Max Hopkins

According to our database, Max Hopkins authored at least 18 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Realizable Learning is All You Need.

Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan

TheoretiCS, 2024

2023

Do PAC-Learners Learn the Marginal Distribution?

Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan

CoRR, 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

Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 2023

Sampling Equilibria: Fast No-Regret Learning in Structured Games.

Daniel Beaglehole, Max Hopkins, Daniel Kane, Sihan Liu, Shachar Lovett

Proceedings of the 2023 ACM-SIAM Symposium on Discrete Algorithms, 2023

Robust Empirical Risk Minimization with Tolerance.

Robi Bhattacharjee, Max Hopkins, Akash Kumar, Hantao Yu, Kamalika Chaudhuri

Proceedings of the International Conference on Algorithmic Learning Theory, 2023

2022

Explicit Lower Bounds Against Ω(n)-Rounds of Sum-of-Squares.

Max Hopkins, Ting-Chun Lin

Electron. Colloquium Comput. Complex., 2022

Hypercontractivity on high dimensional expanders.

Mitali Bafna, Max Hopkins, Tali Kaufman, Shachar Lovett

Proceedings of the STOC '22: 54th Annual ACM SIGACT Symposium on Theory of Computing, Rome, Italy, June 20, 2022

High Dimensional Expanders: Eigenstripping, Pseudorandomness, and Unique Games.

Mitali Bafna, Max Hopkins, Tali Kaufman, Shachar Lovett

Proceedings of the 2022 ACM-SIAM Symposium on Discrete Algorithms, 2022

Active Learning Polynomial Threshold Functions.

Omri Ben-Eliezer, Max Hopkins, Chutong Yang, Hantao Yu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Eigenstripping, Spectral Decay, and Edge-Expansion on Posets.

Jason Gaitonde, Max Hopkins, Tali Kaufman, Shachar Lovett, Ruizhe Zhang

Proceedings of the Approximation, 2022

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

Bounded Memory Active Learning through Enriched Queries.

Max Hopkins, Daniel Kane, Shachar Lovett, Michal Moshkovitz

Proceedings of the Conference on Learning Theory, 2021

2020

High Dimensional Expanders: Random Walks, Pseudorandomness, and Unique Games.

Max Hopkins, Tali Kaufman, Shachar Lovett

Electron. Colloquium Comput. Complex., 2020

The Power of Comparisons for Actively Learning Linear Classifiers.

Max Hopkins, Daniel Kane, Shachar Lovett

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Point Location and Active Learning: Learning Halfspaces Almost Optimally.

Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan

Proceedings of the 61st IEEE Annual Symposium on Foundations of Computer Science, 2020

Noise-tolerant, Reliable Active Classification with Comparison Queries.

Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan

Proceedings of the Conference on Learning Theory, 2020

2019

A Novel CMB Component Separation Method: Hierarchical Generalized Morphological Component Analysis.

Sebastian Wagner-Carena, Max Hopkins, Ana Diaz Rivero, Cora Dvorkin

CoRR, 2019

2018

Simulated Annealing for JPEG Quantization.

Max Hopkins, Michael Mitzenmacher, Sebastian Wagner-Carena

Proceedings of the 2018 Data Compression Conference, 2018