A REPRESENTATION LEARNING PERSPECTIVE ON THE IMPORTANCE OF TRAIN-VALIDATION SPLITTING IN META-LEARNING

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

A SAMPLE COMPLEXITY SEPARATION BETWEEN NON-CONVEX AND CONVEX MET A-LEARNING

von Saunshi, Nikunj
Veröffentlicht in International Conference on Machine Learning (37. : 2020 : Online) 37th International Conference on Machine Learning (ICML 2020) ; Part 11 of 15 (2021)

Predicting What You Already Know Helps: Provable Self-Supervised Learning

von Lee, Jason D
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 1 of 36 (2022)

New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound

von Gupta, Arushi
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 43 of 50 (2023)

PROVABLE REPRESENTATION LEARNING FOR IMITATION LEARNING VIA BI-LEVEL OPTIMIZATION

von Arora, Sanjeev
Veröffentlicht in International Conference on Machine Learning (37. : 2020 : Online) 37th International Conference on Machine Learning (ICML 2020) ; Part 1 of 15 (2021)

Understanding Contrastive Learning Requires Incorporating Inductive Biases

von Saunshi, Nikunj
Veröffentlicht in International Conference on Machine Learning (39. : 2022 : Baltimore, Md.; Online) International Conference on Machine Learning (ICML 2022) ; Part 23 of 33 (2023)