CAN SUBNETWORK STRUCTURE BE THE KEY TO OUT-OF-DISTRIBUTION GENERALIZATION?

von ZHANG, DINGHUAI
Veröffentlicht in International Conference on Machine Learning (38. : 2021 : Online) International Conference on Machine Learning (ICML 2021 ; Part 16 of 16 (2022)

Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework

von Zhang, Dinghuai
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 3 of 27 (2021)

YOU ONLY PROPAGATE ONCE: ACCELERATING ADVERSARIAL TRAINING VIA MAXIMAL PRINCIPLE

von Zhang, Dinghuai
Veröffentlicht in NeurIPS (33. : 2019 : Vancouver, British Columbia) 32nd Conference on Neural Information Processing Systems (NeurIPS 2019) ; Volume 1 of 20 (2020)

Generative Flow Networks for Discrete Probabilistic Modeling

von Zhang, Dinghuai
Veröffentlicht in International Conference on Machine Learning (39. : 2022 : Baltimore, Md.; Online) International Conference on Machine Learning (ICML 2022) ; Part 32 of 33 (2023)

Building Robust Ensembles Via Margin Boosting

von Zhang, Dinghuai
Veröffentlicht in International Conference on Machine Learning (39. : 2022 : Baltimore, Md.; Online) International Conference on Machine Learning (ICML 2022) ; Part 32 of 33 (2023)

Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization

von Ahuja, Kartik
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 5 of 36 (2022)

OUT-OF-DISTRIBUTION GENERALIZATION VIA RISK EXTRAPOLATION

von KRUEGER, DAVID
Veröffentlicht in International Conference on Machine Learning (38. : 2021 : Online) International Conference on Machine Learning (ICML 2021 ; Part 8 of 16 (2022)

Biological Sequence Design with GFlowNets

von Jain, Moksh
Veröffentlicht in International Conference on Machine Learning (39. : 2022 : Baltimore, Md.; Online) International Conference on Machine Learning (ICML 2022) ; Part 12 of 33 (2023)