Simulated Annealing for JPEG Quantization. - Max Hopkins; Michael Mitzenmacher; Sebastian Wagner-Carena (DCC, 2018)

A Novel CMB Component Separation Method: Hierarchical Generalized Morphological Component Analysis. - Sebastian Wagner-Carena; Max Hopkins; Ana Diaz Rivero; Cora Dvorkin (CoRR, 2019)

Noise-tolerant, Reliable Active Classification with Comparison Queries. - Max Hopkins; Daniel Kane 001; Shachar Lovett; Gaurav Mahajan (COLT, 2020)

Point Location and Active Learning: Learning Halfspaces Almost Optimally. - Max Hopkins; Daniel Kane 001; Shachar Lovett; Gaurav Mahajan (FOCS, 2020)

The Power of Comparisons for Actively Learning Linear Classifiers. - Max Hopkins; Daniel Kane 001; Shachar Lovett (NeurIPS, 2020)

High Dimensional Expanders: Random Walks, Pseudorandomness, and Unique Games. - Max Hopkins; Tali Kaufman; Shachar Lovett (Electron. Colloquium Comput. Complex., 2020)

Bounded Memory Active Learning through Enriched Queries. - Max Hopkins; Daniel Kane 001; Shachar Lovett; Michal Moshkovitz (COLT, 2021)

Hypercontractivity on High Dimensional Expanders: a Local-to-Global Approach for Higher Moments. - Mitali Bafna; Max Hopkins; Tali Kaufman; Shachar Lovett (Electron. Colloquium Comput. Complex., 2021)

Eigenstripping, Spectral Decay, and Edge-Expansion on Posets. - Jason Gaitonde; Max Hopkins; Tali Kaufman; Shachar Lovett; Ruizhe Zhang 001 (APPROX/RANDOM, 2022)

Active Learning Polynomial Threshold Functions. - Omri Ben-Eliezer; Max Hopkins; Chutong Yang; Hantao Yu (NeurIPS, 2022)

High Dimensional Expanders: Eigenstripping, Pseudorandomness, and Unique Games. - Mitali Bafna; Max Hopkins; Tali Kaufman; Shachar Lovett (SODA, 2022)

Hypercontractivity on high dimensional expanders. - Mitali Bafna; Max Hopkins; Tali Kaufman; Shachar Lovett (STOC, 2022)

Explicit Lower Bounds Against Ω(n)-Rounds of Sum-of-Squares. - Max Hopkins; Ting-Chun Lin (Electron. Colloquium Comput. Complex., 2022)

Robust Empirical Risk Minimization with Tolerance. - Robi Bhattacharjee; Max Hopkins; Akash Kumar 010; Hantao Yu; Kamalika Chaudhuri (ALT, 2023)

Sampling Equilibria: Fast No-Regret Learning in Structured Games. - Daniel Beaglehole; Max Hopkins; Daniel Kane 001; Sihan Liu; Shachar Lovett (SODA, 2023)

Stability Is Stable: Connections between Replicability, Privacy, and Adaptive Generalization. - Mark Bun; Marco Gaboardi; Max Hopkins; Russell Impagliazzo; Rex Lei; Toniann Pitassi; Satchit Sivakumar; Jessica Sorrell (STOC, 2023)

Replicability in High Dimensional Statistics. - Max Hopkins; Russell Impagliazzo; Daniel M. Kane; Sihan Liu; Christopher Ye 001 (FOCS, 2024)

Chernoff Bounds and Reverse Hypercontractivity on HDX. - Yotam Dikstein; Max Hopkins (Electron. Colloquium Comput. Complex., 2024)

Realizable Learning is All You Need. - Max Hopkins; Daniel M. Kane; Shachar Lovett; Gaurav Mahajan (TheoretiCS, 2024)

High Dimensional Expanders in Analysis and Computation - Max Hopkins (2024)

Do PAC-Learners Learn the Marginal Distribution? - Max Hopkins; Daniel M. Kane; Shachar Lovett; Gaurav Mahajan (ALT, 2025)

Hypercontractivity on HDX II: Symmetrization and q-Norms. - Max Hopkins (STOC, 2025)

The Role of Randomness in Stability. - Max Hopkins; Shay Moran (CoRR, 2025)

From Generative to Episodic: Sample-Efficient Replicable Reinforcement Learning. - Max Hopkins; Sihan Liu; Christopher Ye 001; Yuichi Yoshida (CoRR, 2025)