Hsu, Daniel J

39 publications

COLT 2023 Intrinsic Dimensionality and Generalization Properties of the R-Norm Inductive Bias Navid Ardeshir, Daniel J. Hsu, Clayton H. Sanford

NeurIPS 2023 Representational Strengths and Limitations of Transformers Clayton Sanford, Daniel J. Hsu, Matus J. Telgarsky

NeurIPS 2022 Masked Prediction: A Parameter Identifiability View Bingbin Liu, Daniel J. Hsu, Pradeep K. Ravikumar, Andrej Risteski

COLT 2022 Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals Daniel J Hsu, Clayton H Sanford, Rocco Servedio, Emmanouil Vasileios Vlatakis-Gkaragkounis

NeurIPS 2021 Bayesian Decision-Making Under Misspecified Priors with Applications to Meta-Learning Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel J. Hsu, Thodoris Lykouris, Miro Dudik, Robert E. Schapire

NeurIPS 2021 Support Vector Machines and Linear Regression Coincide with Very High-Dimensional Features Navid Ardeshir, Clayton Sanford, Daniel J. Hsu

NeurIPS 2020 Ensuring Fairness Beyond the Training Data Debmalya Mandal, Samuel Deng, Suman Jana, Jeannette Wing, Daniel J. Hsu

NeurIPS 2019 On the Number of Variables to Use in Principal Component Regression Ji Xu, Daniel J. Hsu

NeurIPS 2018 Benefits of Over-Parameterization with EM Ji Xu, Daniel J. Hsu, Arian Maleki

NeurIPS 2018 Leveraged Volume Sampling for Linear Regression Michal Derezinski, Manfred K. Warmuth, Daniel J. Hsu

NeurIPS 2018 Overfitting or Perfect Fitting? Risk Bounds for Classification and Regression Rules That Interpolate Mikhail Belkin, Daniel J. Hsu, Partha Mitra

NeurIPS 2017 Linear Regression Without Correspondence Daniel J. Hsu, Kevin Shi, Xiaorui Sun

NeurIPS 2016 Global Analysis of Expectation Maximization for Mixtures of Two Gaussians Ji Xu, Daniel J. Hsu, Arian Maleki

NeurIPS 2016 Search Improves Label for Active Learning Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang

NeurIPS 2015 Efficient and Parsimonious Agnostic Active Learning Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire

NeurIPS 2015 Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path Daniel J. Hsu, Aryeh Kontorovich, Csaba Szepesvari

ALT 2015 Tensor Decompositions for Learning Latent Variable Models (a Survey for ALT) Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky

UAI 2014 A Spectral Algorithm for Learning Class-Based N-Gram Models of Natural Language Karl Stratos, Do-kyum Kim, Michael Collins, Daniel J. Hsu

NeurIPS 2014 Scalable Non-Linear Learning with Adaptive Polynomial Expansions Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus J Telgarsky

NeurIPS 2014 The Large Margin Mechanism for Differentially Private Maximization Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song

COLT 2013 A Tensor Spectral Approach to Learning Mixed Membership Community Models Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade

NeurIPS 2013 Contrastive Learning Using Spectral Methods James Y Zou, Daniel J. Hsu, David C. Parkes, Ryan P. Adams

NeurIPS 2013 When Are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity Anima Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade

NeurIPS 2012 A Spectral Algorithm for Latent Dirichlet Allocation Anima Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-kai Liu

ICML 2012 Convergence Rates for Differentially Private Statistical Estimation Kamalika Chaudhuri, Daniel J. Hsu

NeurIPS 2012 Identifiability and Unmixing of Latent Parse Trees Daniel J. Hsu, Sham M. Kakade, Percy Liang

NeurIPS 2012 Learning Mixtures of Tree Graphical Models Anima Anandkumar, Daniel J. Hsu, Furong Huang, Sham M. Kakade

UAI 2011 Efficient Optimal Learning for Contextual Bandits Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang

NeurIPS 2011 Spectral Methods for Learning Multivariate Latent Tree Structure Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang

NeurIPS 2011 Stochastic Convex Optimization with Bandit Feedback Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin

NeurIPS 2010 Agnostic Active Learning Without Constraints Alina Beygelzimer, Daniel J. Hsu, John Langford, Tong Zhang

UAI 2010 An Online Learning-Based Framework for Tracking Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu

NeurIPS 2009 A Parameter-Free Hedging Algorithm Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu

COLT 2009 A Spectral Algorithm for Learning Hidden Markov Models Daniel J. Hsu, Sham M. Kakade, Tong Zhang

NeurIPS 2009 Multi-Label Prediction via Compressed Sensing Daniel J. Hsu, Sham M. Kakade, John Langford, Tong Zhang

ICML 2008 Hierarchical Sampling for Active Learning Sanjoy Dasgupta, Daniel J. Hsu

NeurIPS 2007 A General Agnostic Active Learning Algorithm Sanjoy Dasgupta, Daniel J. Hsu, Claire Monteleoni

COLT 2007 On-Line Estimation with the Multivariate Gaussian Distribution Sanjoy Dasgupta, Daniel J. Hsu

UAI 2006 A Concentration Theorem for Projections Sanjoy Dasgupta, Daniel J. Hsu, Nakul Verma