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Max Hopkins
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2020 – today
- 2024
- [j1]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Realizable Learning is All You Need. TheoretiCS 3 (2024) - [i21]Yotam Dikstein, Max Hopkins:
Chernoff Bounds and Reverse Hypercontractivity on HDX. CoRR abs/2404.10961 (2024) - [i20]Max Hopkins, Russell Impagliazzo, Daniel Kane, Sihan Liu, Christopher Ye:
Replicability in High Dimensional Statistics. CoRR abs/2406.02628 (2024) - [i19]Yotam Dikstein, Max Hopkins:
Chernoff Bounds and Reverse Hypercontractivity on HDX. Electron. Colloquium Comput. Complex. TR24 (2024) - 2023
- [c14]Robi Bhattacharjee, Max Hopkins, Akash Kumar, Hantao Yu, Kamalika Chaudhuri:
Robust Empirical Risk Minimization with Tolerance. ALT 2023: 182-203 - [c13]Daniel Beaglehole, Max Hopkins, Daniel Kane, Sihan Liu, Shachar Lovett:
Sampling Equilibria: Fast No-Regret Learning in Structured Games. SODA 2023: 3817-3855 - [c12]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: 520-527 - [i18]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Do PAC-Learners Learn the Marginal Distribution? CoRR abs/2302.06285 (2023) - [i17]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 abs/2303.12921 (2023) - 2022
- [c11]Jason Gaitonde, Max Hopkins, Tali Kaufman, Shachar Lovett, Ruizhe Zhang:
Eigenstripping, Spectral Decay, and Edge-Expansion on Posets. APPROX/RANDOM 2022: 16:1-16:24 - [c10]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Realizable Learning is All You Need. COLT 2022: 3015-3069 - [c9]Max Hopkins, Ting-Chun Lin:
Explicit Lower Bounds Against Ω(n)-Rounds of Sum-of-Squares. FOCS 2022: 662-673 - [c8]Omri Ben-Eliezer, Max Hopkins, Chutong Yang, Hantao Yu:
Active Learning Polynomial Threshold Functions. NeurIPS 2022 - [c7]Mitali Bafna, Max Hopkins, Tali Kaufman, Shachar Lovett:
High Dimensional Expanders: Eigenstripping, Pseudorandomness, and Unique Games. SODA 2022: 1069-1128 - [c6]Mitali Bafna, Max Hopkins, Tali Kaufman, Shachar Lovett:
Hypercontractivity on high dimensional expanders. STOC 2022: 185-194 - [i16]Omri Ben-Eliezer, Max Hopkins, Chutong Yang, Hantao Yu:
Active Learning Polynomial Threshold Functions. CoRR abs/2201.09433 (2022) - [i15]Max Hopkins, Ting-Chun Lin:
Explicit Lower Bounds Against Ω(n)-Rounds of Sum-of-Squares. CoRR abs/2204.11469 (2022) - [i14]Jason Gaitonde, Max Hopkins, Tali Kaufman, Shachar Lovett, Ruizhe Zhang:
Eigenstripping, Spectral Decay, and Edge-Expansion on Posets. CoRR abs/2205.00644 (2022) - [i13]Robi Bhattacharjee, Max Hopkins, Akash Kumar, Hantao Yu, Kamalika Chaudhuri:
Robust Empirical Risk Minimization with Tolerance. CoRR abs/2210.00635 (2022) - [i12]Max Hopkins, Ting-Chun Lin:
Explicit Lower Bounds Against Ω(n)-Rounds of Sum-of-Squares. Electron. Colloquium Comput. Complex. TR22 (2022) - 2021
- [c5]Max Hopkins, Daniel Kane, Shachar Lovett, Michal Moshkovitz:
Bounded Memory Active Learning through Enriched Queries. COLT 2021: 2358-2387 - [i11]Max Hopkins, Daniel Kane, Shachar Lovett, Michal Moshkovitz:
Bounded Memory Active Learning through Enriched Queries. CoRR abs/2102.05047 (2021) - [i10]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Realizable Learning is All You Need. CoRR abs/2111.04746 (2021) - [i9]Mitali Bafna, Max Hopkins, Tali Kaufman, Shachar Lovett:
Hypercontractivity on High Dimensional Expanders: a Local-to-Global Approach for Higher Moments. CoRR abs/2111.09444 (2021) - [i8]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. TR21 (2021) - 2020
- [c4]Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan:
Noise-tolerant, Reliable Active Classification with Comparison Queries. COLT 2020: 1957-2006 - [c3]Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan:
Point Location and Active Learning: Learning Halfspaces Almost Optimally. FOCS 2020: 1034-1044 - [c2]Max Hopkins, Daniel Kane, Shachar Lovett:
The Power of Comparisons for Actively Learning Linear Classifiers. NeurIPS 2020 - [i7]Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan:
Noise-tolerant, Reliable Active Classification with Comparison Queries. CoRR abs/2001.05497 (2020) - [i6]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Point Location and Active Learning: Learning Halfspaces Almost Optimally. CoRR abs/2004.11380 (2020) - [i5]Max Hopkins, Tali Kaufman, Shachar Lovett:
High Dimensional Expanders: Random Walks, Pseudorandomness, and Unique Games. CoRR abs/2011.04658 (2020) - [i4]Max Hopkins, Tali Kaufman, Shachar Lovett:
High Dimensional Expanders: Random Walks, Pseudorandomness, and Unique Games. Electron. Colloquium Comput. Complex. TR20 (2020)
2010 – 2019
- 2019
- [i3]Max Hopkins, Daniel M. Kane, Shachar Lovett:
The Power of Comparisons for Actively Learning Linear Classifiers. CoRR abs/1907.03816 (2019) - [i2]Sebastian Wagner-Carena, Max Hopkins, Ana Diaz Rivero
, Cora Dvorkin:
A Novel CMB Component Separation Method: Hierarchical Generalized Morphological Component Analysis. CoRR abs/1910.08077 (2019) - 2018
- [c1]Max Hopkins, Michael Mitzenmacher, Sebastian Wagner-Carena:
Simulated Annealing for JPEG Quantization. DCC 2018: 412 - 2017
- [i1]Max Hopkins, Michael Mitzenmacher, Sebastian Wagner-Carena:
Simulated Annealing for JPEG Quantization. CoRR abs/1709.00649 (2017)
Coauthor Index
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last updated on 2024-08-10 00:31 CEST by the dblp team
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