More from the Same Authors

• 2020 : Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics »
  Bo Cowgill · Fabrizio Dell'Acqua · Augustin Chaintreau · Nakul Verma · Samuel Deng · Daniel Hsu
• 2021 Spotlight: Bayesian decision-making under misspecified priors with applications to meta-learning »
  Max Simchowitz · Christopher Tosh · Akshay Krishnamurthy · Daniel Hsu · Thodoris Lykouris · Miro Dudik · Robert Schapire
• 2022 Poster: Masked Prediction: A Parameter Identifiability View »
  Bingbin Liu · Daniel Hsu · Pradeep Ravikumar · Andrej Risteski
• 2021 Poster: Support vector machines and linear regression coincide with very high-dimensional features »
  Navid Ardeshir · Clayton Sanford · Daniel Hsu
• 2021 Poster: Bayesian decision-making under misspecified priors with applications to meta-learning »
  Max Simchowitz · Christopher Tosh · Akshay Krishnamurthy · Daniel Hsu · Thodoris Lykouris · Miro Dudik · Robert Schapire
• 2020 Poster: Ensuring Fairness Beyond the Training Data »
  Debmalya Mandal · Samuel Deng · Suman Jana · Jeannette Wing · Daniel Hsu
• 2019 Poster: On the number of variables to use in principal component regression »
  Ji Xu · Daniel Hsu
• 2018 Poster: Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate »
  Mikhail Belkin · Daniel Hsu · Partha P Mitra
• 2018 Poster: Benefits of over-parameterization with EM »
  Ji Xu · Daniel Hsu · Arian Maleki
• 2018 Poster: Leveraged volume sampling for linear regression »
  Michal Derezinski · Manfred K. Warmuth · Daniel Hsu
• 2018 Spotlight: Leveraged volume sampling for linear regression »
  Michal Derezinski · Manfred K. Warmuth · Daniel Hsu
• 2017 Poster: Linear regression without correspondence »
  Daniel Hsu · Kevin Shi · Xiaorui Sun
• 2016 Poster: Exact Recovery of Hard Thresholding Pursuit »
  Xiaotong Yuan · Ping Li · Tong Zhang
• 2016 Poster: Learning Additive Exponential Family Graphical Models via $\ell_{2,1}$-norm Regularized M-Estimation »
  Xiaotong Yuan · Ping Li · Tong Zhang · Qingshan Liu · Guangcan Liu
• 2016 Poster: Global Analysis of Expectation Maximization for Mixtures of Two Gaussians »
  Ji Xu · Daniel Hsu · Arian Maleki
• 2016 Oral: Global Analysis of Expectation Maximization for Mixtures of Two Gaussians »
  Ji Xu · Daniel Hsu · Arian Maleki
• 2016 Poster: Search Improves Label for Active Learning »
  Alina Beygelzimer · Daniel Hsu · John Langford · Chicheng Zhang
• 2015 Poster: Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path »
  Daniel Hsu · Aryeh Kontorovich · Csaba Szepesvari
• 2015 Poster: Efficient and Parsimonious Agnostic Active Learning »
  Tzu-Kuo Huang · Alekh Agarwal · Daniel Hsu · John Langford · Robert Schapire
• 2015 Spotlight: Efficient and Parsimonious Agnostic Active Learning »
  Tzu-Kuo Huang · Alekh Agarwal · Daniel Hsu · John Langford · Robert Schapire
• 2015 Poster: Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling »
  Zheng Qu · Peter Richtarik · Tong Zhang
• 2015 Poster: Local Smoothness in Variance Reduced Optimization »
  Daniel Vainsencher · Han Liu · Tong Zhang
• 2015 Poster: Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding »
  Rie Johnson · Tong Zhang
• 2015 Spotlight: Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding »
  Rie Johnson · Tong Zhang
• 2015 Poster: Online Gradient Boosting »
  Alina Beygelzimer · Elad Hazan · Satyen Kale · Haipeng Luo
• 2014 Poster: Scalable Non-linear Learning with Adaptive Polynomial Expansions »
  Alekh Agarwal · Alina Beygelzimer · Daniel Hsu · John Langford · Matus J Telgarsky
• 2014 Poster: The Large Margin Mechanism for Differentially Private Maximization »
  Kamalika Chaudhuri · Daniel Hsu · Shuang Song
• 2013 Workshop: Workshop on Spectral Learning »
  Byron Boots · Daniel Hsu · Borja Balle
• 2013 Poster: Accelerating Stochastic Gradient Descent using Predictive Variance Reduction »
  Rie Johnson · Tong Zhang
• 2013 Poster: Accelerated Mini-Batch Stochastic Dual Coordinate Ascent »
  Shai Shalev-Shwartz · Tong Zhang
• 2013 Poster: When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity »
  Anima Anandkumar · Daniel Hsu · Majid Janzamin · Sham M Kakade
• 2013 Poster: Contrastive Learning Using Spectral Methods »
  James Y Zou · Daniel Hsu · David Parkes · Ryan Adams
• 2012 Workshop: Modern Nonparametric Methods in Machine Learning »
  Sivaraman Balakrishnan · Arthur Gretton · Mladen Kolar · John Lafferty · Han Liu · Tong Zhang
• 2012 Poster: Selective Labeling via Error Bound Minimization »
  Quanquan Gu · Tong Zhang · Chris Ding · Jiawei Han
• 2012 Poster: Learning Mixtures of Tree Graphical Models »
  Anima Anandkumar · Daniel Hsu · Furong Huang · Sham M Kakade
• 2012 Poster: A Spectral Algorithm for Latent Dirichlet Allocation »
  Anima Anandkumar · Dean P Foster · Daniel Hsu · Sham M Kakade · Yi-Kai Liu
• 2012 Poster: Identifiability and Unmixing of Latent Parse Trees »
  Percy Liang · Sham M Kakade · Daniel Hsu
• 2012 Spotlight: A Spectral Algorithm for Latent Dirichlet Allocation »
  Anima Anandkumar · Dean P Foster · Daniel Hsu · Sham M Kakade · Yi-Kai Liu
• 2011 Poster: Stochastic convex optimization with bandit feedback »
  Alekh Agarwal · Dean P Foster · Daniel Hsu · Sham M Kakade · Sasha Rakhlin
• 2011 Poster: Learning to Search Efficiently in High Dimensions »
  Zhen Li · Huazhong Ning · Liangliang Cao · Tong Zhang · Yihong Gong · Thomas S Huang
• 2011 Poster: Spectral Methods for Learning Multivariate Latent Tree Structure »
  Anima Anandkumar · Kamalika Chaudhuri · Daniel Hsu · Sham M Kakade · Le Song · Tong Zhang
• 2011 Poster: Greedy Model Averaging »
  Dong Dai · Tong Zhang
• 2010 Spotlight: Learning from Logged Implicit Exploration Data »
  Alex Strehl · Lihong Li · John Langford · Sham M Kakade
• 2010 Poster: Learning from Logged Implicit Exploration Data »
  Alexander L Strehl · John Langford · Lihong Li · Sham M Kakade
• 2010 Poster: Deep Coding Network »
  Yuanqing Lin · Tong Zhang · Shenghuo Zhu · Kai Yu
• 2009 Poster: A Parameter-free Hedging Algorithm »
  Kamalika Chaudhuri · Yoav Freund · Daniel Hsu
• 2009 Poster: Multi-Label Prediction via Compressed Sensing »
  Daniel Hsu · Sham M Kakade · John Langford · Tong Zhang
• 2009 Poster: Nonlinear Learning using Local Coordinate Coding »
  Kai Yu · Tong Zhang · Yihong Gong
• 2009 Poster: Slow Learners are Fast »
  Martin A Zinkevich · Alexander Smola · John Langford
• 2009 Oral: Multi-Label Prediction via Compressed Sensing »
  Daniel Hsu · Sham M Kakade · John Langford · Tong Zhang
• 2008 Poster: Adaptive Forward-Backward Greedy Algorithm for Sparse Learning with Linear Models »
  Tong Zhang
• 2008 Oral: Adaptive Forward-Backward Greedy Algorithm for Sparse Learning with Linear Models »
  Tong Zhang
• 2008 Poster: Sparse Online Learning via Truncated Gradient »
  John Langford · Lihong Li · Tong Zhang
• 2008 Spotlight: Sparse Online Learning via Truncated Gradient »
  John Langford · Lihong Li · Tong Zhang
• 2008 Poster: Multi-stage Convex Relaxation for Learning with Sparse Regularization »
  Tong Zhang
• 2007 Workshop: Principles of Learning Problem Design »
  John Langford · Alina Beygelzimer
• 2007 Spotlight: A general agnostic active learning algorithm »
  Sanjoy Dasgupta · Daniel Hsu · Claire Monteleoni
• 2007 Poster: A general agnostic active learning algorithm »
  Sanjoy Dasgupta · Daniel Hsu · Claire Monteleoni
• 2007 Poster: A General Boosting Method and its Application to Learning Ranking Functions for Web Search »
  Zhaohui Zheng · Hongyuan Zha · Tong Zhang · Olivier Chapelle · Keke Chen · Gordon Sun
• 2007 Poster: The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information »
  John Langford · Tong Zhang
• 2006 Poster: Learning on Graph with Laplacian Regularization »
  Rie Ando · Tong Zhang