Daniel Hsu

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All published works

Action	Title	Year	Authors
+ PDF Chat	The Piranha Problem: Large Effects Swimming in a Small Pond	2024	Christopher Tosh Philip Greengard Ben Goodrich Andrew Gelman Aki Vehtari Daniel Hsu
+ PDF Chat	Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence	2024	Berfin Şimşek Ahmed Bendjeddou Daniel Hsu
+ PDF Chat	Interactive Machine Teaching by Labeling Rules and Instances	2024	Giannis Karamanolakis Daniel Hsu Luis Gravano
+ PDF Chat	One-layer transformers fail to solve the induction heads task	2024	Clayton Sanford Daniel Hsu Matus Telgarsky
+ PDF Chat	Transformers Provably Learn Sparse Token Selection While Fully-Connected Nets Cannot	2024	Zixuan Wang Stanley Wei Daniel Hsu Jason D. Lee
+ PDF Chat	Group-wise oracle-efficient algorithms for online multi-group learning	2024	Samuel Deng Daniel Hsu Jingwen Liu
+ PDF Chat	Seasonality Patterns in 311-Reported Foodborne Illness Cases and Machine Learning-Identified Indications of Foodborne Illnesses from Yelp Reviews, New York City, 2022-2023	2024	Eden Shaveet Crystal Su Daniel Hsu Luis Gravano
+ PDF Chat	Transformers, parallel computation, and logarithmic depth	2024	Clayton Sanford Daniel Hsu Matus Telgarsky
+ PDF Chat	Statistical-computational trade-offs in tensor PCA and related problems via communication complexity	2024	Rishabh Dudeja Daniel Hsu
+ PDF Chat	Multi-group Learning for Hierarchical Groups	2024	Samuel Deng Daniel Hsu
+	Group conditional validity via multi-group learning	2023	Samuel Deng Navid Ardeshir Daniel Hsu
+	Representational Strengths and Limitations of Transformers	2023	Clayton Sanford Daniel Hsu Matus Telgarsky
+	On the sample complexity of estimation in logistic regression	2023	Daniel Hsu Arya Mazumdar
+ PDF Chat	On the proliferation of support vectors in high dimensions*	2022	Daniel Hsu Vidya Muthukumar Ji Xu
+	Masked prediction tasks: a parameter identifiability view	2022	Bingbin Liu Daniel Hsu Pradeep Ravikumar Andrej Risteski
+	Statistical-Computational Trade-offs in Tensor PCA and Related Problems via Communication Complexity	2022	Rishabh Dudeja Daniel Hsu
+	Learning Tensor Representations for Meta-Learning	2022	Samuel Deng Yilin Guo Daniel Hsu Debmalya Mandal
+	Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals	2022	Daniel Hsu Clayton Sanford Rocco A. Servedio Emmanouil-Vasileios Vlatakis-Gkaragkounis
+	Intrinsic dimensionality and generalization properties of the $\mathcal{R}$-norm inductive bias	2022	Clayton Sanford Navid Ardeshir Daniel Hsu
+	Bayesian decision-making under misspecified priors with applications to meta-learning	2021	Max Simchowitz Christopher Tosh Akshay Krishnamurthy Daniel Hsu Thodoris Lykouris Miroslav Dudı́k Robert E. Schapire
+	Support vector machines and linear regression coincide with very high-dimensional features	2021	Navid Ardeshir Clayton Sanford Daniel Hsu
+	Consistent Risk Estimation in Moderately High-Dimensional Linear Regression	2021	Ji Xu Arian Maleki Kamiar Rahnama Rad Daniel Hsu
+	Support vector machines and linear regression coincide with very high-dimensional features	2021	Navid Ardeshir Clayton Sanford Daniel Hsu
+	Generalization bounds via distillation	2021	Daniel Hsu Ziwei Ji Matus Telgarsky Lan Wang
+	On the Approximation Power of Two-Layer Networks of Random ReLUs	2021	Daniel Hsu Clayton Sanford Rocco A. Servedio Emmanouil-Vasileios Vlatakis-Gkaragkounis
+	Generalization bounds via distillation	2021	Daniel Hsu Ziwei Ji Matus Telgarsky Lan Wang
+	On the Approximation Power of Two-Layer Networks of Random ReLUs	2021	Daniel Hsu Clayton Sanford Rocco A. Servedio Emmanouil-Vasileios Vlatakis-Gkaragkounis
+	Bayesian decision-making under misspecified priors with applications to meta-learning	2021	Max Simchowitz Christopher Tosh Akshay Krishnamurthy Daniel Hsu Thodoris Lykouris Miroslav Dudı́k Robert E. Schapire
+	Simple and near-optimal algorithms for hidden stratification and multi-group learning	2021	Christopher Tosh Daniel Hsu
+	Support vector machines and linear regression coincide with very high-dimensional features	2021	Navid Ardeshir Clayton Sanford Daniel Hsu
+	The piranha problem: Large effects swimming in a small pond	2021	Christopher Tosh Philip Greengard Ben Goodrich Andrew Gelman Aki Vehtari Daniel Hsu
+	Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics	2020	Bo Cowgill Fabrizio Dell’Acqua Samuel Deng Daniel Hsu Nakul Verma Augustin Chaintreau
+ PDF Chat	Interpreting deep learning models for weak lensing	2020	José Manuel Zorrilla Matilla M. Sharma Daniel Hsu Zoltán Haiman
+	Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher	2020	Giannis Karamanolakis Daniel Hsu Luis Gravano
+	Contrastive learning, multi-view redundancy, and linear models	2020	Christopher Tosh Akshay Krishnamurthy Daniel Hsu
+	Statistical Query Lower Bounds for Tensor PCA	2020	Rishabh Dudeja Daniel Hsu
+ PDF Chat	Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics	2020	Bo Cowgill Fabrizio Dell’Acqua Samuel Deng Daniel Hsu Nakul Verma Augustin Chaintreau
+	Classification vs regression in overparameterized regimes: Does the loss function matter?	2020	V. Sai Muthukumar Adhyyan Narang Vignesh Subramanian Mikhail Belkin Daniel Hsu Anant Sahai
+	Contrastive estimation reveals topic posterior information to linear models	2020	Christopher Tosh Akshay Krishnamurthy Daniel Hsu
+	Ensuring Fairness Beyond the Training Data	2020	Debmalya Mandal Samuel Deng Suman Jana Jeannette M. Wing Daniel Hsu
+	On the proliferation of support vectors in high dimensions	2020	Daniel Hsu Vidya Muthukumar Ji Xu
+	Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only	2020	Ziyi Liu Giannis Karamanolakis Daniel Hsu Luis Gravano
+	Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only	2020	Ziyi Liu Giannis Karamanolakis Daniel Hsu Luis Gravano
+	Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher	2020	Giannis Karamanolakis Daniel Hsu Luis Gravano
+	Ensuring Fairness Beyond the Training Data	2020	Debmalya Mandal Samuel Deng Suman Jana Jeannette M. Wing Daniel Hsu
+ PDF Chat	Two Models of Double Descent for Weak Features	2020	Mikhail A. Belkin Daniel Hsu Ji Xu
+ PDF Chat	Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics	2020	Bo Cowgill Fabrizio Dell’Acqua Sam Deng Daniel Hsu Nakul Verma Augustin Chaintreau
+	Statistical Query Lower Bounds for Tensor PCA	2020	Rishabh Dudeja Daniel Hsu
+	Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics	2020	Bo Cowgill Fabrizio Dell’Acqua Samuel Deng Daniel Hsu Nakul Verma Augustin Chaintreau
+	Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher	2020	Giannis Karamanolakis Daniel Hsu Luis Gravano
+	Contrastive learning, multi-view redundancy, and linear models	2020	Christopher Tosh Akshay Krishnamurthy Daniel Hsu
+	Classification vs regression in overparameterized regimes: Does the loss function matter?	2020	V. Sai Muthukumar Adhyyan Narang Vignesh Subramanian Mikhail Belkin Daniel Hsu Anant Sahai
+	A New Framework for Query Efficient Active Imitation Learning.	2019	Daniel Hsu
+ PDF Chat	Parameter identification in Markov chain choice models	2019	Arushi Gupta Daniel Hsu
+	Privacy accounting and quality control in the sage differentially private ML platform	2019	Mathias Lécuyer Riley Spahn Kiran Vodrahalli Roxana Geambasu Daniel Hsu
+	Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health	2019	Giannis Karamanolakis Daniel Hsu Luis Gravano
+ PDF Chat	Weak lensing cosmology with convolutional neural networks on noisy data	2019	Dezső Ribli Bálint Pataki José Manuel Zorrilla Matilla Daniel Hsu Zoltán Haiman István Csabai
+	Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training	2019	Giannis Karamanolakis Daniel Hsu Luis Gravano
+ PDF Chat	Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform	2019	Mathias Lécuyer Riley Spahn Kiran Vodrahalli Roxana Geambasu Daniel Hsu
+	Reconciling modern machine-learning practice and the classical bias–variance trade-off	2019	Mikhail Belkin Daniel Hsu Siyuan Ma Soumik Mandal
+ PDF Chat	Mixing time estimation in reversible Markov chains from a single sample path	2019	Daniel Hsu Aryeh Kontorovich David Levin Yuval Peres Csaba Szepesvári Geoffrey Wolfer
+	Unbiased estimators for random design regression	2019	Michał Dereziński Manfred K. Warmuth Daniel Hsu
+ PDF Chat	Using a machine learning approach to determine the space group of a structure from the atomic pair distribution function	2019	Chia-Hao Liu Yunzhe Tao Daniel Hsu Qiang Du Simon J. L. Billinge
+	A gradual, semi-discrete approach to generative network training via explicit Wasserstein minimization	2019	Yucheng Chen Matus Telgarsky Chao Zhang Bolton Bailey Daniel Hsu Jian Peng
+	Diameter-based Interactive Structure Search.	2019	Christopher Tosh Daniel Hsu
+	How many variables should be entered in a principal component regression equation	2019	Ji Xu Daniel Hsu
+	On the number of variables to use in principal component regression	2019	Ji Xu Daniel Hsu
+ PDF Chat	Certified Robustness to Adversarial Examples with Differential Privacy	2019	Mathias Lécuyer Vaggelis Atlidakis Roxana Geambasu Daniel Hsu Suman Jana
+	Correcting the bias in least squares regression with volume-rescaled sampling	2019	Michał Dereziński Manfred K. Warmuth Daniel Hsu
+	A cryptographic approach to black box adversarial machine learning	2019	Kevin Shi Daniel Hsu Allison Bishop
+	A gradual, semi-discrete approach to generative network training via explicit Wasserstein minimization	2019	Yucheng Chen Matus Telgarsky Chao Zhang Bolton Bailey Daniel Hsu Jian Peng
+	Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training	2019	Giannis Karamanolakis Daniel Hsu Luis Gravano
+	On the number of variables to use in principal component regression	2019	Ji Xu Daniel Hsu
+	Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health	2019	Giannis Karamanolakis Daniel Hsu Luis Gravano
+	Diameter-based Interactive Structure Discovery	2019	Christopher Tosh Daniel Hsu
+	Consistent Risk Estimation in Moderately High-Dimensional Linear Regression	2019	Ji Xu Arian Maleki Kamiar Rahnama Rad Daniel Hsu
+	A New Framework for Query Efficient Active Imitation Learning	2019	Daniel Hsu
+	Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health	2019	Giannis Karamanolakis Daniel Hsu Luis Gravano
+	Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training	2019	Giannis Karamanolakis Daniel Hsu Luis Gravano
+	Unbiased estimators for random design regression	2019	Michał Dereziński Manfred K. Warmuth Daniel Hsu
+	Reconciling modern machine learning and the bias-variance trade-off	2018	Mikhail Belkin Daniel Hsu Siyuan Ma Soumik Mandal
+	Reconciling modern machine learning practice and the bias-variance trade-off	2018	Mikhail Belkin Daniel Hsu Siyuan Ma Soumik Mandal
+	Benefits of over-parameterization with EM	2018	Ji Xu Daniel Hsu Arian Maleki
+	Learning Single Index Models in Gaussian Space	2018	Rishabh Dudeja Daniel Hsu
+	Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate	2018	Mikhail Belkin Daniel Hsu Partha P. Mitra
+ PDF Chat	Non-Gaussian information from weak lensing data via deep learning	2018	Arushi Gupta José Manuel Zorrilla Matilla Daniel Hsu Zoltán Haiman
+	Tail bounds for volume sampled linear regression.	2018	Michał Dereziński Manfred K. Warmuth Daniel Hsu
+	On the Connection between Differential Privacy and Adversarial Robustness in Machine Learning	2018	Mathias Lécuyer Vaggelis Atlidakis Roxana Geambasu Daniel Hsu Suman Jana
+	Certified Robustness to Adversarial Examples with Differential Privacy	2018	Mathias Lécuyer Vaggelis Atlidakis Roxana Geambasu Daniel Hsu Suman Jana
+	Leveraged volume sampling for linear regression	2018	Michał Dereziński Manfred K. Warmuth Daniel Hsu
+	Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate	2018	Mikhail Belkin Daniel Hsu Partha P. Mitra
+	Correcting the bias in least squares regression with volume-rescaled sampling	2018	Michał Dereziński Manfred K. Warmuth Daniel Hsu
+	Leveraged volume sampling for linear regression	2018	Michał Dereziński Manfred K. Warmuth Daniel Hsu
+	Benefits of over-parameterization with EM	2018	Ji Xu Daniel Hsu Arian Maleki
+	Certified Robustness to Adversarial Examples with Differential Privacy	2018	Mathias Lécuyer Vaggelis Atlidakis Roxana Geambasu Daniel Hsu Suman Jana
+	Coding with asymmetric prior knowledge.	2017	Alexandr Andoni Javad Ghaderi Daniel Hsu Dan Rubenstein Omri Weinstein
+	Linear regression without correspondence	2017	Daniel Hsu Kevin Shi Xiaorui Sun
+ PDF Chat	FairTest: Discovering Unwarranted Associations in Data-Driven Applications	2017	Florian Tramèr Vaggelis Atlidakis Roxana Geambasu Daniel Hsu Jean‐Pierre Hubaux Mathias Humbert Ari Juels Huang Lin
+	Kernel Approximation Methods for Speech Recognition	2017	Avner May Alireza Bagheri Garakani Zhiyun Lu Dong Guo Kuan Liu Aurélien Bellet Linxi Fan Michael Collins Daniel Hsu Brian Kingsbury
+	Kernel ridge vs. principal component regression: Minimax bounds and the qualification of regularization operators	2017	Lee H. Dicker Dean P. Foster Daniel Hsu
+	Linear regression without correspondence	2017	Daniel Hsu Kevin Shi Xiaorui Sun
+ PDF Chat	Greedy Approaches to Symmetric Orthogonal Tensor Decomposition	2017	Cun Mu Daniel Hsu Donald Goldfarb
+	Greedy Approaches to Symmetric Orthogonal Tensor Decomposition	2017	Cun Mu Daniel Hsu Donald Goldfarb
+	Multi-period Time Series Modeling with Sparsity via Bayesian Variational Inference	2017	Daniel Hsu
+	Coding sets with asymmetric information	2017	Alexandr Andoni Javad Ghaderi Daniel Hsu Dan Rubenstein Omri Weinstein
+	Parameter identification in Markov chain choice models	2017	Arushi Gupta Daniel Hsu
+	Anomaly Detection on Graph Time Series	2017	Daniel Hsu
+	Mixing time estimation in reversible Markov chains from a single sample path	2017	Daniel Hsu Aryeh Kontorovich David Levin Yuval Peres Csaba Szepesvári
+ PDF Chat	Do dark matter halos explain lensing peaks?	2016	José Manuel Zorrilla Matilla Zoltán Haiman Daniel Hsu Arushi Gupta Andrea Petri
+	Greedy bi-criteria approximations for $k$-medians and $k$-means	2016	Daniel Hsu Matus Telgarsky
+	Kernel ridge vs. principal component regression: minimax bounds and adaptability of regularization operators	2016	Lee H. Dicker Dean P. Foster Daniel Hsu
+	Global analysis of Expectation Maximization for mixtures of two Gaussians	2016	Ji Xu Daniel Hsu Arian Maleki
+	Loss minimization and parameter estimation with heavy tails	2016	Daniel Hsu Sivan Sabato
+	Search Improves Label for Active Learning	2016	Alina Beygelzimer Daniel Hsu John Langford Chicheng Zhang
+	Global Analysis of Expectation Maximization for Mixtures of Two Gaussians	2016	Ji Xu Daniel Hsu Arian Maleki
+	Greedy bi-criteria approximations for $k$-medians and $k$-means	2016	Daniel Hsu Matus Telgarsky
+	Mixing time estimation in reversible Markov chains from a single sample path	2015	Daniel Hsu Aryeh Kontorovich Csaba Szepesvári
+	Discovering Unwarranted Associations in Data-Driven Applications with the FairTest Testing Toolkit	2015	Florian Tramèr Vaggelis Atlidakis Roxana Geambasu Daniel Hsu Jean‐Pierre Hubaux Mathias Humbert Ari Juels Huang Lin
+	Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path	2015	Daniel Hsu Aryeh Kontorovich Csaba Szepesvári
+	Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT)	2015	Anima Anandkumar Rong Ge Daniel Hsu Sham M. Kakade Matus Telgarsky
+	FairTest: Discovering Unwarranted Associations in Data-Driven Applications	2015	Florian Tramèr Vaggelis Atlidakis Roxana Geambasu Daniel Hsu Jean‐Pierre Hubaux Mathias Humbert Ari Juels Huang Lin
+	When are overcomplete topic models identifiable? uniqueness of tensor tucker decompositions with structured sparsity	2015	Animashree Anandkumar Daniel Hsu Majid Janzamin Sham M. Kakade
+	Efficient and Parsimonious Agnostic Active Learning	2015	Tzu-Kuo Huang Alekh Agarwal Daniel Hsu John Langford Robert E. Schapire
+ PDF Chat	Successive Rank-One Approximations for Nearly Orthogonally Decomposable Symmetric Tensors	2015	Cun Mu Daniel Hsu Donald Goldfarb
+	Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path	2015	Daniel Hsu Aryeh Kontorovich Csaba Szepesvári
+	Scalable Non-linear Learning with Adaptive Polynomial Expansions	2014	Alekh Agarwal Alina Beygelzimer Daniel Hsu John Langford Matus Telgarsky
+	Scalable Nonlinear Learning with Adaptive Polynomial Expansions	2014	Alekh Agarwal Alina Beygelzimer Daniel Hsu John Langford Matus Telgarsky
+ PDF Chat	A Spectral Algorithm for Latent Dirichlet Allocation	2014	Anima Anandkumar Dean P. Foster Daniel Hsu Sham M. Kakade Yi-Kai Liu
+	Heavy-tailed regression with a generalized median-of-means	2014	Daniel Hsu Sivan Sabato
+ PDF Chat	Random Design Analysis of Ridge Regression	2014	Daniel Hsu Sham M. Kakade Tong Zhang
+	Weighted sampling of outer products	2014	Daniel Hsu
+	Tensor decompositions for learning latent variable models	2014	Animashree Anandkumar Rong Ge Daniel Hsu Sham M. Kakade Matus Telgarsky
+	The Large Margin Mechanism for Differentially Private Maximization	2014	Kamalika Chaudhuri Daniel Hsu Shuang Song
+	Fast Matrix Multiplication with Sketching	2014	Huan Wang Christos Boutsidis Edo Liberty Daniel Hsu
+	Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits	2014	Alekh Agarwal Daniel Hsu Satyen Kale John Langford Lihong Li Robert E. Schapire
+	A tensor approach to learning mixed membership community models	2014	Animashree Anandkumar Rong Ge Daniel Hsu Sham M. Kakade
+	Scalable Nonlinear Learning with Adaptive Polynomial Expansions	2014	Alekh Agarwal Alina Beygelzimer Daniel Hsu John Langford Matus Telgarsky
+	Contrastive Learning Using Spectral Methods	2013	James Zou Daniel Hsu David C. Parkes Ryan P. Adams
+	When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity	2013	Anima Anandkumar Daniel Hsu Majid Janzamin Sham M. Kakade
+	Approximate loss minimization with heavy tails.	2013	Daniel Hsu Sivan Sabato
+	When are Overcomplete Representations Identifiable? Uniqueness of Tensor Decompositions Under Expansion Constraints	2013	Animashree Anandkumar Daniel Hsu Majid Janzamin Sham M. Kakade
+	A Tensor Spectral Approach to Learning Mixed Membership Community Models	2013	Animashree Anandkumar Rong Ge Daniel Hsu Sham M. Kakade
+	A Tensor Spectral Approach to Learning Mixed Membership Community Models	2013	Anima Anandkumar Rong Ge Daniel Hsu Sham M. Kakade
+ PDF Chat	Learning mixtures of spherical gaussians	2013	Daniel Hsu Sham M. Kakade
+	A Tensor Approach to Learning Mixed Membership Community Models	2013	Anima Anandkumar Rong Ge Daniel Hsu Sham M. Kakade
+	Loss minimization and parameter estimation with heavy tails	2013	Daniel Hsu Sivan Sabato
+ PDF Chat	Stochastic Convex Optimization with Bandit Feedback	2013	Alekh Agarwal Dean P. Foster Daniel Hsu Sham M. Kakade Alexander Rakhlin
+	When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity	2013	Animashree Anandkumar Daniel Hsu Majid Janzamin Sham M. Kakade
+ PDF Chat	Tensor Decompositions for Learning Latent Variable Models	2012	Anima Anandkumar Rong Ge Daniel Hsu Sham M. Kakade Matus Telgarsky
+	Identifiability and Unmixing of Latent Parse Trees	2012	Daniel Hsu Sham M. Kakade Percy Liang
+	Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation	2012	Animashree Anandkumar Dean P. Foster Daniel Hsu Sham M. Kakade Yi-Kai Liu
+	A Method of Moments for Mixture Models and Hidden Markov Models	2012	Animashree Anandkumar Daniel Hsu Sham M. Kakade
+	A spectral algorithm for learning Hidden Markov Models	2012	Daniel Hsu Sham M. Kakade Tong Zhang
+	Learning High-Dimensional Mixtures of Graphical Models	2012	Animashree Anandkumar Daniel Hsu Sham M. Kakade
+	Learning Topic Models and Latent Bayesian Networks Under Expansion Constraints	2012	Animashree Anandkumar Daniel Hsu Adel Javanmard Sham M. Kakade
+	An Online Learning-based Framework for Tracking	2012	Kamalika Chaudhuri Yoav Freund Daniel Hsu
+ PDF Chat	A tail inequality for quadratic forms of subgaussian random vectors	2012	Daniel Hsu Sham M. Kakade Tong Zhang
+	Identifiability and Unmixing of Latent Parse Trees	2012	Percy Liang Daniel Hsu Sham M. Kakade
+	A concentration theorem for projections	2012	Sanjoy Dasgupta Daniel Hsu Nakul Verma
+	Analysis of a randomized approximation scheme for matrix multiplication	2012	Daniel Hsu Sham M. Kakade Tong Zhang
+ PDF Chat	Tail inequalities for sums of random matrices that depend on the intrinsic dimension	2012	Daniel Hsu Sham M. Kakade Tong Zhang
+	Learning mixtures of spherical Gaussians: moment methods and spectral decompositions	2012	Daniel Hsu Sham M. Kakade
+	Learning Sparse Low-Threshold Linear Classifiers	2012	Sivan Sabato Shai Shalev‐Shwartz Nathan Srebro Daniel Hsu Tong Zhang
+	Convergence Rates for Differentially Private Statistical Estimation	2012	Kamalika Chaudhuri Daniel Hsu
+	A Spectral Algorithm for Latent Dirichlet Allocation	2012	Animashree Anandkumar Dean P. Foster Daniel Hsu Sham M. Kakade Yi-Kai Liu
+	A Method of Moments for Mixture Models and Hidden Markov Models	2012	Animashree Anandkumar Daniel Hsu Sham M. Kakade
+	Random design analysis of ridge regression	2012	Daniel Hsu Sham M. Kakade Tong Zhang
+	Tensor decompositions for learning latent variable models	2012	Anima Anandkumar Rong Ge Daniel Hsu Sham M. Kakade Matus Telgarsky
+ PDF Chat	Parallel Online Learning	2011	Daniel Hsu Nikos Karampatziakis John Langford Alex Smola
+	Stochastic convex optimization with bandit feedback	2011	Alekh Agarwal Dean P. Foster Daniel Hsu Sham M. Kakade Alexander Rakhlin
+	An Analysis of Random Design Linear Regression	2011	Daniel Hsu Sham M. Kakade Tong Zhang
+	Efficient Optimal Learning for Contextual Bandits	2011	Miroslav Dudı́k Daniel Hsu Satyen Kale Nikos Karampatziakis John Langford Lev Reyzin Tong Zhang
+	Random design analysis of ridge regression	2011	Daniel Hsu Sham M. Kakade Tong Zhang
+	Dimension-free tail inequalities for sums of random matrices	2011	Daniel Hsu Sham M. Kakade Tong Zhang
+	Efficient Optimal Learning for Contextual Bandits	2011	Miroslav Dudı́k Daniel Hsu Satyen Kale Nikos Karampatziakis John Langford Lev Reyzin Tong Zhang
+	Spectral Methods for Learning Multivariate Latent Tree Structure	2011	Animashree Anandkumar Kamalika Chaudhuri Daniel Hsu Sham M. Kakade Le Song Tong Zhang
+	Random design analysis of ridge regression	2011	Daniel Hsu Sham M. Kakade Tong Zhang
+	A tail inequality for quadratic forms of subgaussian random vectors	2011	Daniel Hsu Sham M. Kakade Tong Zhang
+ PDF Chat	Scaling Up Machine Learning: Parallel Online Learning	2011	Daniel Hsu Nikos Karampatziakis John Langford Alex Smola
+	Stochastic convex optimization with bandit feedback	2011	Alekh Agarwal Dean P. Foster Daniel Hsu Sham M. Kakade Alexander Rakhlin
+	Parallel Online Learning	2011	Daniel Hsu Nikos Karampatziakis John Langford Alex Smola
+	An online learning-based framework for tracking	2010	Kamalika Chaudhuri Yoav Freund Daniel Hsu
+	Agnostic Active Learning Without Constraints	2010	Alina Beygelzimer Daniel Hsu John Langford Tong Zhang
+	Agnostic Active Learning Without Constraints	2010	Alina Beygelzimer John Langford Tong Zhang Daniel Hsu
+	Robust Matrix Decomposition with Outliers	2010	Daniel Hsu Sham M. Kakade Tong Zhang
+	A parameter-free hedging algorithm	2009	Kamalika Chaudhuri Yoav Freund Daniel Hsu
+	Tracking using explanation-based modeling	2009	Kamalika Chaudhuri Yoav Freund Daniel Hsu
+	Multi-Label Prediction via Compressed Sensing	2009	Daniel Hsu Sham M. Kakade John Langford Tong Zhang
+	A new Hedging algorithm and its application to inferring latent random variables	2008	Yoav Freund Daniel Hsu
+	A Spectral Algorithm for Learning Hidden Markov Models	2008	Daniel Hsu Sham M. Kakade Tong Zhang
+	A concentration theorem for projections	2006	Sanjoy Dasgupta Daniel Hsu Nakul Verma

Common Coauthors

Coauthor	Papers Together
Sham M. Kakade	41
Matus Telgarsky	16
John Langford	14
Animashree Anandkumar	13
Luis Gravano	13
Giannis Karamanolakis	12
Ji Xu	12
Clayton Sanford	10
Samuel Deng	9
Roxana Geambasu	9
Alekh Agarwal	8
Rong Ge	8
Dean P. Foster	8
Vaggelis Atlidakis	7
Mikhail Belkin	7
Kamalika Chaudhuri	7
Anima Anandkumar	7
Michał Dereziński	7
Christopher Tosh	7
Manfred K. Warmuth	7
Arian Maleki	6
Suman Jana	6
Mathias Lécuyer	6
Alina Beygelzimer	6
Nakul Verma	6
Nikos Karampatziakis	5
Rishabh Dudeja	5
Akshay Krishnamurthy	5
Sivan Sabato	5
Yoav Freund	5
Csaba Szepesvári	5
Navid Ardeshir	5
Aryeh Kontorovich	5
José Manuel Zorrilla Matilla	4
Zoltán Haiman	4
Miroslav Dudík	4
Arushi Gupta	4
Fabrizio Dell’Acqua	4
Tong Zhang	4
Augustin Chaintreau	4
Tong Zhang	4
Bo Cowgill	4
Majid Janzamin	4
Robert E. Schapire	4
Kevin Shi	3
Soumik Mandal	3
Mathias Humbert	3
Siyuan Ma	3
Debmalya Mandal	3
Ari Juels	3

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	A Method of Moments for Mixture Models and Hidden Markov Models	2012	Animashree Anandkumar Daniel Hsu Sham M. Kakade	18
+ PDF Chat	Learning nonsingular phylogenies and hidden Markov models	2006	Elchanan Mossel Sébastien Roch	17
+	Learning mixtures of arbitrary gaussians	2001	Sanjeev Arora Ravi Kannan	11
+ PDF Chat	Adaptive estimation of a quadratic functional by model selection	2000	Béatrice Laurent Pascal Massart	11
+	Tensor decompositions for learning latent variable models	2014	Animashree Anandkumar Rong Ge Daniel Hsu Sham M. Kakade Matus Telgarsky	10
+	Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics	1977	Joseph B. Kruskal	10
+	Tensor Decompositions and Applications	2009	Tamara G. Kolda Brett W. Bader	10
+ PDF Chat	User-Friendly Tail Bounds for Sums of Random Matrices	2011	Joel A. Tropp	10
+	A Spectral Algorithm for Latent Dirichlet Allocation	2012	Animashree Anandkumar Dean P. Foster Daniel Hsu Sham M. Kakade Yi-Kai Liu	9
+	Polynomial Learning of Distribution Families	2010	Mikhail Belkin K. P. Sinha	9
+ PDF Chat	Two Models of Double Descent for Weak Features	2020	Mikhail A. Belkin Daniel Hsu Ji Xu	8
+	Shifted power method for computing tensor eigenpairs.	2010	Jackson Mayo Tamara G. Kolda	8
+	On Spectral Learning of Mixtures of Distributions	2005	Dimitris Achlioptas Frank McSherry	8
+	A spectral algorithm for learning Hidden Markov Models	2012	Daniel Hsu Sham M. Kakade Tong Zhang	7
+	On the Best Rank-1 and Rank-(<i>R</i><sub>1</sub> ,<i>R</i><sub>2</sub> ,. . .,<i>R<sub>N</sub></i>) Approximation of Higher-Order Tensors	2000	Lieven De Lathauwer Bart De Moor Joos Vandewalle	7
+ PDF Chat	Identifiability of parameters in latent structure models with many observed variables	2009	Elizabeth S. Allman Catherine Matias John A. Rhodes	7
+	Regression Shrinkage and Selection Via the Lasso	1996	Robert Tibshirani	7
+	Distilling the Knowledge in a Neural Network	2015	Geoffrey E. Hinton Oriol Vinyals Jay B. Dean	6
+	The Volume of Convex Bodies and Banach Space Geometry	1989	Gilles Pisier	6
+	Rank-One Approximation to High Order Tensors	2001	Tong Zhang Gene H. Golub	6
+	Smallest singular value of random matrices and geometry of random polytopes	2004	A.E. Litvak Alain Pajor Mark Rudelson Nicole Tomczak-Jaegermann	6
+	Some methods for classification and analysis of multivariate observations	1967	James B. MacQueen	6
+	Harmless Interpolation of Noisy Data in Regression	2020	V. Sai Muthukumar Kailas Vodrahalli Vignesh Subramanian Anant Sahai	6
+	Benign overfitting in linear regression	2020	Peter L. Bartlett Philip M. Long Gábor Lugosi Alexander Tsigler	6
+ PDF Chat	On Tail Probabilities for Martingales	1975	David A. Freedman	6
+ PDF Chat	Most Tensor Problems Are NP-Hard	2013	Christopher J. Hillar Lek‐Heng Lim	5
+ PDF Chat	Singular Values and Eigenvalues of Tensors: A Variational Approach	2006	Lek‐Heng Lim	5
+ PDF Chat	Learning Topic Models -- Going beyond SVD	2012	Sanjeev Arora Rong Ge Ankur Moitra	5
+ PDF Chat	On exchangeable random variables and the statistics of large graphs and hypergraphs	2008	Tim Austin	5
+	Mixture Densities, Maximum Likelihood and the EM Algorithm	1984	Richard A. Redner Homer F. Walker	5
+ PDF Chat	Isotropic PCA and Affine-Invariant Clustering	2008	S. Charles Brubaker Santosh Vempala	5
+ PDF Chat	Sums of random Hermitian matrices and an inequality by Rudelson	2010	Roberto I. Oliveira	5
+	On the Best Rank-1 Approximation of Higher-Order Supersymmetric Tensors	2002	Eleftherios Kofidis Phillip A. Regalia	5
+ PDF Chat	A jamming transition from under- to over-parametrization affects generalization in deep learning	2019	Stefano Spigler Mario Geiger Stéphane d’Ascoli Levent Sagun Giulio Biroli Matthieu Wyart	5
+	Numerical Methods for Simultaneous Diagonalization	1993	Angelika Bunse‐Gerstner Ralph Byers Volker Mehrmann	5
+	Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm	1977	A. P. Dempster N. M. Laird Donald B. Rubin	5
+	Settling the Polynomial Learnability of Mixtures of Gaussians	2010	Ankur Moitra Gregory Valiant	5
+ PDF Chat	Random Design Analysis of Ridge Regression	2014	Daniel Hsu Sham M. Kakade Tong Zhang	5
+	Spectral partitioning of random graphs	2001	Frank McSherry	5
+	Surprises in high-dimensional ridgeless least squares interpolation	2022	Trevor Hastie Andrea Montanari Saharon Rosset Ryan J. Tibshirani	5
+ PDF Chat	Settling the Polynomial Learnability of Mixtures of Gaussians	2010	Ankur Moitra Gregory Valiant	5
+	A Modern Take on the Bias-Variance Tradeoff in Neural Networks	2018	Brady Neal Sarthak Mittal Aristide Baratin Vinayak Tantia Matthew Scicluna Simon Lacoste-Julien Ioannis Mitliagkas	5
+	Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders	2012	Sanjeev Arora Rong Ge Ankur Moitra Sushant Sachdeva	5
+	Robust linear least squares regression	2011	Jean-Yves Audibert Olivier Catoni	5
+ PDF Chat	Certifying and Removing Disparate Impact	2015	Michael Feldman Sorelle A. Friedler John Moeller Carlos Scheidegger Suresh Venkatasubramanian	4
+ PDF Chat	Learning mixtures of spherical gaussians	2013	Daniel Hsu Sham M. Kakade	4
+ PDF Chat	Local Convergence of the Alternating Least Squares Algorithm for Canonical Tensor Approximation	2012	André Uschmajew	4
+	Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-model factor analysis	1970	Richard A. Harshman	4
+	Convolutional Neural Networks for Sentence Classification	2014	Yoon Kim	4
+ PDF Chat	Cosmology constraints from the weak lensing peak counts and the power spectrum in CFHTLenS data	2015	Jia Liu Andrea Petri Zoltán Haiman Lam Hui Jan M. Kratochvil M. May	4