ADVERSARIALLY ROBUST FEW-SHOT LEARNING: A META-LEARNING APPROACH

von Goldblum, Micah
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 22 of 27 (2021)

Encoding Robustness to Image Style via Adversarial Feature Perturbations

von Shu, Manli
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 33 of 36 (2022)

Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers

von Yang, Wanqian
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 27 of 50 (2023)

Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch

von Souri, Hossein
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 25 of 50 (2023)

DATA AUGMENTATION FOR META-LEARNING

von NI, RENKUN
Veröffentlicht in International Conference on Machine Learning (38. : 2021 : Online) International Conference on Machine Learning (ICML 2021 ; Part 11 of 16 (2022)

JUST HOW TOXIC IS DATA POISONING? A UNIFIED BENCHMARK FOR BACKDOOR AND DATA POISONING ATTACKS

von SCHWARZSCHILD, AVI
Veröffentlicht in International Conference on Machine Learning (38. : 2021 : Online) International Conference on Machine Learning (ICML 2021 ; Part 12 of 16 (2022)

Fishing for User Data in Large-Batch Federated Learning Via Gradient Magnification

von Wen, Yuxin
Veröffentlicht in International Conference on Machine Learning (39. : 2022 : Baltimore, Md.; Online) International Conference on Machine Learning (ICML 2022) ; Part 29 of 33 (2023)

Bayesian Model Selection, the Marginal Likelihood, and Generalization

von Lotfi, Sanae
Veröffentlicht in International Conference on Machine Learning (39. : 2022 : Baltimore, Md.; Online) International Conference on Machine Learning (ICML 2022) ; Part 18 of 33 (2023)

Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability

von Levin, Roman
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 21 of 50 (2023)

Autoregressive Perturbations for Data Poisoning

von Sandoval-Segura, Pedro
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 36 of 50 (2023)

PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization

von Lotfi, Sanae
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 41 of 50 (2023)

Adversarial Examples Make Strong Poisons

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

Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks

von Schwarzschild, Avi
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 9 of 36 (2022)

End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking

von Bansal, Arpit
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 27 of 50 (2023)

Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors

von Shwartz-Ziv, Ravid
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 36 of 50 (2023)