Robust Influence Maximization for Hyperparametric Models. - Dimitris Kalimeris; Gal Kaplun; Yaron Singer (ICML, 2019)

SGD on Neural Networks Learns Functions of Increasing Complexity. - Preetum Nakkiran; Gal Kaplun; Dimitris Kalimeris; Tristan Yang; Benjamin L. Edelman; Fred Zhang; Boaz Barak (NeurIPS, 2019)

Deep Double Descent: Where Bigger Models and More Data Hurt. - Preetum Nakkiran; Gal Kaplun; Yamini Bansal; Tristan Yang; Boaz Barak; Ilya Sutskever (ICLR, 2020)

Robustness from Simple Classifiers. - Sharon Qian; Dimitris Kalimeris; Gal Kaplun; Yaron Singer (CoRR, 2020)

For self-supervised learning, Rationality implies generalization, provably. - Yamini Bansal; Gal Kaplun; Boaz Barak (ICLR, 2021)

For Manifold Learning, Deep Neural Networks can be Locality Sensitive Hash Functions. - Nishanth Dikkala; Gal Kaplun; Rina Panigrahy (CoRR, 2021)

Knowledge Distillation: Bad Models Can Be Good Role Models. - Gal Kaplun; Eran Malach; Preetum Nakkiran; Shai Shalev-Shwartz (NeurIPS, 2022)

Deconstructing Distributions: A Pointwise Framework of Learning. - Gal Kaplun; Nikhil Ghosh; Saurabh Garg; Boaz Barak; Preetum Nakkiran (ICLR, 2023)

SubTuning: Efficient Finetuning for Multi-Task Learning. - Gal Kaplun; Andrey Gurevich; Tal Swisa; Mazor David; Shai Shalev-Shwartz; Eran Malach (CoRR, 2023)

Corgi^2: A Hybrid Offline-Online Approach To Storage-Aware Data Shuffling For SGD. - Etay Livne; Gal Kaplun; Eran Malach; Shai Shalev-Schwatz (CoRR, 2023)

Beyond Implicit Bias: The Insignificance of SGD Noise in Online Learning. - Nikhil Vyas 001; Depen Morwani; Rosie Zhao; Gal Kaplun; Sham M. Kakade; Boaz Barak (ICML, 2024)