R. Sahu, “Cavity quantum electrooptics,” Institute of Science and Technology Austria, 2023.
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M. Lechner, D. Zikelic, K. Chatterjee, T. A. Henzinger, and D. Rus, “Quantization-aware interval bound propagation for training certifiably robust quantized neural networks,” in Proceedings of the 37th AAAI Conference on Artificial Intelligence, Washington, DC, United States, 2023, vol. 37, no. 12, pp. 14964–14973.
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arXiv: 2211.16187

G. Avni, I. R. Jecker, and D. Zikelic, “Bidding graph games with partially-observable budgets,” in Proceedings of the 37th AAAI Conference on Artificial Intelligence, Washington, DC, United States, 2023, vol. 37, no. 5, pp. 5464–5471.
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arXiv: 2211.13626

K. Tojo, A. Shamir, B. Bickel, and N. Umetani, “Stealth shaper: Reflectivity optimization as surface stylization,” in SIGGRAPH 2023 Conference Proceedings, Los Angeles, CA, United States, 2023.
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arXiv: 2305.05944

D. R. Ladle and S. Hippenmeyer, “Loss of ET/E in motor neurons leads to reduced monosynaptic inputs from proprioceptive sensory neurons,” Journal of Neurophysiology, vol. 129, no. 3. American Physiological Society, pp. 501–512, 2023.
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T. A. Henzinger, M. Karimi, K. Kueffner, and K. Mallik, “Monitoring algorithmic fairness,” in Computer Aided Verification, Paris, France, 2023, vol. 13965, pp. 358–382.
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arXiv: 2305.15979

J. Glajzer et al., “Impact of BRCA mutation status on tumor dissemination pattern, surgical outcome and patient survival in primary and recurrent high-grade serous ovarian cancer: A multicenter retrospective study by the Ovarian Cancer Therapy-Innovative Models Prolong Survival (OCTIPS) consortium,” Annals of Surgical Oncology, vol. 30. Springer Nature, pp. 35–45, 2023.
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J. Glajzer et al., “ASO Visual Abstract: Impact of BRCA mutation status on tumor dissemination pattern, surgical outcome, and patient survival in primary and recurrent high-grade serous ovarian cancer (HGSOC). A multicenter, retrospective study of the ovarian cancer therapy—innovative models prolong survival (OCTIPS) consortium,” Annals of Surgical Oncology, vol. 30. Springer Nature, pp. 46–47, 2023.
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N. C. Vierra et al., “Neuronal ER-plasma membrane junctions couple excitation to C+-activated PKA signaling,” Nature Communications, vol. 14. Springer Nature, 2023.
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J. Kretinsky, T. Meggendorfer, M. Prokop, and S. Rieder, “Guessing winning policies in LTL synthesis by semantic learning,” in 35th International Conference on Computer Aided Verification , Paris, France, 2023, vol. 13964, pp. 390–414.
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S. Sinha, P. Gehler, F. Locatello, and B. Schiele, “TeST: Test-time Self-Training under distribution shift,” in 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, United States, 2023.
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arXiv: 2209.11459

S. Mathis and L. A. Bugnet, “Asymmetries of frequency splittings of dipolar mixed modes: A window on the topology of deep magnetic fields,” Astronomy and Astrophysics, vol. 676. EDP Sciences, 2023.
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J. Gamper, F. Kluibenschedl, A. K. H. Weiss, and T. S. Hofer, “Accessing position space wave functions in band structure calculations of periodic systems - a generalized, adapted numerov implementation for one-, two-, and three-dimensional quantum problems,” Journal of Physical Chemistry Letters, vol. 14, no. 33. American Chemical Society, pp. 7395–7403, 2023.
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Z. Zhu, F. Liu, G. G. Chrysos, F. Locatello, and V. Cevher, “Benign overfitting in deep neural networks under lazy training,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, United States, 2023, vol. 202, pp. 43105–43128.
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M. F. Burg et al., “A data augmentation perspective on diffusion models and retrieval,” arXiv. .
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F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Causal discovery with score matching on additive models with arbitrary noise,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 2023.
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arXiv: 2304.03265

F. Montagna, N. Noceti, L. Rosasco, K. Zhang, and F. Locatello, “Scalable causal discovery with score matching,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 2023.
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arXiv: 2304.03382

Y. Liu et al., “Causal triplet: An open challenge for intervention-centric causal representation learning,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 2023.
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arXiv: 2301.05169

L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, and E. Rodolà, “Relative representations enable zero-shot latent space communication,” in The 11th International Conference on Learning Representations, Kigali, Rwanda, 2023.
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arXiv: 2209.15430

M. Tangemann et al., “Unsupervised object learning via common fate,” in 2nd Conference on Causal Learning and Reasoning, Tübingen, Germany, 2023.
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arXiv: 2110.06562