A sparsity principle for partially observable causal representation learning
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.

Shortcuts for causal discovery of nonlinear models by score matching
F. Montagna, N. Noceti, L. Rosasco, F. Locatello, ArXiv (n.d.).

Unsupervised open-vocabulary object localization in videos
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.).

Object-centric multiple object tracking
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.).

Lattice expansion enables interstitial doping to achieve a high average ZT in ntype PbS
Z. Liu, T. Hong, L. Xu, S. Wang, X. Gao, C. Chang, X. Ding, Y. Xiao, L. Zhao, Interdisciplinary Materials 2 (2023) 161–170.

Parakeet: Practical key transparency for end-to-end eEncrypted messaging
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.

Artefact for: Correct Approximation of Stationary Distributions
T. Meggendorfer, (2023).

Data-assessing memory in convection schemes using idealized tests
Y.-L. Hwong, M. Colin, P. Aglas, C.J. Muller, S.C. Sherwood, (2023).

Universal Functionals in Density Functional Theory
M. Lewin, E.H. Lieb, R. Seiringer, in:, E. Cances, G. Friesecke (Eds.), Density Functional Theory, 1st ed., Springer, 2023, pp. 115–182.

A framework for grassroots research collaboration in machine learning and global health
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.

A flexible toolchain for symbolic rabin games under fair and stochastic uncertainties
R. Majumdar, K. Mallik, M. Rychlicki, A.-K. Schmuck, S. Soudjani, (2023).

Lincheck: A practical framework for testing concurrent data structures on JVM
N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, D.-A. Alistarh, (2023).

Compositional policy learning in stochastic control systems with formal guarantees
D. Zikelic, M. Lechner, A. Verma, K. Chatterjee, T.A. Henzinger, in:, 37th Conference on Neural Information Processing Systems, 2023.

aggregation_data
S. Curk, (2023).

Monitoring hyperproperties with prefix transducers
M. Chalupa, T.A. Henzinger, (2023).

1-Lipschitz neural networks are more expressive with N-activations
B. Prach, C. Lampert, ArXiv (n.d.).

The Hasse principle for random Fano hypersurfaces
T.D. Browning, P.L. Boudec, W. Sawin, Annals of Mathematics 197 (2023) 1115–1203.

Integral points of bounded height on a log Fano threefold
F.A. Wilsch, International Mathematics Research Notices 2023 (2023) 6780–6808.

Divergence of separated nets with respect to displacement equivalence
M. Dymond, V. Kaluza, Geometriae Dedicata (2023).

Highly irregular separated nets
M. Dymond, V. Kaluza, Israel Journal of Mathematics 253 (2023) 501–554.