Nathan Srebro - RankMe

273 papers A* 74A 15B 4C 1Misc 1Journal 155Unranked 22

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Overfitting and Generalizing with (PAC) Bayesian Prediction in Noisy Binary Classification. CoRR Xiaohan Zhu, Mesrob I. Ohannessian, Nathan Srebro
2026	J	`jnl`	Positive Distribution Shift as a Framework for Understanding Tractable Learning. CoRR Marko Medvedev, Idan Attias, Elisabetta Cornacchia, Theodor Misiakiewicz, Gal Vardi, Nathan Srebro
2026	J	`jnl`	Recursive Models for Long-Horizon Reasoning. CoRR Chenxiao Yang, Nathan Srebro, Zhiyuan Li
2025	A*	`conf`	A Theory of Learning with Autoregressive Chain of Thought. COLT Nirmit Joshi, Gal Vardi, Adam Block, Surbhi Goel, Zhiyuan Li, Theodor Misiakiewicz, Nathan Srebro
2025	J	`jnl`	A Theory of Learning with Autoregressive Chain of Thought. CoRR Nirmit Joshi, Gal Vardi, Adam Block, Surbhi Goel, Zhiyuan Li, Theodor Misiakiewicz, Nathan Srebro
2025	J	`jnl`	Better Rates for Random Task Orderings in Continual Linear Models. CoRR Itay Evron, Ran Levinstein, Matan Schliserman, Uri Sherman, Tomer Koren, Daniel Soudry, Nathan Srebro
2025	J	`jnl`	Learning single-index models via harmonic decomposition. CoRR Nirmit Joshi, Hugo Koubbi, Theodor Misiakiewicz, Nathan Srebro
2025	J	`jnl`	Learning to Answer from Correct Demonstrations. CoRR Nirmit Joshi, Gene Li, Siddharth Bhandari, Shiva Prasad Kasiviswanathan, Cong Ma, Nathan Srebro
2025	J	`jnl`	On the Hardness of Learning Regular Expressions. CoRR Idan Attias, Lev Reyzin, Nathan Srebro, Gal Vardi
2025	A*	`conf`	PENCIL: Long Thoughts with Short Memory. ICML Chenxiao Yang, Nathan Srebro, David McAllester, Zhiyuan Li
2025	J	`jnl`	PENCIL: Long Thoughts with Short Memory. CoRR Chenxiao Yang, Nathan Srebro, David McAllester, Zhiyuan Li
2025	A*	`conf`	Quantifying Overfitting along the Regularization Path for Two-Part-Code MDL in Supervised Classification. COLT Xiaohan Zhu, Nathan Srebro
2025	J	`jnl`	Quantifying Overfitting along the Regularization Path for Two-Part-Code MDL in Supervised Classification. CoRR Xiaohan Zhu, Nathan Srebro
2025	J	`jnl`	Research Program: Theory of Learning in Dynamical Systems. CoRR Elad Hazan, Shai Shalev-Shwartz, Nathan Srebro
2025	J	`jnl`	Shift is Good: Mismatched Data Mixing Improves Test Performance. CoRR Marko Medvedev, Kaifeng Lyu, Zhiyuan Li, Nathan Srebro
2025	J	`jnl`	Temperature is All You Need for Generalization in Langevin Dynamics and other Markov Processes. CoRR Itamar Harel, Yonathan Wolanowsky, Gal Vardi, Nathan Srebro, Daniel Soudry
2025	J	`jnl`	Tight Bounds on the Binomial CDF, and the Minimum of i.i.d Binomials, in terms of KL-Divergence. CoRR Xiaohan Zhu, Mesrob I. Ohannessian, Nathan Srebro
2025	A*	`conf`	Weak-to-Strong Generalization Even in Random Feature Networks, Provably. ICML Marko Medvedev, Kaifeng Lyu, Dingli Yu, Sanjeev Arora, Zhiyuan Li, Nathan Srebro
2025	J	`jnl`	Weak-to-Strong Generalization Even in Random Feature Networks, Provably. CoRR Marko Medvedev, Kaifeng Lyu, Dingli Yu, Sanjeev Arora, Zhiyuan Li, Nathan Srebro
2024	A*	`conf`	An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression. ICLR Lijia Zhou, James B. Simon, Gal Vardi, Nathan Srebro
2024	A*	`conf`	Depth Separation in Norm-Bounded Infinite-Width Neural Networks. COLT Suzanna Parkinson, Greg Ongie, Rebecca Willett, Ohad Shamir, Nathan Srebro
2024	J	`jnl`	Depth Separation in Norm-Bounded Infinite-Width Neural Networks. CoRR Suzanna Parkinson, Greg Ongie, Rebecca Willett, Ohad Shamir, Nathan Srebro
2024	A*	`conf`	How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers. ICML Gon Buzaglo, Itamar Harel, Mor Shpigel Nacson, Alon Brutzkus, Nathan Srebro, Daniel Soudry
2024	J	`jnl`	How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers. CoRR Gon Buzaglo, Itamar Harel, Mor Shpigel Nacson, Alon Brutzkus, Nathan Srebro, Daniel Soudry
2024	A*	`conf`	Metalearning with Very Few Samples Per Task. COLT Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Nathan Srebro, Jonathan R. Ullman
2024	A*	`conf`	Noisy Interpolation Learning with Shallow Univariate ReLU Networks. ICLR Nirmit Joshi, Gal Vardi, Nathan Srebro
2024	J	`jnl`	On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries. CoRR Nirmit Joshi, Theodor Misiakiewicz, Nathan Srebro
2024	J	`jnl`	Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality. CoRR Marko Medvedev, Gal Vardi, Nathan Srebro
2024	J	`jnl`	Provable Tempered Overfitting of Minimal Nets and Typical Nets. CoRR Itamar Harel, William M. Hoza, Gal Vardi, Itay Evron, Nathan Srebro, Daniel Soudry
2024	J	`jnl`	Score Design for Multi-Criteria Incentivization. CoRR Anmol Kabra, Mina Karzand, Tosca Lechner, Nathan Srebro, Serena Wang
2024	A*	`conf`	The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication. COLT Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari, Lingxiao Wang, Sebastian U. Stich, Ziheng Cheng, Nirmit Joshi, Nathan Srebro
2024	J	`jnl`	The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication. CoRR Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari, Lingxiao Wang, Sebastian U. Stich, Ziheng Cheng, Nirmit Joshi, Nathan Srebro
2024	J	`jnl`	The Price of Implicit Bias in Adversarially Robust Generalization. CoRR Nikolaos Tsilivis, Natalie Frank, Nathan Srebro, Julia Kempe
2023	J	`jnl`	An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression. CoRR Lijia Zhou, James B. Simon, Gal Vardi, Nathan Srebro
2023	J	`jnl`	Applying statistical learning theory to deep learning. CoRR Cédric Gerbelot, Avetik Karagulyan, Stefani Karp, Kavya Ravichandran, Menachem Stern, Nathan Srebro
2023	A*	`conf`	Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization. COLT Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro
2023	J	`jnl`	Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization. CoRR Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro
2023	A*	`conf`	Continual Learning in Linear Classification on Separable Data. ICML Itay Evron, Edward Moroshko, Gon Buzaglo, Maroun Khriesh, Badea Marjieh, Nathan Srebro, Daniel Soudry
2023	J	`jnl`	Continual Learning in Linear Classification on Separable Data. CoRR Itay Evron, Edward Moroshko, Gon Buzaglo, Maroun Khriesh, Badea Marjieh, Nathan Srebro, Daniel Soudry
2023	J	`jnl`	Efficiently Learning Neural Networks: What Assumptions May Suffice? CoRR Amit Daniely, Nathan Srebro, Gal Vardi
2023	A*	`conf`	Federated Online and Bandit Convex Optimization. ICML Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha, Nathan Srebro
2023	A*	`conf`	Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. ICLR Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu
2023	J	`jnl`	Interpolation Learning With Minimum Description Length. CoRR Naren Sarayu Manoj, Nathan Srebro
2023	J	`jnl`	Lower bounds for non-convex stochastic optimization. Math. Program. Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth
2023	J	`jnl`	Most Neural Networks Are Almost Learnable. CoRR Amit Daniely, Nathan Srebro, Gal Vardi
2023	J	`jnl`	Noisy Interpolation Learning with Shallow Univariate ReLU Networks. CoRR Nirmit Joshi, Gal Vardi, Nathan Srebro
2023	A*	`conf`	Shortest Program Interpolation Learning. COLT Naren Sarayu Manoj, Nathan Srebro
2023	J	`jnl`	The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks. CoRR Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro
2023	J	`jnl`	Uniform Convergence with Square-Root Lipschitz Loss. CoRR Lijia Zhou, Zhen Dai, Frederic Koehler, Nathan Srebro
2023	J	`jnl`	When is Agnostic Reinforcement Learning Statistically Tractable? CoRR Zeyu Jia, Gene Li, Alexander Rakhlin, Ayush Sekhari, Nathan Srebro
2022	J	`jnl`	A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. CoRR Lijia Zhou, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nathan Srebro
2022	J	`jnl`	Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization. CoRR Omar Montasser, Steve Hanneke, Nathan Srebro
2022	A*	`conf`	How catastrophic can catastrophic forgetting be in linear regression? COLT Itay Evron, Edward Moroshko, Rachel A. Ward, Nathan Srebro, Daniel Soudry
2022	J	`jnl`	Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. CoRR Spencer Frei, Gal Vardi, Peter L. Bartlett, Nathan Srebro, Wei Hu
2022	A*	`conf`	Implicit Bias of the Step Size in Linear Diagonal Neural Networks. ICML Mor Shpigel Nacson, Kavya Ravichandran, Nathan Srebro, Daniel Soudry
2022	J	`jnl`	Pessimism for Offline Linear Contextual Bandits using 𝓁 CoRR Gene Li, Cong Ma, Nathan Srebro
2022	A*	`conf`	The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (Extended Abstract). IJCAI Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
2022	J	`jnl`	The Sample Complexity of One-Hidden-Layer Neural Networks. CoRR Gal Vardi, Ohad Shamir, Nathan Srebro
2022	J	`jnl`	Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization. CoRR Idan Amir, Roi Livni, Nathan Srebro
2022	A	`conf`	Transductive Robust Learning Guarantees. AISTATS Omar Montasser, Steve Hanneke, Nathan Srebro
2021	A*	`conf`	A Stochastic Newton Algorithm for Distributed Convex Optimization. NeurIPS Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth
2021	J	`jnl`	A Stochastic Newton Algorithm for Distributed Convex Optimization. CoRR Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth
2021	A*	`conf`	Adversarially Robust Learning with Unknown Perturbation Sets. COLT Omar Montasser, Steve Hanneke, Nathan Srebro
2021	J	`jnl`	Adversarially Robust Learning with Unknown Perturbation Sets. CoRR Omar Montasser, Steve Hanneke, Nathan Srebro
2021	A*	`conf`	An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning. NeurIPS Blake E. Woodworth, Nathan Srebro
2021	J	`jnl`	An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning. CoRR Blake E. Woodworth, Nathan Srebro
2021	J	`jnl`	An accelerated communication-efficient primal-dual optimization framework for structured machine learning. Optim. Methods Softw. Chenxin Ma, Martin Jaggi, Frank E. Curtis, Nathan Srebro, Martin Takác
2021	A	`conf`	Does Invariant Risk Minimization Capture Invariance? AISTATS Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro
2021	J	`jnl`	Does Invariant Risk Minimization Capture Invariance? CoRR Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, Nathan Srebro
2021	A*	`conf`	Dropout: Explicit Forms and Capacity Control. ICML Raman Arora, Peter L. Bartlett, Poorya Mianjy, Nathan Srebro
2021	J	`jnl`	Eluder Dimension and Generalized Rank. CoRR Gene Li, Pritish Kamath, Dylan J. Foster, Nathan Srebro
2021	J	`jnl`	Exponential Family Model-Based Reinforcement Learning via Score Matching. CoRR Gene Li, Junbo Li, Nathan Srebro, Zhaoran Wang, Zhuoran Yang
2021	J	`jnl`	Fast Margin Maximization via Dual Acceleration. CoRR Ziwei Ji, Nathan Srebro, Matus Telgarsky
2021	A*	`conf`	Fast margin maximization via dual acceleration. ICML Ziwei Ji, Nathan Srebro, Matus Telgarsky
2021	A	`conf`	Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent. AISTATS Suriya Gunasekar, Blake E. Woodworth, Nathan Srebro
2021	J	`jnl`	On Margin Maximization in Linear and ReLU Networks. CoRR Gal Vardi, Ohad Shamir, Nathan Srebro
2021	A*	`conf`	On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent. ICML Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E. Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry
2021	J	`jnl`	On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent. CoRR Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E. Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry
2021	A*	`conf`	On the Power of Differentiable Learning versus PAC and SQ Learning. NeurIPS Emmanuel Abbe, Pritish Kamath, Eran Malach, Colin Sandon, Nathan Srebro
2021	J	`jnl`	On the Power of Differentiable Learning versus PAC and SQ Learning. CoRR Emmanuel Abbe, Pritish Kamath, Eran Malach, Colin Sandon, Nathan Srebro
2021	J	`jnl`	Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression. CoRR Lijia Zhou, Frederic Koehler, Danica J. Sutherland, Nathan Srebro
2021	A*	`conf`	Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels. ICML Eran Malach, Pritish Kamath, Emmanuel Abbe, Nathan Srebro
2021	J	`jnl`	Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels. CoRR Eran Malach, Pritish Kamath, Emmanuel Abbe, Nathan Srebro
2021	A*	`conf`	Representation Costs of Linear Neural Networks: Analysis and Design. NeurIPS Zhen Dai, Mina Karzand, Nathan Srebro
2021	A*	`conf`	The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. COLT Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
2021	J	`jnl`	The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. CoRR Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
2021	J	`jnl`	Transductive Robust Learning Guarantees. CoRR Omar Montasser, Steve Hanneke, Nathan Srebro
2021	A*	`conf`	Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting. NeurIPS Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro
2021	J	`jnl`	Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds, and Benign Overfitting. CoRR Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro
2020	A*	`conf`	A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case. ICLR Greg Ongie, Rebecca Willett, Daniel Soudry, Nathan Srebro
2020	B	`conf`	A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates. ALT Yossi Arjevani, Ohad Shamir, Nathan Srebro
2020	A*	`conf`	Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. COLT Pritish Kamath, Omar Montasser, Nathan Srebro
2020	J	`jnl`	Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity. CoRR Pritish Kamath, Omar Montasser, Nathan Srebro
2020	J	`jnl`	Dropout: Explicit Forms and Capacity Control. CoRR Raman Arora, Peter L. Bartlett, Poorya Mianjy, Nathan Srebro
2020	A*	`conf`	Efficiently Learning Adversarially Robust Halfspaces with Noise. ICML Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro
2020	J	`jnl`	Efficiently Learning Adversarially Robust Halfspaces with Noise. CoRR Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro
2020	A*	`conf`	Fair Learning with Private Demographic Data. ICML Hussein Mozannar, Mesrob I. Ohannessian, Nathan Srebro
2020	J	`jnl`	Fair Learning with Private Demographic Data. CoRR Hussein Mozannar, Mesrob I. Ohannessian, Nathan Srebro
2020	A	`conf`	Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. AISTATS Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth
2020	J	`jnl`	Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy. CoRR Edward Moroshko, Suriya Gunasekar, Blake E. Woodworth, Jason D. Lee, Nathan Srebro, Daniel Soudry
2020	A*	`conf`	Is Local SGD Better than Minibatch SGD? ICML Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro
2020	J	`jnl`	Is Local SGD Better than Minibatch SGD? CoRR Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro
2020	A*	`conf`	Kernel and Rich Regimes in Overparametrized Models. COLT Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, Nathan Srebro
2020	J	`jnl`	Kernel and Rich Regimes in Overparametrized Models. CoRR Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, Nathan Srebro
2020	J	`jnl`	Minibatch vs Local SGD for Heterogeneous Distributed Learning. CoRR Blake E. Woodworth, Kumar Kshitij Patel, Nathan Srebro
2020	J	`jnl`	Mirrorless Mirror Descent: A More Natural Discretization of Riemannian Gradient Flow. CoRR Suriya Gunasekar, Blake E. Woodworth, Nathan Srebro
2020	J	`jnl`	On Uniform Convergence and Low-Norm Interpolation Learning. CoRR Lijia Zhou, Danica J. Sutherland, Nathan Srebro
2020	J	`jnl`	Predictive Value Generalization Bounds. CoRR Keshav Vemuri, Nathan Srebro
2020	J	`jnl`	Reducing Adversarially Robust Learning to Non-Robust PAC Learning. CoRR Omar Montasser, Steve Hanneke, Nathan Srebro
2019	J	`jnl`	A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case. CoRR Greg Ongie, Rebecca Willett, Daniel Soudry, Nathan Srebro
2019	A	`conf`	Convergence of Gradient Descent on Separable Data. AISTATS Mor Shpigel Nacson, Jason D. Lee, Suriya Gunasekar, Pedro Henrique Pamplona Savarese, Nathan Srebro, Daniel Soudry
2019	—	`conf`	From Fair Decision Making To Social Equality. FAT Hussein Mouzannar, Mesrob I. Ohannessian, Nathan Srebro
2019	J	`jnl`	Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. CoRR Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth
2019	A*	`conf`	How do infinite width bounded norm networks look in function space? COLT Pedro Savarese, Itay Evron, Daniel Soudry, Nathan Srebro
2019	J	`jnl`	How do infinite width bounded norm networks look in function space? CoRR Pedro Savarese, Itay Evron, Daniel Soudry, Nathan Srebro
2019	A*	`conf`	Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models. ICML Mor Shpigel Nacson, Suriya Gunasekar, Jason D. Lee, Nathan Srebro, Daniel Soudry
2019	J	`jnl`	Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models. CoRR Mor Shpigel Nacson, Suriya Gunasekar, Jason D. Lee, Nathan Srebro, Daniel Soudry
2019	J	`jnl`	Lower Bounds for Non-Convex Stochastic Optimization. CoRR Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth
2019	A*	`conf`	Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory. COLT Blake E. Woodworth, Nathan Srebro
2019	J	`jnl`	Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory. CoRR Blake E. Woodworth, Nathan Srebro
2019	A*	`conf`	Semi-Cyclic Stochastic Gradient Descent. ICML Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, Kunal Talwar
2019	J	`jnl`	Semi-Cyclic Stochastic Gradient Descent. CoRR Hubert Eichner, Tomer Koren, H. Brendan McMahan, Nathan Srebro, Kunal Talwar
2019	J	`jnl`	Stochastic Canonical Correlation Analysis. J. Mach. Learn. Res. Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang
2019	A	`conf`	Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate. AISTATS Mor Shpigel Nacson, Nathan Srebro, Daniel Soudry
2019	B	`conf`	Stochastic Nonconvex Optimization with Large Minibatches. ALT Weiran Wang, Nathan Srebro
2019	A*	`conf`	The Complexity of Making the Gradient Small in Stochastic Convex Optimization. COLT Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth
2019	J	`jnl`	The Complexity of Making the Gradient Small in Stochastic Convex Optimization. CoRR Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth
2019	—	`conf`	The role of over-parametrization in generalization of neural networks. ICLR (Poster) Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro
2019	A*	`conf`	Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. ICML Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You
2019	A*	`conf`	VC Classes are Adversarially Robustly Learnable, but Only Improperly. COLT Omar Montasser, Steve Hanneke, Nathan Srebro
2019	J	`jnl`	VC Classes are Adversarially Robustly Learnable, but Only Improperly. CoRR Omar Montasser, Steve Hanneke, Nathan Srebro
2018	—	`conf`	A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. ICLR (Poster) Behnam Neyshabur, Srinadh Bhojanapalli, Nathan Srebro
2018	J	`jnl`	A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates. CoRR Yossi Arjevani, Ohad Shamir, Nathan Srebro
2018	A*	`conf`	Characterizing Implicit Bias in Terms of Optimization Geometry. ICML Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nathan Srebro
2018	J	`jnl`	Characterizing Implicit Bias in Terms of Optimization Geometry. CoRR Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nathan Srebro
2018	J	`jnl`	Convergence of Gradient Descent on Separable Data. CoRR Mor Shpigel Nacson, Jason D. Lee, Suriya Gunasekar, Nathan Srebro, Daniel Soudry
2018	J	`jnl`	Distributed Stochastic Multi-Task Learning with Graph Regularization. CoRR Weiran Wang, Jialei Wang, Mladen Kolar, Nathan Srebro
2018	B	`conf`	Efficient coordinate-wise leading eigenvector computation. ALT Jialei Wang, Weiran Wang, Dan Garber, Nathan Srebro
2018	J	`jnl`	From Fair Decision Making to Social Equality. CoRR Hussein Mouzannar, Mesrob I. Ohannessian, Nathan Srebro
2018	J	`jnl`	Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization. CoRR Blake E. Woodworth, Jialei Wang, Brendan McMahan, Nathan Srebro
2018	J	`jnl`	Implicit Bias of Gradient Descent on Linear Convolutional Networks. CoRR Suriya Gunasekar, Jason D. Lee, Daniel Soudry, Nathan Srebro
2018	Misc	`conf`	Implicit Regularization in Matrix Factorization. ITA Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro
2018	J	`jnl`	On preserving non-discrimination when combining expert advice. CoRR Avrim Blum, Suriya Gunasekar, Thodoris Lykouris, Nathan Srebro
2018	J	`jnl`	Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate. CoRR Mor Shpigel Nacson, Nathan Srebro, Daniel Soudry
2018	J	`jnl`	The Everlasting Database: Statistical Validity at a Fair Price. CoRR Blake E. Woodworth, Vitaly Feldman, Saharon Rosset, Nathan Srebro
2018	—	`conf`	The Implicit Bias of Gradient Descent on Separable Data. ICLR (Poster) Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Nathan Srebro
2018	J	`jnl`	The Implicit Bias of Gradient Descent on Separable Data. J. Mach. Learn. Res. Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, Nathan Srebro
2018	J	`jnl`	Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks. CoRR Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro
2018	J	`jnl`	Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. CoRR Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You
2017	J	`jnl`	A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. CoRR Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro
2017	J	`jnl`	An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine Learning. CoRR Chenxin Ma, Martin Jaggi, Frank E. Curtis, Nathan Srebro, Martin Takác
2017	A*	`conf`	Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. ICML Dan Garber, Ohad Shamir, Nathan Srebro
2017	J	`jnl`	Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. CoRR Dan Garber, Ohad Shamir, Nathan Srebro
2017	J	`jnl`	Data-Dependent Convergence for Consensus Stochastic Optimization. IEEE Trans. Autom. Control. Avleen Singh Bijral, Anand D. Sarwate, Nathan Srebro
2017	A*	`conf`	Efficient Distributed Learning with Sparsity. ICML Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang
2017	J	`jnl`	Efficient coordinate-wise leading eigenvector computation. CoRR Jialei Wang, Weiran Wang, Dan Garber, Nathan Srebro
2017	J	`jnl`	Exploring Generalization in Deep Learning. CoRR Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro
2017	J	`jnl`	Geometry of Optimization and Implicit Regularization in Deep Learning. CoRR Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro
2017	J	`jnl`	Implicit Regularization in Matrix Factorization. CoRR Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro
2017	A*	`conf`	Learning Non-Discriminatory Predictors. COLT Blake E. Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, Nathan Srebro
2017	J	`jnl`	Learning Non-Discriminatory Predictors. CoRR Blake E. Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, Nathan Srebro
2017	A*	`conf`	Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox. COLT Jialei Wang, Weiran Wang, Nathan Srebro
2017	J	`jnl`	Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox. CoRR Jialei Wang, Weiran Wang, Nathan Srebro
2017	J	`jnl`	Stochastic Canonical Correlation Analysis. CoRR Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang
2017	J	`jnl`	Stochastic Nonconvex Optimization with Large Minibatches. CoRR Weiran Wang, Nathan Srebro
2017	J	`jnl`	The Implicit Bias of Gradient Descent on Separable Data. CoRR Daniel Soudry, Elad Hoffer, Nathan Srebro
2017	J	`jnl`	The Marginal Value of Adaptive Gradient Methods in Machine Learning. CoRR Ashia C. Wilson, Rebecca Roelofs, Mitchell Stern, Nathan Srebro, Benjamin Recht
2016	A*	`conf`	Data-Dependent Path Normalization in Neural Networks. ICLR (Poster) Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro
2016	—	`conf`	Data-dependent bounds on network gradient descent. Allerton Avleen Singh Bijral, Anand D. Sarwate, Nathan Srebro
2016	J	`jnl`	Distributed Multi-Task Learning with Shared Representation. CoRR Jialei Wang, Mladen Kolar, Nathan Srebro
2016	A	`conf`	Distributed Multi-Task Learning. AISTATS Jialei Wang, Mladen Kolar, Nathan Srebro
2016	J	`jnl`	Efficient Distributed Learning with Sparsity. CoRR Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang
2016	J	`jnl`	Equality of Opportunity in Supervised Learning. CoRR Moritz Hardt, Eric Price, Nathan Srebro
2016	A	`conf`	Fast and Scalable Structural SVM with Slack Rescaling. AISTATS Heejin Choi, Ofer Meshi, Nathan Srebro
2016	J	`jnl`	Global Optimality of Local Search for Low Rank Matrix Recovery. CoRR Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro
2016	J	`jnl`	Globally Convergent Stochastic Optimization for Canonical Correlation Analysis. CoRR Weiran Wang, Jialei Wang, Nathan Srebro
2016	J	`jnl`	Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations. CoRR Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nathan Srebro
2016	J	`jnl`	Reducing Runtime by Recycling Samples. CoRR Jialei Wang, Hai Wang, Nathan Srebro
2016	J	`jnl`	Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data. CoRR Jialei Wang, Jason D. Lee, Mehrdad Mahdavi, Mladen Kolar, Nathan Srebro
2016	J	`jnl`	Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm. Math. Program. Deanna Needell, Nathan Srebro, Rachel A. Ward
2015	J	`jnl`	Distributed Mini-Batch SDCA. CoRR Martin Takác, Peter Richtárik, Nathan Srebro
2015	J	`jnl`	Distributed Multitask Learning. CoRR Jialei Wang, Mladen Kolar, Nathan Srebro
2015	A	`conf`	Efficient Training of Structured SVMs via Soft Constraints. AISTATS Ofer Meshi, Nathan Srebro, Tamir Hazan
2015	J	`jnl`	Fast and Scalable Structural SVM with Slack Rescaling. CoRR Heejin Choi, Ofer Meshi, Nathan Srebro
2015	—	`conf`	In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning. ICLR (Workshop) Behnam Neyshabur, Ryota Tomioka, Nathan Srebro
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	A*	`conf`	Norm-Based Capacity Control in Neural Networks. COLT Behnam Neyshabur, Ryota Tomioka, Nathan Srebro
2015	J	`jnl`	Norm-Based Capacity Control in Neural Networks. CoRR Behnam Neyshabur, Ryota Tomioka, Nathan Srebro
2015	J	`jnl`	Normalized Hierarchical SVM. CoRR Heejin Choi, Yutaka Sasaki, Nathan Srebro
2015	A*	`conf`	On Symmetric and Asymmetric LSHs for Inner Product Search. ICML Behnam Neyshabur, Nathan Srebro
2015	—	`conf`	Path-SGD: Path-Normalized Optimization in Deep Neural Networks. NIPS Behnam Neyshabur, Ruslan Salakhutdinov, Nathan Srebro
2015	J	`jnl`	Path-SGD: Path-Normalized Optimization in Deep Neural Networks. CoRR Behnam Neyshabur, Ruslan Salakhutdinov, Nathan Srebro
2015	J	`jnl`	Stochastic Optimization for Deep CCA via Nonlinear Orthogonal Iterations. CoRR Weiran Wang, Raman Arora, Karen Livescu, Nathan Srebro
2015	—	`conf`	Stochastic optimization for deep CCA via nonlinear orthogonal iterations. Allerton Weiran Wang, Raman Arora, Karen Livescu, Nathan Srebro
2014	A*	`conf`	Active collaborative permutation learning. KDD Jialei Wang, Nathan Srebro, James A. Evans
2014	B	`conf`	Clustering, Hamming Embedding, Generalized LSH and the Max Norm. ALT Behnam Neyshabur, Yury Makarychev, Nathan Srebro
2014	J	`jnl`	Clustering, Hamming Embedding, Generalized LSH and the Max Norm. CoRR Behnam Neyshabur, Yury Makarychev, Nathan Srebro
2014	A*	`conf`	Communication-Efficient Distributed Optimization using an Approximate Newton-type Method. ICML Ohad Shamir, Nathan Srebro, Tong Zhang
2014	—	`conf`	Distributed stochastic optimization and learning. Allerton Ohad Shamir, Nathan Srebro
2014	J	`jnl`	On Symmetric and Asymmetric LSHs for Inner Product Search. CoRR Behnam Neyshabur, Nathan Srebro
2014	—	`conf`	Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm. NIPS Deanna Needell, Rachel A. Ward, Nathan Srebro
2013	J	`jnl`	Auditing: Active Learning with Outcome-Dependent Query Costs. CoRR Sivan Sabato, Anand D. Sarwate, Nathan Srebro
2013	J	`jnl`	Communication Efficient Distributed Optimization using an Approximate Newton-type Method. CoRR Ohad Shamir, Nathan Srebro, Tong Zhang
2013	J	`jnl`	Distribution-dependent sample complexity of large margin learning. J. Mach. Learn. Res. Sivan Sabato, Nathan Srebro, Naftali Tishby
2013	J	`jnl`	Maximum Likelihood Bounded Tree-Width Markov Networks CoRR Nathan Srebro
2013	J	`jnl`	Mini-Batch Primal and Dual Methods for SVMs CoRR Martin Takác, Avleen Singh Bijral, Peter Richtárik, Nathan Srebro
2013	J	`jnl`	Stochastic Optimization of PCA with Capped MSG. CoRR Raman Arora, Andrew Cotter, Nathan Srebro
2013	J	`jnl`	Stochastic gradient descent and the randomized Kaczmarz algorithm. CoRR Deanna Needell, Nathan Srebro, Rachel A. Ward
2013	J	`jnl`	The Power of Asymmetry in Binary Hashing. CoRR Behnam Neyshabur, Payman Yadollahpour, Yury Makarychev, Ruslan Salakhutdinov, Nathan Srebro
2012	A	`conf`	Approximate Inference by Intersecting Semidefinite Bound and Local Polytope. AISTATS Jian Peng, Tamir Hazan, Nathan Srebro, Jinbo Xu
2012	A*	`ed.`	COLT 2012 - The 25th Annual Conference on Learning Theory, June 25-27, 2012, Edinburgh, Scotland COLT Shie Mannor, Nathan Srebro, Robert C. Williamson
2012	J	`jnl`	Clustering using Max-norm Constrained Optimization CoRR Ali Jalali, Nathan Srebro
2012	A*	`conf`	Clustering using Max-norm Constrained Optimization. ICML Ali Jalali, Nathan Srebro
2012	J	`jnl`	Complexity of Inference in Graphical Models CoRR Venkat Chandrasekaran, Nathan Srebro, Prahladh Harsha
2012	J	`jnl`	Learning Sparse Low-Threshold Linear Classifiers CoRR Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel J. Hsu, Tong Zhang
2012	J	`jnl`	Matrix reconstruction with the local max norm CoRR Rina Foygel, Nathan Srebro, Ruslan Salakhutdinov
2012	—	`conf`	Matrix reconstruction with the local max norm. NIPS Rina Foygel, Nathan Srebro, Ruslan Salakhutdinov
2012	A*	`conf`	Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss. ICML Shai Ben-David, David Loker, Nathan Srebro, Karthik Sridharan
2012	J	`jnl`	PRISMA: PRoximal Iterative SMoothing Algorithm CoRR Francesco Orabona, Andreas Argyriou, Nathan Srebro
2012	A*	`conf`	Preface. COLT Shie Mannor, Nathan Srebro
2012	J	`jnl`	Semi-supervised Learning with Density Based Distances CoRR Avleen Singh Bijral, Nathan D. Ratliff, Nathan Srebro
2012	—	`conf`	Sparse Prediction with the $k$-Support Norm. NIPS Andreas Argyriou, Rina Foygel, Nathan Srebro
2012	J	`jnl`	Sparse Prediction with the k-Overlap Norm CoRR Andreas Argyriou, Rina Foygel, Nathan Srebro
2012	—	`conf`	Stochastic optimization for PCA and PLS. Allerton Conference Raman Arora, Andrew Cotter, Karen Livescu, Nathan Srebro
2012	J	`jnl`	The Kernelized Stochastic Batch Perceptron CoRR Andrew Cotter, Shai Shalev-Shwartz, Nathan Srebro
2012	A*	`conf`	The Kernelized Stochastic Batch Perceptron. ICML Andrew Cotter, Shai Shalev-Shwartz, Nathan Srebro
2011	A*	`conf`	A GPU-tailored approach for training kernelized SVMs. KDD Andrew Cotter, Nathan Srebro, Joseph Keshet
2011	A*	`conf`	An iterated graph laplacian approach for ranking on manifolds. KDD Xueyuan Zhou, Mikhail Belkin, Nathan Srebro
2011	J	`jnl`	Better Mini-Batch Algorithms via Accelerated Gradient Methods CoRR Andrew Cotter, Ohad Shamir, Nathan Srebro, Karthik Sridharan
2011	J	`jnl`	Concentration-Based Guarantees for Low-Rank Matrix Reconstruction CoRR Rina Foygel, Nathan Srebro
2011	A*	`conf`	Concentration-Based Guarantees for Low-Rank Matrix Reconstruction. COLT Rina Foygel, Nathan Srebro
2011	A	`conf`	Error Analysis of Laplacian Eigenmaps for Semi-supervised Learning. AISTATS Xueyuan Zhou, Nathan Srebro
2011	J	`jnl`	Explicit Approximations of the Gaussian Kernel CoRR Andrew Cotter, Joseph Keshet, Nathan Srebro
2011	J	`jnl`	Learning with the Weighted Trace-norm under Arbitrary Sampling Distributions CoRR Rina Foygel, Ruslan Salakhutdinov, Ohad Shamir, Nathan Srebro
2011	J	`jnl`	On the Universality of Online Mirror Descent CoRR Nathan Srebro, Karthik Sridharan, Ambuj Tewari
2011	J	`jnl`	Pegasos: primal estimated sub-gradient solver for SVM. Math. Program. Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro, Andrew Cotter
2011	A	`conf`	Semi-supervised Learning with Density Based Distances. UAI Avleen Singh Bijral, Nathan D. Ratliff, Nathan Srebro
2010	J	`jnl`	Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm CoRR Ruslan Salakhutdinov, Nathan Srebro
2010	—	`conf`	Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm. NIPS Ruslan Salakhutdinov, Nathan Srebro
2010	J	`jnl`	Learnability, Stability and Uniform Convergence. J. Mach. Learn. Res. Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan
2010	—	`conf`	Practical Large-Scale Optimization for Max-norm Regularization. NIPS Jason D. Lee, Ben Recht, Ruslan Salakhutdinov, Nathan Srebro, Joel A. Tropp
2010	A	`conf`	Reducing Label Complexity by Learning From Bags. AISTATS Sivan Sabato, Nathan Srebro, Naftali Tishby
2010	—	`conf`	Smoothness, Low Noise and Fast Rates. NIPS Nathan Srebro, Karthik Sridharan, Ambuj Tewari
2010	J	`jnl`	Smoothness, Low-Noise and Fast Rates CoRR Nathan Srebro, Karthik Sridharan, Ambuj Tewari
2010	J	`jnl`	Tight Sample Complexity of Large-Margin Learning CoRR Sivan Sabato, Nathan Srebro, Naftali Tishby
2010	—	`conf`	Tight Sample Complexity of Large-Margin Learning. NIPS Sivan Sabato, Nathan Srebro, Naftali Tishby
2010	J	`jnl`	Trading Accuracy for Sparsity in Optimization Problems with Sparsity Constraints. SIAM J. Optim. Shai Shalev-Shwartz, Nathan Srebro, Tong Zhang
2009	A*	`conf`	Learnability and Stability in the General Learning Setting. COLT Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan
2009	—	`conf`	Statistical Analysis of Semi-Supervised Learning: The Limit of Infinite Unlabelled Data. NIPS Boaz Nadler, Nathan Srebro, Xueyuan Zhou
2009	A*	`conf`	Stochastic Convex Optimization. COLT Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan
2008	J	`jnl`	A theory of learning with similarity functions. Mach. Learn. Maria-Florina Balcan, Avrim Blum, Nathan Srebro
2008	A	`conf`	Complexity of Inference in Graphical Models. UAI Venkat Chandrasekaran, Nathan Srebro, Prahladh Harsha
2008	—	`conf`	Fast Rates for Regularized Objectives. NIPS Karthik Sridharan, Shai Shalev-Shwartz, Nathan Srebro
2008	A*	`conf`	Improved Guarantees for Learning via Similarity Functions. COLT Maria-Florina Balcan, Avrim Blum, Nathan Srebro
2008	A*	`conf`	SVM optimization: inverse dependence on training set size. ICML Shai Shalev-Shwartz, Nathan Srebro
2007	A*	`conf`	Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation? COLT Nathan Srebro
2007	A*	`conf`	How Good Is a Kernel When Used as a Similarity Measure? COLT Nathan Srebro
2007	A*	`conf`	Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. ICML Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro
2007	A*	`conf`	Uncovering shared structures in multiclass classification. ICML Yonatan Amit, Michael Fink, Nathan Srebro, Shimon Ullman
2006	A*	`conf`	An investigation of computational and informational limits in Gaussian mixture clustering. ICML Nathan Srebro, Gregory Shakhnarovich, Sam T. Roweis
2006	A*	`conf`	Learning Bounds for Support Vector Machines with Learned Kernels. COLT Nathan Srebro, Shai Ben-David
2005	A*	`conf`	Fast maximum margin matrix factorization for collaborative prediction. ICML Jason D. M. Rennie, Nathan Srebro
2005	A*	`conf`	Rank, Trace-Norm and Max-Norm. COLT Nathan Srebro, Adi Shraibman
2004	—	`conf`	Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices. NIPS Nathan Srebro, Noga Alon, Tommi S. Jaakkola
2004	—	`—`	Learning with matrix factorizations. Nathan Srebro
2004	—	`conf`	Maximum-Margin Matrix Factorization. NIPS Nathan Srebro, Jason D. M. Rennie, Tommi S. Jaakkola
2003	J	`jnl`	K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data. Bioinform. Ziv Bar-Joseph, Erik D. Demaine, David K. Gifford, Nathan Srebro, Angèle M. Hamel, Tommi S. Jaakkola
2003	—	`conf`	Linear Dependent Dimensionality Reduction. NIPS Nathan Srebro, Tommi S. Jaakkola
2003	J	`jnl`	Maximum likelihood bounded tree-width Markov networks. Artif. Intell. Nathan Srebro
2003	A*	`conf`	Weighted Low-Rank Approximations. ICML Nathan Srebro, Tommi S. Jaakkola
2002	C	`conf`	K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data. WABI Ziv Bar-Joseph, Erik D. Demaine, David K. Gifford, Angèle M. Hamel, Tommi S. Jaakkola, Nathan Srebro
2001	A*	`conf`	Learning Markov networks: maximum bounded tree-width graphs. SODA David R. Karger, Nathan Srebro
2001	A	`conf`	Maximum Likelihood Bounded Tree-Width Markov Networks. UAI Nathan Srebro