzarote}, pages = {464--472}, publisher = {ML Research Press}, title = {{Boosting variational inference: An optimization perspective}}, volume = {84}, year = {2018}, } @inproceedings{14202, abstract = {Approximating a probability density in a tractable manner is a central task in Bayesian statistics. Variational Inference (VI) is a popular technique that achieves tractability by choosing a relatively simple variational family. Borrowing ideas from the classic boosting framework, recent approaches attempt to \emph{boost} VI by replacing the selection of a single density with a greedily constructed mixture of densities. In order to guarantee convergence, previous works impose stringent assumptions that require significant effort for practitioners. Specifically, they require a custom implementation of the greedy step (called the LMO) for every probabilistic model with respect to an unnatural variational family of truncated distributions. Our work fixes these issues with novel theoretical and algorithmic insights. On the theoretical side, we show that boosting VI satisfies a relaxed smoothness assumption which is sufficient for the convergence of the functional Frank-Wolfe (FW) algorithm. Furthermore, we rephrase the LMO problem and propose to maximize the Residual ELBO (RELBO) which replaces the standard ELBO optimization in VI. These theoretical enhancements allow for black box implementation of the boosting subroutine. Finally, we present a stopping criterion drawn from the duality gap in the classic FW analyses and exhaustive experiments to illustrate the usefulness of our theoretical and algorithmic contributions.}, author = {Locatello, Francesco and Dresdner, Gideon and Khanna, Rajiv and Valera, Isabel and Rätsch, Gunnar}, booktitle = {Advances in Neural Information Processing Systems}, isbn = {9781510884472}, issn = {1049-5258}, location = {Montreal, Canada}, publisher = {Neural Information Processing Systems Foundation}, title = {{Boosting black box variational inference}}, volume = {31}, year = {2018}, } @inproceedings{14203, abstract = {We propose a conditional gradient framework for a composite convex minimization template with broad applications. Our approach combines smoothing and homotopy techniques under the CGM framework, and provably achieves the optimal O(1/k−−√) convergence rate. We demonstrate that the same rate holds if the linear subproblems are solved approximately with additive or multiplicative error. In contrast with the relevant work, we are able to characterize the convergence when the non-smooth term is an indicator function. Specific applications of our framework include the non-smooth minimization, semidefinite programming, and minimization with linear inclusion constraints over a compact domain. Numerical evidence demonstrates the benefits of our framework.}, author = {Yurtsever, Alp and Fercoq, Olivier and Locatello, Francesco and Cevher, Volkan}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, location = {Stockholm, Sweden}, pages = {5727--5736}, publisher = {ML Research Press}, title = {{A conditional gradient framework for composite convex minimization with applications to semidefinite programming}}, volume = {80}, year = {2018}, }