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papers

Publications (50)

cs.LG2011

Spectral Methods for Learning Multivariate Latent Tree Structure

Animashree Anandkumar, Kamalika Chaudhuri, Daniel Hsu +3

cs.GT2008

A new Hedging algorithm and its application to inferring latent random variables

Yoav Freund, Daniel Hsu

math.PR2011

Dimension-free tail inequalities for sums of random matrices

Daniel Hsu, Sham M. Kakade, Tong Zhang

cs.LG2012

A Spectral Algorithm for Learning Hidden Markov Models

Daniel Hsu, Sham M. Kakade, Tong Zhang

astro-ph.CO2018

Non-Gaussian information from weak lensing data via deep learning

Arushi Gupta, José Manuel Zorrilla Matilla, Daniel Hsu +1

cs.LG2018

Benefits of over-parameterization with EM

Ji Xu, Daniel Hsu, Arian Maleki

cs.LG2018

Correcting the bias in least squares regression with volume-rescaled sampling

Michał Dereziński, Manfred K. Warmuth, Daniel Hsu

cs.LG2012

Learning mixtures of spherical Gaussians: moment methods and spectral decompositions

Daniel Hsu, Sham M. Kakade

cs.LG2012

Convergence Rates for Differentially Private Statistical Estimation

Kamalika Chaudhuri, Daniel Hsu

stat.ML2017

Kernel Approximation Methods for Speech Recognition

Avner May, Alireza Bagheri Garakani, Zhiyun Lu +9

cs.LG2016

Search Improves Label for Active Learning

Alina Beygelzimer, Daniel Hsu, John Langford +1

cs.LG2014

The Large Margin Mechanism for Differentially Private Maximization

Kamalika Chaudhuri, Daniel Hsu, Shuang Song

cs.LG2015

Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path

Daniel Hsu, Aryeh Kontorovich, Csaba Szepesvári

cs.LG2014

Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits

Alekh Agarwal, Daniel Hsu, Satyen Kale +3

stat.ML2018

Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate

Mikhail Belkin, Daniel Hsu, Partha Mitra

cs.NE2018

Multi-period Time Series Modeling with Sparsity via Bayesian Variational Inference

Daniel Hsu

math.ST2014

Random design analysis of ridge regression

Daniel Hsu, Sham M. Kakade, Tong Zhang

cs.CY2016

FairTest: Discovering Unwarranted Associations in Data-Driven Applications

Florian Tramèr, Vaggelis Atlidakis, Roxana Geambasu +5

cs.LG2010

Agnostic Active Learning Without Constraints

Alina Beygelzimer, Daniel Hsu, John Langford +1

cs.LG2009

Multi-Label Prediction via Compressed Sensing

Daniel Hsu, Sham M. Kakade, John Langford +1

cs.LG2012

An Online Learning-based Framework for Tracking

Kamalika Chaudhuri, Yoav Freund, Daniel Hsu

cs.LG2011

Efficient Optimal Learning for Contextual Bandits

Miroslav Dudik, Daniel Hsu, Satyen Kale +4

cs.DS2014

Weighted sampling of outer products

Daniel Hsu

math.PR2011

A tail inequality for quadratic forms of subgaussian random vectors

Daniel Hsu, Sham M. Kakade, Tong Zhang

cs.LG2010

Tracking using explanation-based modeling

Kamalika Chaudhuri, Yoav Freund, Daniel Hsu

cs.NE2017

Time Series Compression Based on Adaptive Piecewise Recurrent Autoencoder

Daniel Hsu

cs.LG2012

A Method of Moments for Mixture Models and Hidden Markov Models

Animashree Anandkumar, Daniel Hsu, Sham M. Kakade

stat.ML2019

Certified Robustness to Adversarial Examples with Differential Privacy

Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu +2

cs.LG2017

Linear regression without correspondence

Daniel Hsu, Kevin Shi, Xiaorui Sun

cs.LG2010

A parameter-free hedging algorithm

Kamalika Chaudhuri, Yoav Freund, Daniel Hsu

math.ST2016

Kernel ridge vs. principal component regression: minimax bounds and adaptability of regularization operators

Lee H. Dicker, Dean P. Foster, Daniel Hsu

math.ST2017

Mixing time estimation in reversible Markov chains from a single sample path

Daniel Hsu, Aryeh Kontorovich, David A. Levin +2

cs.DS2016

Greedy bi-criteria approximations for $k$-medians and $k$-means

Daniel Hsu, Matus Telgarsky

stat.ML2016

Learning Sparse Low-Threshold Linear Classifiers

Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro +2

cs.LG2012

A concentration theorem for projections

Sanjoy Dasgupta, Daniel Hsu, Nakul Verma

cs.LG2018

Leveraged volume sampling for linear regression

Michał Dereziński, Manfred K. Warmuth, Daniel Hsu

math.ST2016

Global analysis of Expectation Maximization for mixtures of two Gaussians

Ji Xu, Daniel Hsu, Arian Maleki

cs.LG2019

A gradual, semi-discrete approach to generative network training via explicit Wasserstein minimization

Yucheng Chen, Matus Telgarsky, Chao Zhang +3

cs.LG2013

A Spectral Algorithm for Latent Dirichlet Allocation

Animashree Anandkumar, Dean P. Foster, Daniel Hsu +2

cond-mat.supr-con2009

Modulation of superconductivity by spin canting in a hybrid antiferromagnet/superconductor oxide

Awadhesh Mani, T. Geetha Kumary, Daniel Hsu +2

cond-mat.str-el2012

Interplay between the magnetic and electric degrees-of-freedom in multiferroic Co3TeO6

Wen-Hsien Li, Chin-Wei Wang, Daniel Hsu +8

cs.LG2013

A Tensor Approach to Learning Mixed Membership Community Models

Anima Anandkumar, Rong Ge, Daniel Hsu +1

cs.LG2011

Parallel Online Learning

Daniel Hsu, Nikos Karampatziakis, John Langford +1

math.ST2017

Parameter identification in Markov chain choice models

Arushi Gupta, Daniel Hsu

astro-ph.CO2016

Do dark matter halos explain lensing peaks?

José Manuel Zorrilla Matilla, Zoltán Haiman, Daniel Hsu +2

cs.DS2018

Coding sets with asymmetric information

Alexandr Andoni, Javad Ghaderi, Daniel Hsu +2

cs.LG2016

Loss minimization and parameter estimation with heavy tails

Daniel Hsu, Sivan Sabato

cs.LG2014

Scalable Nonlinear Learning with Adaptive Polynomial Expansions

Alekh Agarwal, Alina Beygelzimer, Daniel Hsu +2

stat.ML2012

Identifiability and Unmixing of Latent Parse Trees

Daniel Hsu, Sham M. Kakade, Percy Liang

stat.ML2013

Learning Topic Models and Latent Bayesian Networks Under Expansion Constraints

Animashree Anandkumar, Daniel Hsu, Adel Javanmard +1