Nathan Srebro

发表

Data-dependent bounds on network gradient descent

Anand D. Sarwate, Avleen Singh Bijral, Nathan Srebro, 2016, 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

How Good Is a Kernel When Used as a Similarity Measure?

Nathan Srebro, Nathan Srebro, 2007, COLT.

In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning pdf

Ryota Tomioka, Nathan Srebro, Behnam Neyshabur, 2014, ICLR.

The Kernelized Stochastic Batch Perceptron pdf

Nathan Srebro, Andrew Cotter, Shai Shalev-Shwartz, 2012, ICML.

Learning with the weighted trace-norm under arbitrary sampling distributions

Ohad Shamir, Ruslan Salakhutdinov, Nathan Srebro, 2011, NIPS.

Maximum-Margin Matrix Factorization

Tommi S. Jaakkola, Nathan Srebro, Jason D. M. Rennie, 2004, NIPS.

A theory of learning with similarity functions

Maria-Florina Balcan, Avrim Blum, Nathan Srebro, 2008, Machine Learning.

Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models

Nathan Srebro, Daniel Soudry, Jason D. Lee, 2019, ICML.

Equality of Opportunity in Supervised Learning

Nathan Srebro, Moritz Hardt, Eric Price, 2016, NIPS.

Distributed Stochastic Multi-Task Learning with Graph Regularization pdf

Mladen Kolar, Nathan Srebro, Jialei Wang, 2018, ArXiv.

The Complexity of Making the Gradient Small in Stochastic Convex Optimization

Ohad Shamir, Nathan Srebro, Karthik Sridharan, 2019, COLT.

Characterizing Implicit Bias in Terms of Optimization Geometry pdf

Nathan Srebro, Daniel Soudry, Suriya Gunasekar, 2018, ICML.

Linear Dependent Dimensionality Reduction

Tommi S. Jaakkola, Nathan Srebro, T. Jaakkola, 2003, NIPS.

Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data

Mladen Kolar, Nathan Srebro, Jialei Wang, 2016, AISTATS.

An investigation of computational and informational limits in Gaussian mixture clustering

Gregory Shakhnarovich, Nathan Srebro, Sam T. Roweis, 2006, ICML '06.

Normalized Hierarchical SVM pdf

Nathan Srebro, Yutaka Sasaki, Heejin Choi, 2015, ArXiv.

Reducing Runtime by Recycling Samples pdf

Hai Wang, Nathan Srebro, Jialei Wang, 2016, ArXiv.

Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory

Nathan Srebro, Blake Woodworth, 2019, COLT.

Distributed Multitask Learning pdf

Mladen Kolar, Nathan Srebro, Jialei Wang, 2015, ArXiv.

Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks pdf

Yann LeCun, Zhiyuan Li, Nathan Srebro, 2018, ArXiv.

Fair Learning with Private Demographic Data

Nathan Srebro, Mesrob I. Ohannessian, Hussein Mozannar, 2020, ICML.

Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity pdf

Nathan Srebro, Pritish Kamath, Omar Montasser, 2020, COLT 2020.

K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data

Erik D. Demaine, Tommi S. Jaakkola, Ziv Bar-Joseph, 2002, WABI.

Is Local SGD Better than Minibatch SGD? pdf

Ohad Shamir, Nathan Srebro, Sebastian U. Stich, 2020, ICML.

Uncovering shared structures in multiclass classification

Shimon Ullman, Nathan Srebro, Yonatan Amit, 2007, ICML '07.

Global Optimality of Local Search for Low Rank Matrix Recovery

Nathan Srebro, Behnam Neyshabur, Srinadh Bhojanapalli, 2016, NIPS.

Learning Bounds for Support Vector Machines with Learned Kernels

Shai Ben-David, Nathan Srebro, Nathan Srebro, 2006, COLT.

Kernel and Deep Regimes in Overparametrized Models pdf

Nathan Srebro, Daniel Soudry, Jason D. Lee, 2019, ArXiv.

Complexity of Inference in Graphical Models

Venkat Chandrasekaran, Nathan Srebro, Prahladh Harsha, 2008, UAI.

Learning sparse low-threshold linear classifiers pdf

Tong Zhang, Nathan Srebro, Shai Shalev-Shwartz, 2015, J. Mach. Learn. Res..

Learnability, Stability and Uniform Convergence

Ohad Shamir, Nathan Srebro, Karthik Sridharan, 2010, J. Mach. Learn. Res..

Rank, Trace-Norm and Max-Norm

Adi Shraibman, Nathan Srebro, Nathan Srebro, 2005, COLT.

Fast maximum margin matrix factorization for collaborative prediction

Nathan Srebro, Jason D. M. Rennie, Nathan Srebro, 2005, ICML.

Implicit Bias of Gradient Descent on Linear Convolutional Networks

Nathan Srebro, Daniel Soudry, Suriya Gunasekar, 2018, NeurIPS.

Data-Dependent Path Normalization in Neural Networks pdf

Ruslan Salakhutdinov, Ryota Tomioka, Nathan Srebro, 2015, ICLR.

A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks pdf

David A. McAllester, Nathan Srebro, Behnam Neyshabur, 2017, ICLR.

Normalized Spectral Map Synchronization

Sujay Sanghavi, Qi-Xing Huang, Nathan Srebro, 2016, NIPS.

The Marginal Value of Adaptive Gradient Methods in Machine Learning

Nathan Srebro, Benjamin Recht, Mitchell Stern, 2017, NIPS.

Beating SGD: Learning SVMs in Sublinear Time

Nathan Srebro, Elad Hazan, Tomer Koren, 2011, NIPS.

Learning Non-Discriminatory Predictors pdf

Nathan Srebro, Mesrob I. Ohannessian, Suriya Gunasekar, 2017, COLT.

Mirrorless Mirror Descent: A More Natural Discretization of Riemannian Gradient Flow pdf

Nathan Srebro, Suriya Gunasekar, Blake E. Woodworth, 2020, ArXiv.

Better Mini-Batch Algorithms via Accelerated Gradient Methods

Ohad Shamir, Nathan Srebro, Karthik Sridharan, 2011, NIPS.

Sparse Prediction with the k-Overlap Norm pdf

Nathan Srebro, Rina Foygel, Andreas Argyriou, 2012, ArXiv.

Clustering, Hamming Embedding, Generalized LSH and the Max Norm pdf

Nathan Srebro, Yury Makarychev, Behnam Neyshabur, 2014, ALT.

Distributed stochastic optimization and learning

Ohad Shamir, Nathan Srebro, Nathan Srebro, 2014, 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton).

Trading Accuracy for Sparsity in Optimization Problems with Sparsity Constraints

Tong Zhang, Nathan Srebro, Shai Shalev-Shwartz, 2010, SIAM J. Optim..

Distribution-dependent sample complexity of large margin learning

Nathan Srebro, Naftali Tishby, Sivan Sabato, 2012, J. Mach. Learn. Res..

Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis

Nathan Srebro, Jialei Wang, Dan Garber, 2016, NIPS.

l1 Regularization in Infinite Dimensional Feature Spaces

Ji Zhu, Nathan Srebro, Saharon Rosset, 2007, COLT.

Pegasos: primal estimated sub-gradient solver for SVM

Yoram Singer, Nathan Srebro, Shai Shalev-Shwartz, 2007, ICML '07.

Fast Rates for Regularized Objectives

Nathan Srebro, Karthik Sridharan, Shai Shalev-Shwartz, 2008, NIPS.

Learning with matrix factorizations

Nathan Srebro, Nathan Srebro, N. Srebro, 2004 .

Learnability and Stability in the General Learning Setting

Ohad Shamir, Nathan Srebro, Karthik Sridharan, 2009, COLT.

On Uniform Convergence and Low-Norm Interpolation Learning pdf

Nathan Srebro, Lijia Zhou, D. J. Sutherland, 2020, NeurIPS.

Weighted Low-Rank Approximations

Tommi S. Jaakkola, Nathan Srebro, T. Jaakkola, 2003, ICML.

Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm

Ruslan Salakhutdinov, Nathan Srebro, R. Salakhutdinov, 2010, NIPS.

Tight Sample Complexity of Large-Margin Learning

Nathan Srebro, Naftali Tishby, Sivan Sabato, 2010, NIPS.

On preserving non-discrimination when combining expert advice

Avrim Blum, Nathan Srebro, Thodoris Lykouris, 2018, NeurIPS.

An iterated graph laplacian approach for ranking on manifolds

Mikhail Belkin, Nathan Srebro, Xueyuan Zhou, 2011, KDD.

A simpler and better LSH for Maximum Inner Product Search (MIPS)

Nathan Srebro, Behnam Neyshabur, 2014 .

Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints

Maya R. Gupta, Nathan Srebro, Karthik Sridharan, 2018, ICML.

Tight Complexity Bounds for Optimizing Composite Objectives

Nathan Srebro, Blake E. Woodworth, Nathan Srebro, 2016, NIPS.

On the Universality of Online Mirror Descent

Ambuj Tewari, Nathan Srebro, Karthik Sridharan, 2011, NIPS.

On Symmetric and Asymmetric LSHs for Inner Product Search

Nathan Srebro, Behnam Neyshabur, Nathan Srebro, 2014, ICML.

Matrix reconstruction with the local max norm

Ruslan Salakhutdinov, Nathan Srebro, Rina Foygel, 2012, NIPS.

Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox pdf

Nathan Srebro, Jialei Wang, Weiran Wang, 2017, COLT.

A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case pdf

Nathan Srebro, Daniel Soudry, Rebecca Willett, 2019, ICLR.

A GPU-tailored approach for training kernelized SVMs

Nathan Srebro, Andrew Cotter, Joseph Keshet, 2011, KDD.

Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate

Nathan Srebro, Daniel Soudry, Mor Shpigel Nacson, 2018, AISTATS.

Semi-Cyclic Stochastic Gradient Descent pdf

Nathan Srebro, Hubert Eichner, H. Brendan McMahan, 2019, ICML.

Fast and Scalable Structural SVM with Slack Rescaling

Ofer Meshi, Nathan Srebro, Heejin Choi, 2015, AISTATS.

The Everlasting Database: Statistical Validity at a Fair Price

Vitaly Feldman, Nathan Srebro, Saharon Rosset, 2018, NeurIPS.

Lower Bounds for Non-Convex Stochastic Optimization pdf

Nathan Srebro, Yair Carmon, John C. Duchi, 2019, ArXiv.

Communication-Efficient Distributed Optimization using an Approximate Newton-type Method pdf

Ohad Shamir, Tong Zhang, Nathan Srebro, 2013, ICML.

SVM optimization: inverse dependence on training set size

Nathan Srebro, Shai Shalev-Shwartz, S. Shalev-Shwartz, 2008, ICML '08.

Efficient Training of Structured SVMs via Soft Constraints

Tamir Hazan, Ofer Meshi, Nathan Srebro, 2015, AISTATS.

Maximum likelihood bounded tree-width Markov networks

Nathan Srebro, Nathan Srebro, 2001, Artif. Intell..

Stochastic Optimization of PCA with Capped MSG

Nathan Srebro, Raman Arora, Andrew Cotter, 2013, NIPS.

Practical Large-Scale Optimization for Max-norm Regularization

Ruslan Salakhutdinov, Joel A. Tropp, Nathan Srebro, 2010, NIPS.

Auditing: Active Learning with Outcome-Dependent Query Costs

Anand D. Sarwate, Nathan Srebro, Sivan Sabato, 2013, NIPS.

PRISMA: PRoximal Iterative SMoothing Algorithm pdf

Francesco Orabona, Nathan Srebro, Andreas Argyriou, 2012, ArXiv.

On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning

Nathan Srebro, Percy Liang, P. Liang, 2010, ICML.

Stochastic Canonical Correlation Analysis pdf

Chao Gao, Nathan Srebro, Jialei Wang, 2017, J. Mach. Learn. Res..

Predictive Value Generalization Bounds pdf

Nathan Srebro, Keshav Vemuri, Nathan Srebro, 2020, ArXiv.

Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy pdf

Nathan Srebro, Daniel Soudry, Jason D. Lee, 2020, NeurIPS.

Distributed Multi-Task Learning with Shared Representation pdf

Mladen Kolar, Nathan Srebro, Jialei Wang, 2016, ArXiv.

Globally Convergent Stochastic Optimization for Canonical Correlation Analysis pdf

Nathan Srebro, Jialei Wang, Weiran Wang, 2016, ArXiv.

Stochastic Nonconvex Optimization with Large Minibatches pdf

Nathan Srebro, Weiran Wang, 2019, ALT.

Reducing Adversarially Robust Learning to Non-Robust PAC Learning pdf

Nathan Srebro, Steve Hanneke, Omar Montasser, 2020, NeurIPS.

Clustering using Max-norm Constrained Optimization pdf

Ali Jalali, Nathan Srebro, 2012, ICML.

Kernel and Rich Regimes in Overparametrized Models

Nathan Srebro, Daniel Soudry, Jason D. Lee, 2019, COLT.

Learning Markov networks: maximum bounded tree-width graphs

David R. Karger, Nathan Srebro, Nathan Srebro, 2001, SODA '01.

A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates

Ohad Shamir, Nathan Srebro, Yossi Arjevani, 2018, ALT.

Dropout: Explicit Forms and Capacity Control pdf

Nathan Srebro, Raman Arora, Peter Bartlett, 2020, ICML.

Convergence of Gradient Descent on Separable Data

Nathan Srebro, Daniel Soudry, Jason D. Lee, 2018, AISTATS.

Efficient coordinate-wise leading eigenvector computation pdf

Nathan Srebro, Jialei Wang, Dan Garber, 2018, ALT.

Learning Optimally Sparse Support Vector Machines

Nathan Srebro, Andrew Cotter, Shai Shalev-Shwartz, 2013, ICML.

Minibatch vs Local SGD for Heterogeneous Distributed Learning pdf

Nathan Srebro, Blake E. Woodworth, Blake Woodworth, 2020, NeurIPS.

Exploring Generalization in Deep Learning

Nathan Srebro, Behnam Neyshabur, David McAllester, 2017, NIPS.

Does Invariant Risk Minimization Capture Invariance? pdf

Nathan Srebro, Pritish Kamath, Akilesh Tangella, 2021, ArXiv.

Stochastic gradient descent and the randomized Kaczmarz algorithm pdf

Deanna Needell, Rachel Ward, Nathan Srebro, 2013, ArXiv.

Error Analysis of Laplacian Eigenmaps for Semi-supervised Learning

Nathan Srebro, Xueyuan Zhou, Nathan Srebro, 2011, AISTATS.

Simple Surveys: Response Retrieval Inspired by Recommendation Systems

Nathan Srebro, James Evans, Nandana Sengupta, 2018, Social Science Computer Review.

VC Classes are Adversarially Robustly Learnable, but Only Improperly pdf

Nathan Srebro, Steve Hanneke, Omar Montasser, 2019, COLT.

Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices

Noga Alon, Tommi S. Jaakkola, Nathan Srebro, 2004, NIPS.

Improved Guarantees for Learning via Similarity Functions

Maria-Florina Balcan, Avrim Blum, Nathan Srebro, 2008, COLT.

Geometry of Optimization and Implicit Regularization in Deep Learning pdf

Ruslan Salakhutdinov, Ryota Tomioka, Nathan Srebro, 2017, ArXiv.

Explicit Approximations of the Gaussian Kernel pdf

Nathan Srebro, Andrew Cotter, Joseph Keshet, 2011, ArXiv.

Large-Margin Matrix Factorization

Tommi S. Jaakkola, Nathan Srebro, Jason D. M. Rennie, 2004 .

K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data

Erik D. Demaine, Tommi S. Jaakkola, Ziv Bar-Joseph, 2002, WABI.

Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation?

Nathan Srebro, 2007, COLT.

Stochastic Convex Optimization

Ohad Shamir, Nathan Srebro, Karthik Sridharan, 2009, COLT.

Active collaborative permutation learning

Nathan Srebro, Jialei Wang, James Evans, 2014, KDD.

Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis pdf

Aaron Roth, Nathan Srebro, Om Thakkar, 2020, AISTATS.

Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis

Ohad Shamir, Nathan Srebro, Dan Garber, 2017, ICML.

The Implicit Bias of Gradient Descent on Separable Data pdf

Nathan Srebro, Elad Hoffer, Daniel Soudry, 2017, J. Mach. Learn. Res..

How do infinite width bounded norm networks look in function space? pdf

Nathan Srebro, Daniel Soudry, Pedro H. P. Savarese, 2019, COLT.

From Fair Decision Making To Social Equality

Nathan Srebro, Mesrob I. Ohannessian, Hussein Mouzannar, 2018, FAT.

Statistical Analysis of Semi-Supervised Learning: The Limit of Infinite Unlabelled Data

Nathan Srebro, Boaz Nadler, Xueyuan Zhou, 2009, NIPS.

Smoothness, Low Noise and Fast Rates

Ambuj Tewari, Nathan Srebro, Karthik Sridharan, 2010, NIPS.

Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations

Ruslan Salakhutdinov, Nathan Srebro, Behnam Neyshabur, 2016, NIPS.

Implicit Regularization in Matrix Factorization

Nathan Srebro, Behnam Neyshabur, Suriya Gunasekar, 2017, 2018 Information Theory and Applications Workshop (ITA).

Efficient Distributed Learning with Sparsity

Mladen Kolar, Tong Zhang, Nathan Srebro, 2016, ICML.

Pegasos: primal estimated sub-gradient solver for SVM

Yoram Singer, Nathan Srebro, Andrew Cotter, 2011, Math. Program..

Maximum likelihood Markov networks : an algorithmic approach

Nathan Srebro, N. Srebro, 2000 .

Characterizing the Sample Complexity of Large-Margin Learning With Second-Order Statistics pdf

Nathan Srebro, Naftali Tishby, Sivan Sabato, 2012, ArXiv.

Distributed Mini-Batch SDCA pdf

Peter Richtárik, Martin Takác, Nathan Srebro, 2015, ArXiv.

Semi-supervised Learning with Density Based Distances pdf

Avleen Singh Bijral, Nathan Srebro, Nathan D. Ratliff, 2011, UAI.

Data-Dependent Convergence for Consensus Stochastic Optimization

Anand D. Sarwate, Avleen Singh Bijral, Nathan Srebro, 2017, IEEE Transactions on Automatic Control.

Mini-Batch Primal and Dual Methods for SVMs pdf

Avleen Singh Bijral, Peter Richtárik, Martin Takác, 2013, ICML.

Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm

Deanna Needell, Nathan Srebro, Rachel Ward, 2013, NIPS.

Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization

Nathan Srebro, H. Brendan McMahan, Blake E. Woodworth, 2018, NeurIPS.

Distributed Multi-Task Learning

Mladen Kolar, Nathan Srebro, Jialei Wang, 2016, AISTATS.

The Power of Asymmetry in Binary Hashing

Ruslan Salakhutdinov, Nathan Srebro, Yury Makarychev, 2013, NIPS.

Fixed-Structure H ∞ Controller Design based on Distributed Probabilistic Model-Building Genetic Algorithm

Tatsuo Narikiyo, Nathan Srebro, Michihiro Kawanishi, 2011 .

Path-SGD: Path-Normalized Optimization in Deep Neural Networks

Ruslan Salakhutdinov, Nathan Srebro, Behnam Neyshabur, 2015, NIPS.

Reducing Label Complexity by Learning From Bags

Nathan Srebro, Naftali Tishby, Sivan Sabato, 2010, AISTATS.

Approximate Inference by Intersecting Semidefinite Bound and Local Polytope

Tamir Hazan, Jian Peng, Nathan Srebro, 2012, AISTATS.

Norm-Based Capacity Control in Neural Networks

Ryota Tomioka, Nathan Srebro, Behnam Neyshabur, 2015, COLT.

Concentration-Based Guarantees for Low-Rank Matrix Reconstruction

Nathan Srebro, Rina Foygel, 2011, COLT.

Sparse Prediction with the $k$-Support Norm

Nathan Srebro, Rina Foygel, Andreas Argyriou, 2012, NIPS.

Stochastic optimization for PCA and PLS

Nathan Srebro, Raman Arora, Andrew Cotter, 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

An accelerated communication-efficient primal-dual optimization framework for structured machine learning

Martin Jaggi, Martin Takác, Nathan Srebro, 2017, ArXiv.

Efficiently Learning Adversarially Robust Halfspaces with Noise

Ilias Diakonikolas, Nathan Srebro, Omar Montasser, 2020, ICML.

Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss

Nathan Srebro, Karthik Sridharan, Shai Ben-David, 2012, ICML.

Stochastic optimization for deep CCA via nonlinear orthogonal iterations

Nathan Srebro, Raman Arora, Karen Livescu, 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).

Stochastic Approximation for Canonical Correlation Analysis

Nathan Srebro, Raman Arora, Teodor Vanislavov Marinov, 2017, NIPS.

On the Power of Differentiable Learning versus PAC and SQ Learning pdf

Nathan Srebro, Pritish Kamath, Emmanuel Abbe, 2021, ArXiv.

Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent

Nathan Srebro, Suriya Gunasekar, Blake E. Woodworth, 2021, AISTATS.

On Margin Maximization in Linear and ReLU Networks pdf

Ohad Shamir, Nathan Srebro, Gal Vardi, 2021, ArXiv.

Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression pdf

Nathan Srebro, Frederic Koehler, Lijia Zhou, 2021, ACM / IMS Journal of Data Science.

On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent

Nathan Srebro, Daniel Soudry, Amir Globerson, 2021, ICML.

Adversarially Robust Learning with Unknown Perturbation Sets

Nathan Srebro, Steve Hanneke, Omar Montasser, 2021, COLT.

An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning pdf

Nathan Srebro, Blake E. Woodworth, Blake Woodworth, 2021, NeurIPS.

Fast Margin Maximization via Dual Acceleration pdf

Matus Telgarsky, Nathan Srebro, Ziwei Ji, 2021, ICML.

Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels

Nathan Srebro, Pritish Kamath, Emmanuel Abbe, 2021, ICML.

The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication

Ohad Shamir, Nathan Srebro, Blake E. Woodworth, 2021, COLT.

Transductive Robust Learning Guarantees pdf

Nathan Srebro, Steve Hanneke, Omar Montasser, 2021, AISTATS.

A Stochastic Newton Algorithm for Distributed Convex Optimization pdf

Ohad Shamir, Nathan Srebro, Blake Woodworth, 2021, ArXiv.

Eluder Dimension and Generalized Rank pdf

Nathan Srebro, Pritish Kamath, Dylan J. Foster, 2021, ArXiv.

Exponential Family Model-Based Reinforcement Learning via Score Matching pdf

Nathan Srebro, Zhuoran Yang, Zhaoran Wang, 2021, ArXiv.

Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds, and Benign Overfitting pdf

Nathan Srebro, Frederic Koehler, Lijia Zhou, 2021, NeurIPS.

Iterative Loss Minimization with ` 1-Norm Constraint and Guarantees on Sparsity

Nathan Srebro, Shai Shalev-Shwartz, 2008 .

Sparse Matrix Factorization of Gene Expression Data

Tommi S. Jaakkola, Nathan Srebro, 2001 .

When is Clustering Hard ?

Nathan Srebro, 2005 .

Minibatch vs Local SGD for Heterogeneous Distributed Learning pdf

Nathan Srebro, Blake Woodworth, Kumar Kshitij Patel, 2020, NeurIPS.

Is Local SGD Better than Minibatch SGD? pdf

Ohad Shamir, Nathan Srebro, Sebastian U. Stich, 2020, ICML.

Sparse Matrix Factorization for Analyzing Gene Expression Patterns

Nathan Srebro, Tommi Jaakkola, T. Jaakkola, 2001 .

- ary Clustering with Optimal Leaf Ordering for Gene Expression Data

Erik D. Demaine, Nathan Srebro, David K. Gifford, 2022 .

Methods and Experiments With Bounded Tree-width Markov Networks

Nathan Srebro, Percy Liang, 2004 .

Distribution of short paired duplications in mammalian genomes.

Nathan Srebro, Bud Mishra, Michael Wigler, 2004, Proceedings of the National Academy of Sciences of the United States of America.