Hsu, Daniel

41 publications

NeurIPS 2025 Fast Attention Mechanisms: A Tale of Parallelism Jingwen Liu, Hantao Yu, Clayton Sanford, Alexandr Andoni, Daniel Hsu

COLT 2025 Learning Compositional Functions with Transformers from Easy-to-Hard Data Zixuan Wang, Eshaan Nichani, Alberto Bietti, Alex Damian, Daniel Hsu, Jason D Lee, Denny Wu

AISTATS 2025 Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence Berfin Simsek, Amire Bendjeddou, Daniel Hsu

ALT 2024 Algorithmic Learning Theory 2024: Preface Claire Vernade, Daniel Hsu

NeurIPS 2024 Group-Wise Oracle-Efficient Algorithms for Online Multi-Group Learning Samuel Deng, Daniel Hsu, Jingwen Liu

ICML 2024 Multi-Group Learning for Hierarchical Groups Samuel Deng, Daniel Hsu

COLT 2024 On the Sample Complexity of Parameter Estimation in Logistic Regression with Normal Design Daniel Hsu, Arya Mazumdar

ICML 2024 Transformers Provably Learn Sparse Token Selection While Fully-Connected Nets Cannot Zixuan Wang, Stanley Wei, Daniel Hsu, Jason D. Lee

ICML 2024 Transformers, Parallel Computation, and Logarithmic Depth Clayton Sanford, Daniel Hsu, Matus Telgarsky

AISTATS 2022 Learning Tensor Representations for Meta-Learning Samuel Deng, Yilin Guo, Daniel Hsu, Debmalya Mandal

ICML 2022 Simple and Near-Optimal Algorithms for Hidden Stratification and Multi-Group Learning Christopher J Tosh, Daniel Hsu

JMLR 2022 Unbiased Estimators for Random Design Regression Michał Dereziński, Manfred K. Warmuth, Daniel Hsu

AISTATS 2021 On the Proliferation of Support Vectors in High Dimensions Daniel Hsu, Vidya Muthukumar, Ji Xu

JMLR 2021 Classification vs Regression in Overparameterized Regimes: Does the Loss Function Matter? Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel Hsu, Anant Sahai

JMLR 2021 Contrastive Estimation Reveals Topic Posterior Information to Linear Models Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu

ALT 2021 Contrastive Learning, Multi-View Redundancy, and Linear Models Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu

ICLR 2021 Generalization Bounds via Distillation Daniel Hsu, Ziwei Ji, Matus Telgarsky, Lan Wang

COLT 2021 On the Approximation Power of Two-Layer Networks of Random ReLUs Daniel Hsu, Clayton H Sanford, Rocco Servedio, Emmanouil Vasileios Vlatakis-Gkaragkounis

JMLR 2021 Statistical Query Lower Bounds for Tensor PCA Rishabh Dudeja, Daniel Hsu

AISTATS 2020 Diameter-Based Interactive Structure Discovery Christopher Tosh, Daniel Hsu

ICML 2019 A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization Yucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng

ALT 2019 Attribute-Efficient Learning of Monomials over Highly-Correlated Variables Alexandr Andoni, Rishabh Dudeja, Daniel Hsu, Kiran Vodrahalli

COLT 2019 Conference on Learning Theory 2019: Preface Alina Beygelzimer, Daniel Hsu

AISTATS 2019 Correcting the Bias in Least Squares Regression with Volume-Rescaled Sampling Michal Derezinski, Manfred K. Warmuth, Daniel Hsu

JMLR 2019 Kernel Approximation Methods for Speech Recognition Avner May, Alireza Bagheri Garakani, Zhiyun Lu, Dong Guo, Kuan Liu, Aurélien Bellet, Linxi Fan, Michael Collins, Daniel Hsu, Brian Kingsbury, Michael Picheny, Fei Sha

ICML 2019 Teaching a Black-Box Learner Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis, Xiaojin Zhu

ICLRW 2019 Training Neural Networks for Aspect Extraction Using Descriptive Keywords Only Giannis Karamanolakis, Daniel Hsu, Luis Gravano

COLT 2018 Learning Single-Index Models in Gaussian Space Rishabh Dudeja, Daniel Hsu

COLT 2017 Correspondence Retrieval Alexandr Andoni, Daniel Hsu, Kevin Shi, Xiaorui Sun

ALT 2017 Parameter Identification in Markov Chain Choice Models Arushi Gupta, Daniel Hsu

JMLR 2016 Loss Minimization and Parameter Estimation with Heavy Tails Daniel Hsu, Sivan Sabato

JMLR 2015 Learning Sparse Low-Threshold Linear Classifiers Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel Hsu, Tong Zhang

JMLR 2015 When Are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity Animashree Anandkumar, Daniel Hsu, Majid Janzamin, Sham Kakade

JMLR 2014 A Tensor Approach to Learning Mixed Membership Community Models Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade

ICML 2014 Heavy-Tailed Regression with a Generalized Median-of-Means Daniel Hsu, Sivan Sabato

ICML 2014 Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, Robert Schapire

JMLR 2014 Tensor Decompositions for Learning Latent Variable Models Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M. Kakade, Matus Telgarsky

ICML 2013 Learning Linear Bayesian Networks with Latent Variables Animashree Anandkumar, Daniel Hsu, Adel Javanmard, Sham Kakade

COLT 2012 A Method of Moments for Mixture Models and Hidden Markov Models Animashree Anandkumar, Daniel Hsu, Sham M. Kakade

COLT 2012 Random Design Analysis of Ridge Regression Daniel Hsu, Sham M. Kakade, Tong Zhang

COLT 2011 Sample Complexity Bounds for Differentially Private Learning Kamalika Chaudhuri, Daniel Hsu