Controlling SelfAssembly of Nanoparticles Using Light
T. Bian, Z. Chu, R. Klajn, in:, N. Giuseppone, A. Walther (Eds.), OutofEquilibrium (Supra)Molecular Systems and Materials, Wiley, 2021, pp. 241–273.

Evolution of accretor stars in massive binaries: Broader implications from modeling ;6 Ophiuchi
M. Renzo, Y.L.L. Götberg, The Astrophysical Journal 923 (2021).

Pre-explosion properties of Helium star donors to thermonuclear supernovae
T.L.S. Wong, J. Schwab, Y.L.L. Götberg, The Astrophysical Journal 922 (2021).

Spectral signatures of population III and envelope-stripped stars in galaxies at the epoch of reionization
E. Berzin, A. Secunda, R. Cen, A. Menegas, Y.L.L. Götberg, The Astrophysical Journal 918 (2021).

The young massive SMC cluster NGC 330 seen by MUSE. II. Multiplicity properties of the massive-star population
J. Bodensteiner, H. Sana, C. Wang, N. Langer, L. Mahy, G. Banyard, A. de Koter, S.E. de Mink, C.J. Evans, Y.L.L. Götberg, L.R. Patrick, F.R.N. Schneider, F. Tramper, Astronomy &; Astrophysics 652 (2021).

Binary-stripped stars as core-collapse supernovae progenitors
D. Vartanyan, E. Laplace, M. Renzo, Y.L.L. Götberg, A. Burrows, S.E. de Mink, The Astrophysical Journal Letters 916 (2021).

The detection and characterization of Be+sdO binaries from HST/STIS FUV spectroscopy
L. Wang, D.R. Gies, G.J. Peters, Y.L.L. Götberg, S.D. Chojnowski, K.V. Lester, S.B. Howell, The Astronomical Journal 161 (2021).

Attosecond interferometry of shape resonances in the recoil frame of C
S. Heck, D.R. Baykusheva, M. Han, J.-B. Ji, C. Perry, X. Gong, H.J. Wörner, Science Advances 7 (2021).

All-optical probe of three-dimensional topological insulators based on high-harmonic generation by circularly polarized laser fields
D.R. Baykusheva, A. Chacón, J. Lu, T.P. Bailey, J.A. Sobota, H. Soifer, P.S. Kirchmann, C. Rotundu, C. Uher, T.F. Heinz, D.A. Reis, S. Ghimire, Nano Letters 21 (2021) 8970–8978.

Strong-field physics in three-dimensional topological insulators
D.R. Baykusheva, A. Chacón, D. Kim, D.E. Kim, D.A. Reis, S. Ghimire, Physical Review A 103 (2021).

Science with the ultraviolet explorer (UVEX)
S.R. Kulkarni, F.A. Harrison, B.W. Grefenstette, H.P. Earnshaw, I. Andreoni, D.A. Berg, J.S. Bloom, S.B. Cenko, R. Chornock, J.L. Christiansen, M.W. Coughlin, A.W. Criswell, B. Darvish, K.K. Das, K. De, L. Dessart, D. Dixon, B. Dorsman, K.E.-B. Kareem El-Badry, C. Evans, K.E.S. Ford, C. Fremling, B.T. Gansicke, S. Gezari, Y.L.L. Götberg, G.M. Green, M.J. Graham, M. Heida, A.Y.Q. Ho, A.D. Jaodand, C.M.J.-K. Christopher M. Johns-Krull, M.M. Kasliwal, M. Lazzarini, W. Lu, R. Margutti, D.C. Martin, D.C. Masters, B. McKernan, Y. Naze, S.M. Nissanke, B. Parazin, D.A. Perley, E.S. Phinney, A.L. Piro, G. Raaijmakers, G. Rauw, A.C. Rodriguez, H. Sana, P. Senchyna, L.P. Singer, J.J. Spake, K.G. Stassun, D. Stern, H.I. Teplitz, D.R. Weisz, Y. Yao, ArXiv (n.d.).

Toward causal representation learning
B. Scholkopf, F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, Y. Bengio, Proceedings of the IEEE 109 (2021) 612–634.

Neighborhood contrastive learning applied to online patient monitoring
H. Yèche, G. Dresdner, F. Locatello, M. Hüser, G. Rätsch, in:, Proceedings of 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 11964–11974.

On disentangled representations learned from correlated data
F. Träuble, E. Creager, N. Kilbertus, F. Locatello, A. Dittadi, A. Goyal, B. Schölkopf, S. Bauer, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 10401–10412.

On the transfer of disentangled representations in realistic settings
A. Dittadi, F. Träuble, F. Locatello, M. Wüthrich, V. Agrawal, O. Winther, S. Bauer, B. Schölkopf, in:, The Ninth International Conference on Learning Representations, 2021.

Self-supervised learning with data augmentations provably isolates content from style
J. von Kügelgen, Y. Sharma, L. Gresele, W. Brendel, B. Schölkopf, M. Besserve, F. Locatello, in:, Advances in Neural Information Processing Systems, 2021, pp. 16451–16467.

Dynamic inference with neural interpreters
N. Rahaman, M.W. Gondal, S. Joshi, P. Gehler, Y. Bengio, F. Locatello, B. Schölkopf, in:, Advances in Neural Information Processing Systems, 2021, pp. 10985–10998.

Boosting variational inference with locally adaptive step-sizes
G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, G. Rätsch, in:, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, 2021, pp. 2337–2343.

Backward-compatible prediction updates: A probabilistic approach
F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, P. Gehler, in:, 35th Conference on Neural Information Processing Systems, 2021, pp. 116–128.

Enforcing and discovering structure in machine learning
F. Locatello, ArXiv (n.d.).