Daniel J. Hsu

Publication Activity (10 Years)

Years Active: 2006-2023
Publications (10 Years): 31

Top Venues

CoRR

NeurIPS

NIPS

J. Mach. Learn. Res.

University of California, San Diego, USA (PHD)

Education History

University of California, San Diego, USA

PHD

Publications

Navid Ardeshir, Daniel J. Hsu, Clayton Hendrick Sanford
Intrinsic dimensionality and generalization properties of the R-norm inductive bias. COLT (2023)
Clayton Sanford, Daniel J. Hsu, Matus Telgarsky
Representational Strengths and Limitations of Transformers. NeurIPS (2023)
Daniel J. Hsu, Clayton Hendrick Sanford, Rocco A. Servedio, Emmanouil-Vasileios Vlatakis-Gkaragkounis
Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals. COLT (2022)
Bingbin Liu, Daniel J. Hsu, Pradeep Ravikumar, Andrej Risteski
Masked Prediction: A Parameter Identifiability View. NeurIPS (2022)
Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel J. Hsu, Thodoris Lykouris, Miroslav Dudík, Robert E. Schapire
Bayesian decision-making under misspecified priors with applications to meta-learning. NeurIPS (2021)
Navid Ardeshir, Clayton Sanford, Daniel J. Hsu
Support vector machines and linear regression coincide with very high-dimensional features. NeurIPS (2021)
Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel J. Hsu, Anant Sahai
Classification vs regression in overparameterized regimes: Does the loss function matter? CoRR (2020)
Debmalya Mandal, Samuel Deng, Suman Jana, Jeannette M. Wing, Daniel J. Hsu
Ensuring Fairness Beyond the Training Data. NeurIPS (2020)
Ji Xu, Daniel J. Hsu
On the number of variables to use in principal component regression. NeurIPS (2019)
Michal Derezinski, Manfred K. Warmuth, Daniel J. Hsu
Leveraged volume sampling for linear regression. NeurIPS (2018)
Ji Xu, Daniel J. Hsu, Arian Maleki
Benefits of over-parameterization with EM. NeurIPS (2018)
Mikhail Belkin, Daniel J. Hsu, Partha Mitra
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate. NeurIPS (2018)
Daniel J. Hsu, Kevin Shi, Xiaorui Sun
Linear regression without correspondence. NIPS (2017)
Florian Tramèr, Vaggelis Atlidakis, Roxana Geambasu, Daniel J. Hsu, Jean-Pierre Hubaux, Mathias Humbert, Ari Juels, Huang Lin
FairTest: Discovering Unwarranted Associations in Data-Driven Applications. EuroS&P (2017)
Cun Mu, Daniel J. Hsu, Donald Goldfarb
Greedy Approaches to Symmetric Orthogonal Tensor Decomposition. SIAM J. Matrix Anal. Appl. 38 (4) (2017)
Alexandr Andoni, Daniel J. Hsu, Kevin Shi, Xiaorui Sun
Correspondence retrieval. COLT (2017)
Daniel J. Hsu, Aryeh Kontorovich, David A. Levin, Yuval Peres, Csaba Szepesvári
Mixing time estimation in reversible Markov chains from a single sample path. CoRR (2017)
Cun Mu, Daniel J. Hsu, Donald Goldfarb
Greedy Approaches to Symmetric Orthogonal Tensor Decomposition. CoRR (2017)
Avner May, Alireza Bagheri Garakani, Zhiyun Lu, Dong Guo, Kuan Liu, Aurélien Bellet, Linxi Fan, Michael Collins, Daniel J. Hsu, Brian Kingsbury, Michael Picheny, Fei Sha
Kernel Approximation Methods for Speech Recognition. CoRR (2017)
Alexandr Andoni, Javad Ghaderi, Daniel J. Hsu, Dan Rubenstein, Omri Weinstein
Coding with asymmetric prior knowledge. CoRR (2017)
Cun Mu, Daniel J. Hsu, Donald Goldfarb
Successive Rank-One Approximations for Nearly Orthogonally Decomposable Symmetric Tensors. CoRR (2017)
Daniel J. Hsu, Kevin Shi, Xiaorui Sun
Linear regression without correspondence. CoRR (2017)
Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang
Search Improves Label for Active Learning. NIPS (2016)
Ji Xu, Daniel J. Hsu, Arian Maleki
Global analysis of Expectation Maximization for mixtures of two Gaussians. CoRR (2016)
Karl Stratos, Michael Collins, Daniel J. Hsu
Unsupervised Part-Of-Speech Tagging with Anchor Hidden Markov Models. Trans. Assoc. Comput. Linguistics 4 (2016)
Daniel J. Hsu, Matus Telgarsky
Greedy bi-criteria approximations for k-medians and k-means. CoRR (2016)
Ji Xu, Daniel J. Hsu, Arian Maleki
Global Analysis of Expectation Maximization for Mixtures of Two Gaussians. NIPS (2016)
Daniel J. Hsu, Sivan Sabato
Loss Minimization and Parameter Estimation with Heavy Tails. J. Mach. Learn. Res. 17 (2016)
Haris Aziz, Elias Bareinboim, Yejin Choi, Daniel J. Hsu, Shivaram Kalyanakrishnan, Reshef Meir, Suchi Saria, Gerardo I. Simari, Lirong Xia, William Yeoh
AI's 10 to Watch. IEEE Intell. Syst. 31 (1) (2016)
Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang
Search Improves Label for Active Learning. CoRR (2016)
Avner May, Michael Collins, Daniel J. Hsu, Brian Kingsbury
Compact kernel models for acoustic modeling via random feature selection. ICASSP (2016)
Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire
Efficient and Parsimonious Agnostic Active Learning. NIPS (2015)
Y. Cem Sübakan, Johannes Traa, Paris Smaragdis, Daniel J. Hsu
Method of moments learning for left-to-right Hidden Markov models. WASPAA (2015)
Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel J. Hsu, Tong Zhang
Learning sparse low-threshold linear classifiers. J. Mach. Learn. Res. 16 (2015)
Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire
Efficient and Parsimonious Agnostic Active Learning. CoRR (2015)
Anima Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-Kai Liu
A Spectral Algorithm for Latent Dirichlet Allocation. Algorithmica 72 (1) (2015)
Daniel J. Hsu, Aryeh Kontorovich, Csaba Szepesvári
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path. CoRR (2015)
Animashree Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade
When are overcomplete topic models identifiable? uniqueness of tensor tucker decompositions with structured sparsity. J. Mach. Learn. Res. 16 (2015)
Mathias Lécuyer, Riley Spahn, Yannis Spiliopolous, Augustin Chaintreau, Roxana Geambasu, Daniel J. Hsu
Sunlight: Fine-grained Targeting Detection at Scale with Statistical Confidence. CCS (2015)
Daniel J. Hsu, Aryeh Kontorovich, Csaba Szepesvári
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path. NIPS (2015)
Cun Mu, Daniel J. Hsu, Donald Goldfarb
Successive Rank-One Approximations for Nearly Orthogonally Decomposable Symmetric Tensors. SIAM J. Matrix Anal. Appl. 36 (4) (2015)
Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky
Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT). ALT (2015)
Florian Tramèr, Vaggelis Atlidakis, Roxana Geambasu, Daniel J. Hsu, Jean-Pierre Hubaux, Mathias Humbert, Ari Juels, Huang Lin
Discovering Unwarranted Associations in Data-Driven Applications with the FairTest Testing Toolkit. CoRR (2015)
Karl Stratos, Michael Collins, Daniel J. Hsu
Model-based Word Embeddings from Decompositions of Count Matrices. ACL (1) (2015)
Daniel J. Hsu, Sivan Sabato
Heavy-tailed regression with a generalized median-of-means. ICML (2014)
Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song
The Large Margin Mechanism for Differentially Private Maximization. NIPS (2014)
Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus Telgarsky
Scalable Nonlinear Learning with Adaptive Polynomial Expansions. CoRR (2014)
Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus Telgarsky
Scalable Non-linear Learning with Adaptive Polynomial Expansions. NIPS (2014)
Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky
Tensor decompositions for learning latent variable models. J. Mach. Learn. Res. 15 (1) (2014)
Karl Stratos, Do-kyum Kim, Michael Collins, Daniel J. Hsu
A Spectral Algorithm for Learning Class-Based n-gram Models of Natural Language. UAI (2014)
Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade
A tensor approach to learning mixed membership community models. J. Mach. Learn. Res. 15 (1) (2014)
Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song
The Large Margin Mechanism for Differentially Private Maximization. CoRR (2014)
Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. CoRR (2014)
Daniel J. Hsu, Sham M. Kakade, Tong Zhang
Random Design Analysis of Ridge Regression. Found. Comput. Math. 14 (3) (2014)
Daniel J. Hsu
Weighted sampling of outer products. CoRR (2014)
Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. ICML (2014)
James Y. Zou, Daniel J. Hsu, David C. Parkes, Ryan Prescott Adams
Contrastive Learning Using Spectral Methods. NIPS (2013)
Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin
Stochastic Convex Optimization with Bandit Feedback. SIAM J. Optim. 23 (1) (2013)
Animashree Anandkumar, Daniel J. Hsu, Adel Javanmard, Sham M. Kakade
Learning Linear Bayesian Networks with Latent Variables. ICML (1) (2013)
Daniel J. Hsu, Sivan Sabato
Approximate loss minimization with heavy tails. CoRR (2013)
Animashree Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity. CoRR (2013)
Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade
A Tensor Spectral Approach to Learning Mixed Membership Community Models CoRR (2013)
Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade
A Tensor Spectral Approach to Learning Mixed Membership Community Models. COLT (2013)
Anima Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity. NIPS (2013)
Daniel J. Hsu, Sham M. Kakade
Learning mixtures of spherical gaussians: moment methods and spectral decompositions. ITCS (2013)
Daniel J. Hsu, Sham M. Kakade
Learning Gaussian Mixture Models: Moment Methods and Spectral Decompositions CoRR (2012)
Daniel J. Hsu, Sham M. Kakade, Tong Zhang
Analysis of a randomized approximation scheme for matrix multiplication CoRR (2012)
Animashree Anandkumar, Daniel J. Hsu, Sham M. Kakade
Learning High-Dimensional Mixtures of Graphical Models CoRR (2012)
Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel J. Hsu, Tong Zhang
Learning Sparse Low-Threshold Linear Classifiers CoRR (2012)
Kamalika Chaudhuri, Daniel J. Hsu
Convergence Rates for Differentially Private Statistical Estimation. ICML (2012)
Animashree Anandkumar, Daniel J. Hsu, Furong Huang, Sham M. Kakade
Learning Mixtures of Tree Graphical Models. NIPS (2012)
Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu
An Online Learning-based Framework for Tracking CoRR (2012)
Daniel J. Hsu, Sham M. Kakade, Tong Zhang
A spectral algorithm for learning Hidden Markov Models. J. Comput. Syst. Sci. 78 (5) (2012)
Daniel J. Hsu, Sham M. Kakade, Percy Liang
Identifiability and Unmixing of Latent Parse Trees CoRR (2012)
Animashree Anandkumar, Daniel J. Hsu, Adel Javanmard, Sham M. Kakade
Learning Linear Bayesian Networks with Latent Variables CoRR (2012)
Sanjoy Dasgupta, Daniel J. Hsu, Nakul Verma
A concentration theorem for projections. CoRR (2012)
Anima Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-Kai Liu
A Spectral Algorithm for Latent Dirichlet Allocation. NIPS (2012)
Animashree Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-Kai Liu
Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation CoRR (2012)
Daniel J. Hsu, Sham M. Kakade, Tong Zhang
Random Design Analysis of Ridge Regression. COLT (2012)
Animashree Anandkumar, Daniel J. Hsu, Sham M. Kakade
A Method of Moments for Mixture Models and Hidden Markov Models CoRR (2012)
Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky
Tensor decompositions for learning latent variable models CoRR (2012)
Daniel J. Hsu, Sham M. Kakade, Percy Liang
Identifiability and Unmixing of Latent Parse Trees. NIPS (2012)
Animashree Anandkumar, Daniel J. Hsu, Sham M. Kakade
A Method of Moments for Mixture Models and Hidden Markov Models. COLT (2012)
Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin
Stochastic convex optimization with bandit feedback. NIPS (2011)
Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin
Stochastic convex optimization with bandit feedback CoRR (2011)
Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang
Efficient Optimal Learning for Contextual Bandits. UAI (2011)
Kamalika Chaudhuri, Daniel J. Hsu
Sample Complexity Bounds for Differentially Private Learning. COLT (2011)
Daniel J. Hsu, Sham M. Kakade, Tong Zhang
A tail inequality for quadratic forms of subgaussian random vectors CoRR (2011)
Daniel J. Hsu, Sham M. Kakade, Tong Zhang
Dimension-free tail inequalities for sums of random matrices. CoRR (2011)
Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang
Spectral Methods for Learning Multivariate Latent Tree Structure. NIPS (2011)
Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang
Spectral Methods for Learning Multivariate Latent Tree Structure CoRR (2011)
Daniel J. Hsu, Sham M. Kakade, Tong Zhang
Robust Matrix Decomposition With Sparse Corruptions. IEEE Trans. Inf. Theory 57 (11) (2011)
Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang
Efficient Optimal Learning for Contextual Bandits CoRR (2011)
Daniel J. Hsu, Sham M. Kakade, Tong Zhang
An Analysis of Random Design Linear Regression CoRR (2011)
Daniel J. Hsu, Nikos Karampatziakis, John Langford, Alexander J. Smola
Parallel Online Learning CoRR (2011)
Alina Beygelzimer, Daniel J. Hsu, John Langford, Tong Zhang
Agnostic Active Learning Without Constraints CoRR (2010)
Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu
An Online Learning-based Framework for Tracking. UAI (2010)
Daniel J. Hsu, Sham M. Kakade, Tong Zhang
Robust Matrix Decomposition with Outliers CoRR (2010)
Alina Beygelzimer, Daniel J. Hsu, John Langford, Tong Zhang
Agnostic Active Learning Without Constraints. NIPS (2010)
Daniel J. Hsu, Sham M. Kakade, John Langford, Tong Zhang
Multi-Label Prediction via Compressed Sensing CoRR (2009)

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Publication Activity (10 Years)

Top Topics

Top Venues

Daniel J. Hsu