a CAPpella: programming by demonstration of context-aware applications. - Anind K. Dey; Raffay Hamid; Chris Beckmann; Ian Li; Daniel Hsu 001 (CHI, 2004)

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

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

A general agnostic active learning algorithm. - Sanjoy Dasgupta; Daniel J. Hsu; Claire Monteleoni (NIPS, 2007)

Hierarchical sampling for active learning. - Sanjoy Dasgupta; Daniel J. Hsu (ICML, 2008)

A new Hedging algorithm and its application to inferring latent random variables - Yoav Freund; Daniel J. Hsu (CoRR, 2008)

A Parameter-free Hedging Algorithm. - Kamalika Chaudhuri; Yoav Freund; Daniel J. Hsu (NIPS, 2009)

Multi-Label Prediction via Compressed Sensing. - Daniel J. Hsu; Sham M. Kakade; John Langford 001; Tong Zhang 001 (NIPS, 2009)

Tracking using explanation-based modeling - Kamalika Chaudhuri; Yoav Freund; Daniel J. Hsu (CoRR, 2009)

Agnostic Active Learning Without Constraints. - Alina Beygelzimer; Daniel J. Hsu; John Langford 001; Tong Zhang 001 (NIPS, 2010)

An Online Learning-based Framework for Tracking. - Kamalika Chaudhuri; Yoav Freund; Daniel J. Hsu (UAI, 2010)

Robust Matrix Decomposition with Outliers - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (CoRR, 2010)

Algorithms for active learning. - Daniel J. Hsu (2010)

Spectral Methods for Learning Multivariate Latent Tree Structure. - Animashree Anandkumar; Kamalika Chaudhuri; Daniel J. Hsu; Sham M. Kakade; Le Song; Tong Zhang 001 (NIPS, 2011)

Efficient Optimal Learning for Contextual Bandits. - Miroslav Dudík; Daniel J. Hsu; Satyen Kale; Nikos Karampatziakis; John Langford 001; Lev Reyzin; Tong Zhang 001 (UAI, 2011)

Dimension-free tail inequalities for sums of random matrices. - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (CoRR, 2011)

Parallel Online Learning - Daniel J. Hsu; Nikos Karampatziakis; John Langford 001; Alexander J. Smola (CoRR, 2011)

An Analysis of Random Design Linear Regression - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (CoRR, 2011)

A tail inequality for quadratic forms of subgaussian random vectors - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (CoRR, 2011)

Sample Complexity Bounds for Differentially Private Learning. - Kamalika Chaudhuri; Daniel J. Hsu (COLT, 2011)

Robust Matrix Decomposition With Sparse Corruptions. - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (IEEE Trans. Inf. Theory, 2011)

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

Learning Mixtures of Tree Graphical Models. - Animashree Anandkumar; Daniel J. Hsu; Furong Huang; Sham M. Kakade (NIPS, 2012)

Identifiability and Unmixing of Latent Parse Trees. - Daniel J. Hsu; Sham M. Kakade; Percy Liang (NIPS, 2012)

Learning High-Dimensional Mixtures of Graphical Models - Animashree Anandkumar; Daniel J. Hsu; Sham M. Kakade (CoRR, 2012)

Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation - Animashree Anandkumar; Dean P. Foster; Daniel J. Hsu; Sham M. Kakade; Yi-Kai Liu 001 (CoRR, 2012)

Learning Gaussian Mixture Models: Moment Methods and Spectral Decompositions - Daniel J. Hsu; Sham M. Kakade (CoRR, 2012)

Analysis of a randomized approximation scheme for matrix multiplication - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (CoRR, 2012)

A spectral algorithm for learning Hidden Markov Models. - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (J. Comput. Syst. Sci., 2012)

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

A Tensor Spectral Approach to Learning Mixed Membership Community Models. - Animashree Anandkumar; Rong Ge 001; Daniel J. Hsu; Sham M. Kakade (COLT, 2013)

Learning Linear Bayesian Networks with Latent Variables. - Animashree Anandkumar; Daniel J. Hsu; Adel Javanmard; Sham M. Kakade (ICML (1), 2013)

Learning mixtures of spherical gaussians: moment methods and spectral decompositions. - Daniel J. Hsu; Sham M. Kakade (ITCS, 2013)

Contrastive Learning Using Spectral Methods. - James Y. Zou; Daniel J. Hsu; David C. Parkes; Ryan Prescott Adams (NIPS, 2013)

Approximate loss minimization with heavy tails. - Daniel J. Hsu; Sivan Sabato (CoRR, 2013)

Stochastic Convex Optimization with Bandit Feedback. - Alekh Agarwal; Dean P. Foster; Daniel J. Hsu; Sham M. Kakade; Alexander Rakhlin (SIAM J. Optim., 2013)

Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. - Alekh Agarwal; Daniel J. Hsu; Satyen Kale; John Langford 001; Lihong Li 001; Robert E. Schapire (ICML, 2014)

Heavy-tailed regression with a generalized median-of-means. - Daniel J. Hsu; Sivan Sabato (ICML, 2014)

Scalable Non-linear Learning with Adaptive Polynomial Expansions. - Alekh Agarwal; Alina Beygelzimer; Daniel J. Hsu; John Langford 001; Matus Telgarsky (NIPS, 2014)

The Large Margin Mechanism for Differentially Private Maximization. - Kamalika Chaudhuri; Daniel J. Hsu; Shuang Song 001 (NIPS, 2014)

A Spectral Algorithm for Learning Class-Based n-gram Models of Natural Language. - Karl Stratos; Do-kyum Kim; Michael Collins 001; Daniel J. Hsu (UAI, 2014)

Scalable Nonlinear Learning with Adaptive Polynomial Expansions. - Alekh Agarwal; Alina Beygelzimer; Daniel J. Hsu; John Langford 001; Matus Telgarsky (CoRR, 2014)

Weighted sampling of outer products. - Daniel J. Hsu (CoRR, 2014)

Random Design Analysis of Ridge Regression. - Daniel J. Hsu; Sham M. Kakade; Tong Zhang 001 (Found. Comput. Math., 2014)

A tensor approach to learning mixed membership community models. - Animashree Anandkumar; Rong Ge 001; Daniel J. Hsu; Sham M. Kakade (J. Mach. Learn. Res., 2014)

Tensor decompositions for learning latent variable models. - Animashree Anandkumar; Rong Ge 001; Daniel J. Hsu; Sham M. Kakade; Matus Telgarsky (J. Mach. Learn. Res., 2014)

Model-based Word Embeddings from Decompositions of Count Matrices. - Karl Stratos; Michael Collins 001; Daniel J. Hsu (ACL (1), 2015)

Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT). - Anima Anandkumar; Rong Ge 001; Daniel J. Hsu; Sham M. Kakade; Matus Telgarsky (ALT, 2015)

Sunlight: Fine-grained Targeting Detection at Scale with Statistical Confidence. - Mathias Lécuyer; Riley Spahn; Yannis Spiliopolous; Augustin Chaintreau; Roxana Geambasu; Daniel J. Hsu (CCS, 2015)

Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path. - Daniel J. Hsu; Aryeh Kontorovich; Csaba Szepesvári (NIPS, 2015)

Efficient and Parsimonious Agnostic Active Learning. - Tzu-Kuo Huang; Alekh Agarwal; Daniel J. Hsu; John Langford 001; Robert E. Schapire (NIPS, 2015)

Method of moments learning for left-to-right Hidden Markov models. - Y. Cem Sübakan; Johannes Traa; Paris Smaragdis; Daniel J. Hsu (WASPAA, 2015)

A Spectral Algorithm for Latent Dirichlet Allocation. - Anima Anandkumar; Dean P. Foster; Daniel J. Hsu; Sham M. Kakade; Yi-Kai Liu 001 (Algorithmica, 2015)

Discovering Unwarranted Associations in Data-Driven Applications with the FairTest Testing Toolkit. - Florian Tramèr; Vaggelis Atlidakis; Roxana Geambasu; Daniel J. Hsu; Jean-Pierre Hubaux; Mathias Humbert; Ari Juels; Huang Lin (CoRR, 2015)

When are overcomplete topic models identifiable? uniqueness of tensor tucker decompositions with structured sparsity. - Animashree Anandkumar; Daniel J. Hsu; Majid Janzamin; Sham M. Kakade (J. Mach. Learn. Res., 2015)

Learning sparse low-threshold linear classifiers. - Sivan Sabato; Shai Shalev-Shwartz; Nathan Srebro; Daniel J. Hsu; Tong Zhang 001 (J. Mach. Learn. Res., 2015)

Successive Rank-One Approximations for Nearly Orthogonally Decomposable Symmetric Tensors. - Cun Mu; Daniel J. Hsu; Donald Goldfarb (SIAM J. Matrix Anal. Appl., 2015)

Compact kernel models for acoustic modeling via random feature selection. - Avner May; Michael Collins 001; Daniel J. Hsu; Brian Kingsbury (ICASSP, 2016)

Search Improves Label for Active Learning. - Alina Beygelzimer; Daniel J. Hsu; John Langford 001; Chicheng Zhang (NIPS, 2016)

Global Analysis of Expectation Maximization for Mixtures of Two Gaussians. - Ji Xu 003; Daniel J. Hsu; Arian Maleki (NIPS, 2016)

Greedy bi-criteria approximations for k-medians and k-means. - Daniel J. Hsu; Matus Telgarsky (CoRR, 2016)

AI's 10 to Watch. - Haris Aziz 001; Elias Bareinboim; Yejin Choi 001; Daniel J. Hsu; Shivaram Kalyanakrishnan; Reshef Meir; Suchi Saria; Gerardo I. Simari; Lirong Xia; William Yeoh 001 (IEEE Intell. Syst., 2016)

Loss Minimization and Parameter Estimation with Heavy Tails. - Daniel J. Hsu; Sivan Sabato (J. Mach. Learn. Res., 2016)

Unsupervised Part-Of-Speech Tagging with Anchor Hidden Markov Models. - Karl Stratos; Michael Collins 001; Daniel J. Hsu (Trans. Assoc. Comput. Linguistics, 2016)

Correspondence retrieval. - Alexandr Andoni; Daniel J. Hsu; Kevin Shi; Xiaorui Sun (COLT, 2017)

FairTest: Discovering Unwarranted Associations in Data-Driven Applications. - Florian Tramèr; Vaggelis Atlidakis; Roxana Geambasu; Daniel J. Hsu; Jean-Pierre Hubaux; Mathias Humbert; Ari Juels; Huang Lin (EuroS&P, 2017)

Linear regression without correspondence. - Daniel J. Hsu; Kevin Shi; Xiaorui Sun (NIPS, 2017)

Coding with asymmetric prior knowledge. - Alexandr Andoni; Javad Ghaderi; Daniel J. Hsu; Dan Rubenstein; Omri Weinstein (CoRR, 2017)

Mixing time estimation in reversible Markov chains from a single sample path. - Daniel J. Hsu; Aryeh Kontorovich; David A. Levin; Yuval Peres; Csaba Szepesvári (CoRR, 2017)

Greedy Approaches to Symmetric Orthogonal Tensor Decomposition. - Cun Mu; Daniel J. Hsu; Donald Goldfarb (SIAM J. Matrix Anal. Appl., 2017)

Learning Single-Index Models in Gaussian Space. - Rishabh Dudeja; Daniel Hsu 001 (COLT, 2018)

Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate. - Mikhail Belkin; Daniel J. Hsu; Partha Mitra (NeurIPS, 2018)

Leveraged volume sampling for linear regression. - Michal Derezinski; Manfred K. Warmuth; Daniel J. Hsu (NeurIPS, 2018)

Benefits of over-parameterization with EM. - Ji Xu 003; Daniel J. Hsu; Arian Maleki (NeurIPS, 2018)

Non-Gaussian information from weak lensing data via deep learning. - Arushi Gupta; José Manuel Zorrilla Matilla; Daniel Hsu 001; Zoltán Haiman (CoRR, 2018)

On the Connection between Differential Privacy and Adversarial Robustness in Machine Learning. - Mathias Lécuyer; Vaggelis Atlidakis; Roxana Geambasu; Daniel Hsu 001; Suman Jana (CoRR, 2018)

Tail bounds for volume sampled linear regression. - Michal Derezinski; Manfred K. Warmuth; Daniel Hsu 001 (CoRR, 2018)

Reconciling modern machine learning and the bias-variance trade-off. - Mikhail Belkin; Daniel Hsu 001; Siyuan Ma; Soumik Mandal (CoRR, 2018)

Discovering foodborne illness in online restaurant reviews. - Thomas Effland; Anna Lawson; Sharon Balter; Katelynn Devinney; Vasudha Reddy; HaeNa Waechter; Luis Gravano; Daniel Hsu 001 (J. Am. Medical Informatics Assoc., 2018)

Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health. - Giannis Karamanolakis; Daniel Hsu 001; Luis Gravano (W-NUT@EMNLP, 2019)

Correcting the bias in least squares regression with volume-rescaled sampling. - Michal Derezinski; Manfred K. Warmuth; Daniel Hsu 001 (AISTATS, 2019)

Attribute-efficient learning of monomials over highly-correlated variables. - Alexandr Andoni; Rishabh Dudeja; Daniel Hsu 001; Kiran Vodrahalli (ALT, 2019)

Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training. - Giannis Karamanolakis; Daniel Hsu 001; Luis Gravano (EMNLP/IJCNLP (1), 2019)

A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization. - Yucheng Chen; Matus Telgarsky; Chao Zhang 014; Bolton Bailey; Daniel Hsu 001; Jian Peng 001 (ICML, 2019)

Teaching a black-box learner. - Sanjoy Dasgupta; Daniel Hsu 001; Stefanos Poulis; Xiaojin Zhu 001 (ICML, 2019)

On the number of variables to use in principal component regression. - Ji Xu 003; Daniel J. Hsu (NeurIPS, 2019)

Certified Robustness to Adversarial Examples with Differential Privacy. - Mathias Lécuyer; Vaggelis Atlidakis; Roxana Geambasu; Daniel Hsu 001; Suman Jana (IEEE Symposium on Security and Privacy, 2019)

How many variables should be entered in a principal component regression equation? - Ji Xu 003; Daniel Hsu 001 (CoRR, 2019)

Diameter-based Interactive Structure Search. - Christopher Tosh; Daniel Hsu 001 (CoRR, 2019)

A cryptographic approach to black box adversarial machine learning. - Kevin Shi; Daniel Hsu 001; Allison Bishop (CoRR, 2019)

Kernel Approximation Methods for Speech Recognition. - Avner May; Alireza Bagheri Garakani; Zhiyun Lu; Dong Guo; Kuan Liu; Aurélien Bellet; Linxi Fan; Michael Collins 001; Daniel Hsu 001; Brian Kingsbury; Michael Picheny; Fei Sha (J. Mach. Learn. Res., 2019)

Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform. - Mathias Lécuyer; Riley Spahn; Kiran Vodrahalli; Roxana Geambasu; Daniel Hsu 001 (ACM SIGOPS Oper. Syst. Rev., 2019)

Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only. - Ziyi Liu; Giannis Karamanolakis; Daniel Hsu 001; Luis Gravano (LOUHI@EMNLP, 2020)

Diameter-based Interactive Structure Discovery. - Christopher Tosh; Daniel Hsu 001 (AISTATS, 2020)

Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher. - Giannis Karamanolakis; Daniel Hsu 001; Luis Gravano (EMNLP (Findings), 2020)

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

Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics. - Bo Cowgill; Fabrizio Dell'Acqua; Samuel Deng; Daniel Hsu 001; Nakul Verma; Augustin Chaintreau (EC, 2020)

Two Models of Double Descent for Weak Features. - Mikhail Belkin; Daniel Hsu 001; Ji Xu 003 (SIAM J. Math. Data Sci., 2020)

Parameter identification in Markov chain choice models. - Arushi Gupta; Daniel Hsu 001 (Theor. Comput. Sci., 2020)

Quantifying the Effects of COVID-19 on Restaurant Reviews. - Ivy Cao; Zizhou Liu; Giannis Karamanolakis; Daniel Hsu 001; Luis Gravano (SocialNLP@NAACL, 2021)

On the proliferation of support vectors in high dimensions. - Daniel Hsu 001; Vidya Muthukumar; Ji Xu 003 (AISTATS, 2021)

Contrastive learning, multi-view redundancy, and linear models. - Christopher Tosh; Akshay Krishnamurthy; Daniel Hsu 001 (ALT, 2021)

On the Approximation Power of Two-Layer Networks of Random ReLUs. - Daniel Hsu 001; Clayton Sanford; Rocco A. Servedio; Emmanouil V. Vlatakis-Gkaragkounis (COLT, 2021)

Generalization bounds via distillation. - Daniel Hsu 001; Ziwei Ji; Matus Telgarsky; Lan Wang (ICLR, 2021)

Support vector machines and linear regression coincide with very high-dimensional features. - Navid Ardeshir; Clayton Sanford; Daniel J. Hsu (NeurIPS, 2021)

Bayesian decision-making under misspecified priors with applications to meta-learning. - Max Simchowitz; Christopher Tosh; Akshay Krishnamurthy; Daniel J. Hsu; Thodoris Lykouris; Miroslav Dudík; Robert E. Schapire (NeurIPS, 2021)

Statistical Query Lower Bounds for Tensor PCA. - Rishabh Dudeja; Daniel Hsu 001 (J. Mach. Learn. Res., 2021)

Classification vs regression in overparameterized regimes: Does the loss function matter? - Vidya Muthukumar; Adhyyan Narang; Vignesh Subramanian; Mikhail Belkin; Daniel Hsu 001; Anant Sahai (J. Mach. Learn. Res., 2021)

Contrastive Estimation Reveals Topic Posterior Information to Linear Models. - Christopher Tosh; Akshay Krishnamurthy; Daniel Hsu 001 (J. Mach. Learn. Res., 2021)

Consistent Risk Estimation in Moderately High-Dimensional Linear Regression. - Ji Xu 003; Arian Maleki; Kamiar Rahnama Rad; Daniel Hsu 001 (IEEE Trans. Inf. Theory, 2021)

Learning Tensor Representations for Meta-Learning. - Samuel Deng; Yilin Guo; Daniel Hsu 001; Debmalya Mandal (AISTATS, 2022)

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

Simple and near-optimal algorithms for hidden stratification and multi-group learning. - Christopher J. Tosh; Daniel Hsu 001 (ICML, 2022)

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

Masked prediction tasks: a parameter identifiability view. - Bingbin Liu; Daniel Hsu 001; Pradeep Ravikumar; Andrej Risteski (CoRR, 2022)

Statistical-Computational Trade-offs in Tensor PCA and Related Problems via Communication Complexity. - Rishabh Dudeja; Daniel Hsu 001 (CoRR, 2022)

Unbiased estimators for random design regression. - Michal Derezinski; Manfred K. Warmuth; Daniel Hsu 001 (J. Mach. Learn. Res., 2022)

Intrinsic dimensionality and generalization properties of the R-norm inductive bias. - Navid Ardeshir; Daniel J. Hsu; Clayton Hendrick Sanford (COLT, 2023)

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

On the sample complexity of estimation in logistic regression. - Daniel Hsu 001; Arya Mazumdar (CoRR, 2023)

Efficient Estimation of the Central Mean Subspace via Smoothed Gradient Outer Products. - Gan Yuan; Mingyue Xu; Samory Kpotufe; Daniel Hsu 001 (CoRR, 2023)

On the sample complexity of parameter estimation in logistic regression with normal design. - Daniel Hsu 001; Arya Mazumdar (COLT, 2024)

Distribution-Specific Auditing for Subgroup Fairness. - Daniel Hsu 001; Jizhou Huang; Brendan Juba (FORC, 2024)

Multi-group Learning for Hierarchical Groups. - Samuel Deng; Daniel Hsu 001 (ICML, 2024)

Transformers, parallel computation, and logarithmic depth. - Clayton Sanford; Daniel Hsu 001; Matus Telgarsky (ICML, 2024)

Transformers Provably Learn Sparse Token Selection While Fully-Connected Nets Cannot. - Zixuan Wang; Stanley Wei; Daniel Hsu 001; Jason D. Lee (ICML, 2024)

Group-wise oracle-efficient algorithms for online multi-group learning. - Samuel Deng; Jingwen Liu; Daniel J. Hsu (NeurIPS, 2024)

Polynomial time auditing of statistical subgroup fairness for Gaussian data. - Daniel Hsu 001; Jizhou Huang; Brendan Juba (CoRR, 2024)

Seasonality Patterns in 311-Reported Foodborne Illness Cases and Machine Learning-Identified Indications of Foodborne Illnesses from Yelp Reviews, New York City, 2022-2023. - Eden Shaveet; Crystal Su; Daniel Hsu 001; Luis Gravano (CoRR, 2024)

One-layer transformers fail to solve the induction heads task. - Clayton Sanford; Daniel Hsu 001; Matus Telgarsky (CoRR, 2024)

Interactive Machine Teaching by Labeling Rules and Instances. - Giannis Karamanolakis; Daniel Hsu 001; Luis Gravano (CoRR, 2024)

Interactive Machine Teaching by Labeling Rules and Instances. - Giannis Karamanolakis; Daniel Hsu 001; Luis Gravaano (Trans. Assoc. Comput. Linguistics, 2024)

Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence. - Berfin Simsek; Amire Bendjeddou; Daniel Hsu 001 (AISTATS, 2025)

Learning Compositional Functions with Transformers from Easy-to-Hard Data. - Zixuan Wang; Eshaan Nichani; Alberto Bietti; Alex Damian; Daniel Hsu 001; Jason D. Lee; Denny Wu (COLT, 2025)

Survey on Algorithms for multi-index models. - Joan Bruna; Daniel Hsu 001 (CoRR, 2025)