More from the Same Authors

• 2021 Spotlight: On the Power of Differentiable Learning versus PAC and SQ Learning »
  Emmanuel Abbe · Pritish Kamath · Eran Malach · Colin Sandon · Nathan Srebro
• 2021 : Exponential Family Model-Based Reinforcement Learning via Score Matching »
  Gene Li · Junbo Li · Nathan Srebro · Zhaoran Wang · Zhuoran Yang
• 2021 Poster: On the Power of Differentiable Learning versus PAC and SQ Learning »
  Emmanuel Abbe · Pritish Kamath · Eran Malach · Colin Sandon · Nathan Srebro
• 2021 Oral: Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting »
  Frederic Koehler · Lijia Zhou · Danica J. Sutherland · Nathan Srebro
• 2021 Poster: Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting »
  Frederic Koehler · Lijia Zhou · Danica J. Sutherland · Nathan Srebro
• 2021 Poster: Representation Costs of Linear Neural Networks: Analysis and Design »
  Zhen Dai · Mina Karzand · Nathan Srebro
• 2021 Poster: An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning »
  Blake Woodworth · Nathan Srebro
• 2021 Poster: A Stochastic Newton Algorithm for Distributed Convex Optimization »
  Brian Bullins · Kshitij Patel · Ohad Shamir · Nathan Srebro · Blake Woodworth
• 2020 Poster: On Uniform Convergence and Low-Norm Interpolation Learning »
  Lijia Zhou · Danica J. Sutherland · Nati Srebro
• 2020 Poster: Reducing Adversarially Robust Learning to Non-Robust PAC Learning »
  Omar Montasser · Steve Hanneke · Nati Srebro
• 2020 Spotlight: On Uniform Convergence and Low-Norm Interpolation Learning »
  Lijia Zhou · Danica J. Sutherland · Nati Srebro
• 2020 Poster: Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy »
  Edward Moroshko · Blake Woodworth · Suriya Gunasekar · Jason Lee · Nati Srebro · Daniel Soudry
• 2020 Poster: Minibatch vs Local SGD for Heterogeneous Distributed Learning »
  Blake Woodworth · Kumar Kshitij Patel · Nati Srebro
• 2020 Spotlight: Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy »
  Edward Moroshko · Blake Woodworth · Suriya Gunasekar · Jason Lee · Nati Srebro · Daniel Soudry
• 2019 : Poster Session »
  Gergely Flamich · Shashanka Ubaru · Charles Zheng · Josip Djolonga · Kristoffer Wickstrøm · Diego Granziol · Konstantinos Pitas · Jun Li · Robert Williamson · Sangwoong Yoon · Kwot Sin Lee · Julian Zilly · Linda Petrini · Ian Fischer · Zhe Dong · Alexander Alemi · Bao-Ngoc Nguyen · Rob Brekelmans · Tailin Wu · Aditya Mahajan · Alexander Li · Kirankumar Shiragur · Yair Carmon · Linara Adilova · SHIYU LIU · Bang An · Sanjeeb Dash · Oktay Gunluk · Arya Mazumdar · Mehul Motani · Julia Rosenzweig · Michael Kamp · Marton Havasi · Leighton P Barnes · Zhengqing Zhou · Yi Hao · Dylan Foster · Yuval Benjamini · Nati Srebro · Michael Tschannen · Paul Rubenstein · Sylvain Gelly · John Duchi · Aaron Sidford · Robin Ru · Stefan Zohren · Murtaza Dalal · Michael A Osborne · Stephen J Roberts · Moses Charikar · Jayakumar Subramanian · Xiaodi Fan · Max Schwarzer · Nicholas Roberts · Simon Lacoste-Julien · Vinay Prabhu · Aram Galstyan · Greg Ver Steeg · Lalitha Sankar · Yung-Kyun Noh · Gautam Dasarathy · Frank Park · Ngai-Man (Man) Cheung · Ngoc-Trung Tran · Linxiao Yang · Ben Poole · Andrea Censi · Tristan Sylvain · R Devon Hjelm · Bangjie Liu · Jose Gallego-Posada · Tyler Sypherd · Kai Yang · Jan Nikolas Morshuis
• 2018 Poster: Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization »
  Blake Woodworth · Jialei Wang · Adam Smith · Brendan McMahan · Nati Srebro
• 2018 Spotlight: Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization »
  Blake Woodworth · Jialei Wang · Adam Smith · Brendan McMahan · Nati Srebro
• 2018 Poster: Implicit Bias of Gradient Descent on Linear Convolutional Networks »
  Suriya Gunasekar · Jason Lee · Daniel Soudry · Nati Srebro
• 2018 Poster: The Everlasting Database: Statistical Validity at a Fair Price »
  Blake Woodworth · Vitaly Feldman · Saharon Rosset · Nati Srebro
• 2018 Poster: On preserving non-discrimination when combining expert advice »
  Avrim Blum · Suriya Gunasekar · Thodoris Lykouris · Nati Srebro
• 2017 Poster: The Marginal Value of Adaptive Gradient Methods in Machine Learning »
  Ashia C Wilson · Becca Roelofs · Mitchell Stern · Nati Srebro · Benjamin Recht
• 2017 Oral: The Marginal Value of Adaptive Gradient Methods in Machine Learning »
  Ashia C Wilson · Becca Roelofs · Mitchell Stern · Nati Srebro · Benjamin Recht
• 2017 Poster: Stochastic Approximation for Canonical Correlation Analysis »
  Raman Arora · Teodor Vanislavov Marinov · Poorya Mianjy · Nati Srebro
• 2017 Poster: Exploring Generalization in Deep Learning »
  Behnam Neyshabur · Srinadh Bhojanapalli · David Mcallester · Nati Srebro
• 2017 Poster: Implicit Regularization in Matrix Factorization »
  Suriya Gunasekar · Blake Woodworth · Srinadh Bhojanapalli · Behnam Neyshabur · Nati Srebro
• 2017 Spotlight: Implicit Regularization in Matrix Factorization »
  Suriya Gunasekar · Blake Woodworth · Srinadh Bhojanapalli · Behnam Neyshabur · Nati Srebro
• 2016 Poster: Tight Complexity Bounds for Optimizing Composite Objectives »
  Blake Woodworth · Nati Srebro
• 2016 Poster: Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis »
  Weiran Wang · Jialei Wang · Dan Garber · Dan Garber · Nati Srebro
• 2016 Poster: Global Optimality of Local Search for Low Rank Matrix Recovery »
  Srinadh Bhojanapalli · Behnam Neyshabur · Nati Srebro
• 2016 Poster: Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations »
  Behnam Neyshabur · Yuhuai Wu · Russ Salakhutdinov · Nati Srebro
• 2016 Poster: Equality of Opportunity in Supervised Learning »
  Moritz Hardt · Eric Price · Eric Price · Nati Srebro
• 2016 Poster: Normalized Spectral Map Synchronization »
  Yanyao Shen · Qixing Huang · Nati Srebro · Sujay Sanghavi
• 2015 Poster: Path-SGD: Path-Normalized Optimization in Deep Neural Networks »
  Behnam Neyshabur · Russ Salakhutdinov · Nati Srebro
• 2014 Poster: Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm »
  Deanna Needell · Rachel Ward · Nati Srebro
• 2013 Workshop: Learning Faster From Easy Data »
  Peter Grünwald · Wouter M Koolen · Sasha Rakhlin · Nati Srebro · Alekh Agarwal · Karthik Sridharan · Tim van Erven · Sebastien Bubeck
• 2013 Workshop: Large Scale Matrix Analysis and Inference »
  Reza Zadeh · Gunnar Carlsson · Michael Mahoney · Manfred K. Warmuth · Wouter M Koolen · Nati Srebro · Satyen Kale · Malik Magdon-Ismail · Ashish Goel · Matei A Zaharia · David Woodruff · Ioannis Koutis · Benjamin Recht
• 2013 Poster: Stochastic Optimization of PCA with Capped MSG »
  Raman Arora · Andrew Cotter · Nati Srebro
• 2013 Poster: Auditing: Active Learning with Outcome-Dependent Query Costs »
  Sivan Sabato · Anand D Sarwate · Nati Srebro
• 2013 Poster: The Power of Asymmetry in Binary Hashing »
  Behnam Neyshabur · Nati Srebro · Russ Salakhutdinov · Yury Makarychev · Payman Yadollahpour
• 2012 Poster: Sparse Prediction with the $k$-Support Norm »
  Andreas Argyriou · Rina Foygel · Nati Srebro
• 2012 Spotlight: Sparse Prediction with the $k$-Support Norm »
  Andreas Argyriou · Rina Foygel · Nati Srebro
• 2012 Poster: Relax and Randomize : From Value to Algorithms »
  Sasha Rakhlin · Ohad Shamir · Karthik Sridharan
• 2012 Poster: Matrix reconstruction with the local max norm »
  Rina Foygel · Nati Srebro · Russ Salakhutdinov
• 2012 Oral: Relax and Randomize : From Value to Algorithms »
  Sasha Rakhlin · Ohad Shamir · Karthik Sridharan
• 2012 Poster: Feature Clustering for Accelerating Parallel Coordinate Descent »
  Chad Scherrer · Ambuj Tewari · Mahantesh Halappanavar · David Haglin
• 2011 Poster: Beating SGD: Learning SVMs in Sublinear Time »
  Elad Hazan · Tomer Koren · Nati Srebro
• 2011 Poster: Better Mini-Batch Algorithms via Accelerated Gradient Methods »
  Andrew Cotter · Ohad Shamir · Nati Srebro · Karthik Sridharan
• 2011 Poster: Greedy Algorithms for Structurally Constrained High Dimensional Problems »
  Ambuj Tewari · Pradeep Ravikumar · Inderjit Dhillon
• 2011 Poster: Learning with the weighted trace-norm under arbitrary sampling distributions »
  Rina Foygel · Russ Salakhutdinov · Ohad Shamir · Nati Srebro
• 2011 Poster: Nearest Neighbor based Greedy Coordinate Descent »
  Inderjit Dhillon · Pradeep Ravikumar · Ambuj Tewari
• 2011 Poster: Online Learning: Stochastic, Constrained, and Smoothed Adversaries »
  Sasha Rakhlin · Karthik Sridharan · Ambuj Tewari
• 2011 Poster: Orthogonal Matching Pursuit with Replacement »
  Prateek Jain · Ambuj Tewari · Inderjit Dhillon
• 2010 Session: Spotlights Session 11 »
  Nati Srebro
• 2010 Session: Oral Session 13 »
  Nati Srebro
• 2010 Poster: Tight Sample Complexity of Large-Margin Learning »
  Sivan Sabato · Nati Srebro · Naftali Tishby
• 2010 Poster: Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm »
  Russ Salakhutdinov · Nati Srebro
• 2010 Oral: Online Learning: Random Averages, Combinatorial Parameters, and Learnability »
  Sasha Rakhlin · Karthik Sridharan · Ambuj Tewari
• 2010 Poster: Practical Large-Scale Optimization for Max-norm Regularization »
  Jason D Lee · Benjamin Recht · Russ Salakhutdinov · Nati Srebro · Joel A Tropp
• 2010 Poster: Online Learning: Random Averages, Combinatorial Parameters, and Learnability »
  Sasha Rakhlin · Karthik Sridharan · Ambuj Tewari
• 2010 Poster: Smoothness, Low Noise and Fast Rates »
  Nati Srebro · Karthik Sridharan · Ambuj Tewari
• 2009 Workshop: Understanding Multiple Kernel Learning Methods »
  Brian McFee · Gert Lanckriet · Francis Bach · Nati Srebro
• 2009 Poster: Statistical Analysis of Semi-Supervised Learning: The Limit of Infinite Unlabelled Data »
  Boaz Nadler · Nati Srebro · Xueyuan Zhou
• 2009 Spotlight: Statistical Analysis of Semi-Supervised Learning: The Limit of Infinite Unlabelled Data »
  Boaz Nadler · Nati Srebro · Xueyuan Zhou
• 2008 Poster: Fast Rates for Regularized Objectives »
  Karthik Sridharan · Shai Shalev-Shwartz · Nati Srebro
• 2008 Poster: On the Generalization Ability of Online Strongly Convex Programming Algorithms »
  Sham M Kakade · Ambuj Tewari
• 2008 Spotlight: On the Generalization Ability of Online Strongly Convex Programming Algorithms »
  Sham M Kakade · Ambuj Tewari
• 2008 Poster: On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization »
  Sham M Kakade · Karthik Sridharan · Ambuj Tewari
• 2007 Poster: Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs »
  Ambuj Tewari · Peter Bartlett
• 2006 Poster: Sample Complexity of Policy Search with Known Dynamics »
  Peter Bartlett · Ambuj Tewari