Daniel J. Hsu - RankMe

114 papers A* 24A 6B 1Misc 2Journal 59Unranked 21

Year	Rank	Type	Title / Venue / Authors
2024	A*	`conf`	Group-wise oracle-efficient algorithms for online multi-group learning. NeurIPS Samuel Deng, Jingwen Liu, Daniel J. Hsu
2023	A*	`conf`	Intrinsic dimensionality and generalization properties of the R-norm inductive bias. COLT Navid Ardeshir, Daniel J. Hsu, Clayton Hendrick Sanford
2023	A*	`conf`	Representational Strengths and Limitations of Transformers. NeurIPS Clayton Sanford, Daniel J. Hsu, Matus Telgarsky
2022	A*	`conf`	Masked Prediction: A Parameter Identifiability View. NeurIPS Bingbin Liu, Daniel J. Hsu, Pradeep Ravikumar, Andrej Risteski
2022	A*	`conf`	Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals. COLT Daniel J. Hsu, Clayton Hendrick Sanford, Rocco A. Servedio, Emmanouil-Vasileios Vlatakis-Gkaragkounis
2021	A*	`conf`	Bayesian decision-making under misspecified priors with applications to meta-learning. NeurIPS Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel J. Hsu, Thodoris Lykouris, Miroslav Dudík, Robert E. Schapire
2021	A*	`conf`	Support vector machines and linear regression coincide with very high-dimensional features. NeurIPS Navid Ardeshir, Clayton Sanford, Daniel J. Hsu
2020	J	`jnl`	Classification vs regression in overparameterized regimes: Does the loss function matter? CoRR Vidya Muthukumar, Adhyyan Narang, Vignesh Subramanian, Mikhail Belkin, Daniel J. Hsu, Anant Sahai
2020	A*	`conf`	Ensuring Fairness Beyond the Training Data. NeurIPS Debmalya Mandal, Samuel Deng, Suman Jana, Jeannette M. Wing, Daniel J. Hsu
2019	A*	`conf`	On the number of variables to use in principal component regression. NeurIPS Ji Xu, Daniel J. Hsu
2018	A*	`conf`	Benefits of over-parameterization with EM. NeurIPS Ji Xu, Daniel J. Hsu, Arian Maleki
2018	A*	`conf`	Leveraged volume sampling for linear regression. NeurIPS Michal Derezinski, Manfred K. Warmuth, Daniel J. Hsu
2018	A*	`conf`	Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate. NeurIPS Mikhail Belkin, Daniel J. Hsu, Partha Mitra
2017	J	`jnl`	Coding with asymmetric prior knowledge. CoRR Alexandr Andoni, Javad Ghaderi, Daniel J. Hsu, Dan Rubenstein, Omri Weinstein
2017	A*	`conf`	Correspondence retrieval. COLT Alexandr Andoni, Daniel J. Hsu, Kevin Shi, Xiaorui Sun
2017	A	`conf`	FairTest: Discovering Unwarranted Associations in Data-Driven Applications. EuroS&P Florian Tramèr, Vaggelis Atlidakis, Roxana Geambasu, Daniel J. Hsu, Jean-Pierre Hubaux, Mathias Humbert, Ari Juels, Huang Lin
2017	J	`jnl`	Greedy Approaches to Symmetric Orthogonal Tensor Decomposition. CoRR Cun Mu, Daniel J. Hsu, Donald Goldfarb
2017	J	`jnl`	Greedy Approaches to Symmetric Orthogonal Tensor Decomposition. SIAM J. Matrix Anal. Appl. Cun Mu, Daniel J. Hsu, Donald Goldfarb
2017	J	`jnl`	Kernel Approximation Methods for Speech Recognition. CoRR 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
2017	—	`conf`	Linear regression without correspondence. NIPS Daniel J. Hsu, Kevin Shi, Xiaorui Sun
2017	J	`jnl`	Linear regression without correspondence. CoRR Daniel J. Hsu, Kevin Shi, Xiaorui Sun
2017	J	`jnl`	Mixing time estimation in reversible Markov chains from a single sample path. CoRR Daniel J. Hsu, Aryeh Kontorovich, David A. Levin, Yuval Peres, Csaba Szepesvári
2017	J	`jnl`	Successive Rank-One Approximations for Nearly Orthogonally Decomposable Symmetric Tensors. CoRR Cun Mu, Daniel J. Hsu, Donald Goldfarb
2016	J	`jnl`	AI's 10 to Watch. IEEE Intell. Syst. Haris Aziz, Elias Bareinboim, Yejin Choi, Daniel J. Hsu, Shivaram Kalyanakrishnan, Reshef Meir, Suchi Saria, Gerardo I. Simari, Lirong Xia, William Yeoh
2016	Misc	`conf`	Compact kernel models for acoustic modeling via random feature selection. ICASSP Avner May, Michael Collins, Daniel J. Hsu, Brian Kingsbury
2016	—	`conf`	Global Analysis of Expectation Maximization for Mixtures of Two Gaussians. NIPS Ji Xu, Daniel J. Hsu, Arian Maleki
2016	J	`jnl`	Global analysis of Expectation Maximization for mixtures of two Gaussians. CoRR Ji Xu, Daniel J. Hsu, Arian Maleki
2016	J	`jnl`	Greedy bi-criteria approximations for k-medians and k-means. CoRR Daniel J. Hsu, Matus Telgarsky
2016	J	`jnl`	Loss Minimization and Parameter Estimation with Heavy Tails. J. Mach. Learn. Res. Daniel J. Hsu, Sivan Sabato
2016	—	`conf`	Search Improves Label for Active Learning. NIPS Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang
2016	J	`jnl`	Search Improves Label for Active Learning. CoRR Alina Beygelzimer, Daniel J. Hsu, John Langford, Chicheng Zhang
2016	J	`jnl`	Unsupervised Part-Of-Speech Tagging with Anchor Hidden Markov Models. Trans. Assoc. Comput. Linguistics Karl Stratos, Michael Collins, Daniel J. Hsu
2015	J	`jnl`	A Spectral Algorithm for Latent Dirichlet Allocation. Algorithmica Anima Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-Kai Liu
2015	J	`jnl`	Discovering Unwarranted Associations in Data-Driven Applications with the FairTest Testing Toolkit. CoRR Florian Tramèr, Vaggelis Atlidakis, Roxana Geambasu, Daniel J. Hsu, Jean-Pierre Hubaux, Mathias Humbert, Ari Juels, Huang Lin
2015	—	`conf`	Efficient and Parsimonious Agnostic Active Learning. NIPS Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire
2015	J	`jnl`	Efficient and Parsimonious Agnostic Active Learning. CoRR Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire
2015	J	`jnl`	Learning sparse low-threshold linear classifiers. J. Mach. Learn. Res. Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel J. Hsu, Tong Zhang
2015	—	`conf`	Method of moments learning for left-to-right Hidden Markov models. WASPAA Y. Cem Sübakan, Johannes Traa, Paris Smaragdis, Daniel J. Hsu
2015	—	`conf`	Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path. NIPS Daniel J. Hsu, Aryeh Kontorovich, Csaba Szepesvári
2015	J	`jnl`	Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path. CoRR Daniel J. Hsu, Aryeh Kontorovich, Csaba Szepesvári
2015	—	`conf`	Model-based Word Embeddings from Decompositions of Count Matrices. ACL (1) Karl Stratos, Michael Collins, Daniel J. Hsu
2015	J	`jnl`	Successive Rank-One Approximations for Nearly Orthogonally Decomposable Symmetric Tensors. SIAM J. Matrix Anal. Appl. Cun Mu, Daniel J. Hsu, Donald Goldfarb
2015	A*	`conf`	Sunlight: Fine-grained Targeting Detection at Scale with Statistical Confidence. CCS Mathias Lécuyer, Riley Spahn, Yannis Spiliopolous, Augustin Chaintreau, Roxana Geambasu, Daniel J. Hsu
2015	B	`conf`	Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT). ALT Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky
2015	J	`jnl`	When are overcomplete topic models identifiable? uniqueness of tensor tucker decompositions with structured sparsity. J. Mach. Learn. Res. Animashree Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade
2014	A	`conf`	A Spectral Algorithm for Learning Class-Based n-gram Models of Natural Language. UAI Karl Stratos, Do-kyum Kim, Michael Collins, Daniel J. Hsu
2014	J	`jnl`	A tensor approach to learning mixed membership community models. J. Mach. Learn. Res. Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade
2014	A*	`conf`	Heavy-tailed regression with a generalized median-of-means. ICML Daniel J. Hsu, Sivan Sabato
2014	J	`jnl`	Random Design Analysis of Ridge Regression. Found. Comput. Math. Daniel J. Hsu, Sham M. Kakade, Tong Zhang
2014	—	`conf`	Scalable Non-linear Learning with Adaptive Polynomial Expansions. NIPS Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus Telgarsky
2014	J	`jnl`	Scalable Nonlinear Learning with Adaptive Polynomial Expansions. CoRR Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus Telgarsky
2014	A*	`conf`	Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. ICML Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
2014	J	`jnl`	Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. CoRR Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
2014	J	`jnl`	Tensor decompositions for learning latent variable models. J. Mach. Learn. Res. Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky
2014	—	`conf`	The Large Margin Mechanism for Differentially Private Maximization. NIPS Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song
2014	J	`jnl`	The Large Margin Mechanism for Differentially Private Maximization. CoRR Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song
2014	J	`jnl`	Weighted sampling of outer products. CoRR Daniel J. Hsu
2013	J	`jnl`	A Tensor Spectral Approach to Learning Mixed Membership Community Models CoRR Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade
2013	A*	`conf`	A Tensor Spectral Approach to Learning Mixed Membership Community Models. COLT Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade
2013	J	`jnl`	Approximate loss minimization with heavy tails. CoRR Daniel J. Hsu, Sivan Sabato
2013	—	`conf`	Contrastive Learning Using Spectral Methods. NIPS James Y. Zou, Daniel J. Hsu, David C. Parkes, Ryan Prescott Adams
2013	—	`conf`	Learning Linear Bayesian Networks with Latent Variables. ICML (1) Animashree Anandkumar, Daniel J. Hsu, Adel Javanmard, Sham M. Kakade
2013	A	`conf`	Learning mixtures of spherical gaussians: moment methods and spectral decompositions. ITCS Daniel J. Hsu, Sham M. Kakade
2013	J	`jnl`	Stochastic Convex Optimization with Bandit Feedback. SIAM J. Optim. Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin
2013	—	`conf`	When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity. NIPS Anima Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade
2013	J	`jnl`	When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity. CoRR Animashree Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade
2012	J	`jnl`	A Method of Moments for Mixture Models and Hidden Markov Models CoRR Animashree Anandkumar, Daniel J. Hsu, Sham M. Kakade
2012	A*	`conf`	A Method of Moments for Mixture Models and Hidden Markov Models. COLT Animashree Anandkumar, Daniel J. Hsu, Sham M. Kakade
2012	—	`conf`	A Spectral Algorithm for Latent Dirichlet Allocation. NIPS Anima Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-Kai Liu
2012	J	`jnl`	A concentration theorem for projections. CoRR Sanjoy Dasgupta, Daniel J. Hsu, Nakul Verma
2012	J	`jnl`	A spectral algorithm for learning Hidden Markov Models. J. Comput. Syst. Sci. Daniel J. Hsu, Sham M. Kakade, Tong Zhang
2012	J	`jnl`	An Online Learning-based Framework for Tracking CoRR Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu
2012	J	`jnl`	Analysis of a randomized approximation scheme for matrix multiplication CoRR Daniel J. Hsu, Sham M. Kakade, Tong Zhang
2012	A*	`conf`	Convergence Rates for Differentially Private Statistical Estimation. ICML Kamalika Chaudhuri, Daniel J. Hsu
2012	J	`jnl`	Identifiability and Unmixing of Latent Parse Trees CoRR Daniel J. Hsu, Sham M. Kakade, Percy Liang
2012	—	`conf`	Identifiability and Unmixing of Latent Parse Trees. NIPS Daniel J. Hsu, Sham M. Kakade, Percy Liang
2012	J	`jnl`	Learning Gaussian Mixture Models: Moment Methods and Spectral Decompositions CoRR Daniel J. Hsu, Sham M. Kakade
2012	J	`jnl`	Learning High-Dimensional Mixtures of Graphical Models CoRR Animashree Anandkumar, Daniel J. Hsu, Sham M. Kakade
2012	J	`jnl`	Learning Linear Bayesian Networks with Latent Variables CoRR Animashree Anandkumar, Daniel J. Hsu, Adel Javanmard, Sham M. Kakade
2012	—	`conf`	Learning Mixtures of Tree Graphical Models. NIPS Animashree Anandkumar, Daniel J. Hsu, Furong Huang, Sham M. Kakade
2012	J	`jnl`	Learning Sparse Low-Threshold Linear Classifiers CoRR Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel J. Hsu, Tong Zhang
2012	A*	`conf`	Random Design Analysis of Ridge Regression. COLT Daniel J. Hsu, Sham M. Kakade, Tong Zhang
2012	J	`jnl`	Tensor decompositions for learning latent variable models CoRR Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky
2012	J	`jnl`	Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation CoRR Animashree Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-Kai Liu
2011	J	`jnl`	A tail inequality for quadratic forms of subgaussian random vectors CoRR Daniel J. Hsu, Sham M. Kakade, Tong Zhang
2011	J	`jnl`	An Analysis of Random Design Linear Regression CoRR Daniel J. Hsu, Sham M. Kakade, Tong Zhang
2011	J	`jnl`	Dimension-free tail inequalities for sums of random matrices. CoRR Daniel J. Hsu, Sham M. Kakade, Tong Zhang
2011	J	`jnl`	Efficient Optimal Learning for Contextual Bandits CoRR Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang
2011	A	`conf`	Efficient Optimal Learning for Contextual Bandits. UAI Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang
2011	J	`jnl`	Parallel Online Learning CoRR Daniel J. Hsu, Nikos Karampatziakis, John Langford, Alexander J. Smola
2011	J	`jnl`	Robust Matrix Decomposition With Sparse Corruptions. IEEE Trans. Inf. Theory Daniel J. Hsu, Sham M. Kakade, Tong Zhang
2011	A*	`conf`	Sample Complexity Bounds for Differentially Private Learning. COLT Kamalika Chaudhuri, Daniel J. Hsu
2011	J	`jnl`	Spectral Methods for Learning Multivariate Latent Tree Structure CoRR Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang
2011	—	`conf`	Spectral Methods for Learning Multivariate Latent Tree Structure. NIPS Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang
2011	J	`jnl`	Stochastic convex optimization with bandit feedback CoRR Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin
2011	—	`conf`	Stochastic convex optimization with bandit feedback. NIPS Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin
2010	J	`jnl`	Agnostic Active Learning Without Constraints CoRR Alina Beygelzimer, Daniel J. Hsu, John Langford, Tong Zhang
2010	—	`conf`	Agnostic Active Learning Without Constraints. NIPS Alina Beygelzimer, Daniel J. Hsu, John Langford, Tong Zhang
2010	—	`—`	Algorithms for active learning. Daniel J. Hsu
2010	A	`conf`	An Online Learning-based Framework for Tracking. UAI Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu
2010	J	`jnl`	Robust Matrix Decomposition with Outliers CoRR Daniel J. Hsu, Sham M. Kakade, Tong Zhang
2009	—	`conf`	A Parameter-free Hedging Algorithm. NIPS Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu
2009	A*	`conf`	A Spectral Algorithm for Learning Hidden Markov Models. COLT Daniel J. Hsu, Sham M. Kakade, Tong Zhang
2009	J	`jnl`	A parameter-free hedging algorithm CoRR Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu
2009	J	`jnl`	Multi-Label Prediction via Compressed Sensing CoRR Daniel J. Hsu, Sham M. Kakade, John Langford, Tong Zhang
2009	—	`conf`	Multi-Label Prediction via Compressed Sensing. NIPS Daniel J. Hsu, Sham M. Kakade, John Langford, Tong Zhang
2009	J	`jnl`	Tracking using explanation-based modeling CoRR Kamalika Chaudhuri, Yoav Freund, Daniel J. Hsu
2008	Misc	`conf`	A General Agnostic Active Learning Algorithm. ISAIM Sanjoy Dasgupta, Daniel J. Hsu, Claire Monteleoni
2008	J	`jnl`	A Spectral Algorithm for Learning Hidden Markov Models CoRR Daniel J. Hsu, Sham M. Kakade, Tong Zhang
2008	J	`jnl`	A new Hedging algorithm and its application to inferring latent random variables CoRR Yoav Freund, Daniel J. Hsu
2008	A*	`conf`	Hierarchical sampling for active learning. ICML Sanjoy Dasgupta, Daniel J. Hsu
2007	—	`conf`	A general agnostic active learning algorithm. NIPS Sanjoy Dasgupta, Daniel J. Hsu, Claire Monteleoni
2007	A*	`conf`	On-Line Estimation with the Multivariate Gaussian Distribution. COLT Sanjoy Dasgupta, Daniel J. Hsu
2006	A	`conf`	A Concentration Theorem for Projections. UAI Sanjoy Dasgupta, Daniel J. Hsu, Nakul Verma