Max Hopkins

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 36

Top Topics

Simulated Annealing

Quantization Table

Active Learning

High Dimensional

Top Venues

Electron. Colloquium Comput. Complex.

Publications

Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan
Realizable Learning is All You Need. TheoretiCS 3 (2024)
Yotam Dikstein, Max Hopkins
Chernoff Bounds and Reverse Hypercontractivity on HDX. Electron. Colloquium Comput. Complex. (2024)
Yotam Dikstein, Max Hopkins
Chernoff Bounds and Reverse Hypercontractivity on HDX. CoRR (2024)
Max Hopkins, Russell Impagliazzo, Daniel Kane, Sihan Liu, Christopher Ye
Replicability in High Dimensional Statistics. CoRR (2024)
Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan
Do PAC-Learners Learn the Marginal Distribution? CoRR (2023)
Daniel Beaglehole, Max Hopkins, Daniel Kane, Sihan Liu, Shachar Lovett
Sampling Equilibria: Fast No-Regret Learning in Structured Games. SODA (2023)
Robi Bhattacharjee, Max Hopkins, Akash Kumar, Hantao Yu, Kamalika Chaudhuri
Robust Empirical Risk Minimization with Tolerance. ALT (2023)
Mark Bun, Marco Gaboardi, Max Hopkins, Russell Impagliazzo, Rex Lei, Toniann Pitassi, Satchit Sivakumar, Jessica Sorrell
Stability is Stable: Connections between Replicability, Privacy, and Adaptive Generalization. CoRR (2023)
Mark Bun, Marco Gaboardi, Max Hopkins, Russell Impagliazzo, Rex Lei, Toniann Pitassi, Satchit Sivakumar, Jessica Sorrell
Stability Is Stable: Connections between Replicability, Privacy, and Adaptive Generalization. STOC (2023)
Omri Ben-Eliezer, Max Hopkins, Chutong Yang, Hantao Yu
Active Learning Polynomial Threshold Functions. CoRR (2022)
Max Hopkins, Ting-Chun Lin
Explicit Lower Bounds Against Ω(n)-Rounds of Sum-of-Squares. FOCS (2022)
Jason Gaitonde, Max Hopkins, Tali Kaufman, Shachar Lovett, Ruizhe Zhang
Eigenstripping, Spectral Decay, and Edge-Expansion on Posets. CoRR (2022)
Max Hopkins, Ting-Chun Lin
Explicit Lower Bounds Against Ω(n)-Rounds of Sum-of-Squares. CoRR (2022)
Omri Ben-Eliezer, Max Hopkins, Chutong Yang, Hantao Yu
Active Learning Polynomial Threshold Functions. NeurIPS (2022)
Robi Bhattacharjee, Max Hopkins, Akash Kumar, Hantao Yu, Kamalika Chaudhuri
Robust Empirical Risk Minimization with Tolerance. CoRR (2022)
Mitali Bafna, Max Hopkins, Tali Kaufman, Shachar Lovett
High Dimensional Expanders: Eigenstripping, Pseudorandomness, and Unique Games. SODA (2022)
Mitali Bafna, Max Hopkins, Tali Kaufman, Shachar Lovett
Hypercontractivity on high dimensional expanders. STOC (2022)
Max Hopkins, Ting-Chun Lin
Explicit Lower Bounds Against Ω(n)-Rounds of Sum-of-Squares. Electron. Colloquium Comput. Complex. (2022)
Jason Gaitonde, Max Hopkins, Tali Kaufman, Shachar Lovett, Ruizhe Zhang
Eigenstripping, Spectral Decay, and Edge-Expansion on Posets. APPROX/RANDOM (2022)
Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan
Realizable Learning is All You Need. COLT (2022)
Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan
Realizable Learning is All You Need. CoRR (2021)
Max Hopkins, Daniel Kane, Shachar Lovett, Michal Moshkovitz
Bounded Memory Active Learning through Enriched Queries. COLT (2021)
Max Hopkins, Daniel Kane, Shachar Lovett, Michal Moshkovitz
Bounded Memory Active Learning through Enriched Queries. CoRR (2021)
Mitali Bafna, Max Hopkins, Tali Kaufman, Shachar Lovett
Hypercontractivity on High Dimensional Expanders: a Local-to-Global Approach for Higher Moments. Electron. Colloquium Comput. Complex. 28 (2021)
Mitali Bafna, Max Hopkins, Tali Kaufman, Shachar Lovett
Hypercontractivity on High Dimensional Expanders: a Local-to-Global Approach for Higher Moments. CoRR (2021)
Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan
Point Location and Active Learning: Learning Halfspaces Almost Optimally. FOCS (2020)
Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan
Point Location and Active Learning: Learning Halfspaces Almost Optimally. CoRR (2020)
Max Hopkins, Daniel Kane, Shachar Lovett
The Power of Comparisons for Actively Learning Linear Classifiers. NeurIPS (2020)
Max Hopkins, Tali Kaufman, Shachar Lovett
High Dimensional Expanders: Random Walks, Pseudorandomness, and Unique Games. Electron. Colloquium Comput. Complex. 27 (2020)
Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan
Noise-tolerant, Reliable Active Classification with Comparison Queries. COLT (2020)
Max Hopkins, Tali Kaufman, Shachar Lovett
High Dimensional Expanders: Random Walks, Pseudorandomness, and Unique Games. CoRR (2020)
Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan
Noise-tolerant, Reliable Active Classification with Comparison Queries. CoRR (2020)
Max Hopkins, Daniel M. Kane, Shachar Lovett
The Power of Comparisons for Actively Learning Linear Classifiers. CoRR (2019)
Sebastian Wagner-Carena, Max Hopkins, Ana Diaz Rivero, Cora Dvorkin
A Novel CMB Component Separation Method: Hierarchical Generalized Morphological Component Analysis. CoRR (2019)
Max Hopkins, Michael Mitzenmacher, Sebastian Wagner-Carena
Simulated Annealing for JPEG Quantization. DCC (2018)
Max Hopkins, Michael Mitzenmacher, Sebastian Wagner-Carena
Simulated Annealing for JPEG Quantization. CoRR (2017)