Nathan Srebro

Q: What Schools Are Affiliated With Nathan Srebro

Nathan Srebro is affiliated with the following schools: Toyota Technological Institute at Chicago, Technion – Israel Institute of Technology, University of Chicago, Massachusetts Institute of Technology

Nathan Srebro's AcademicInfluence.com Rankings

Nathan Srebro

Computer Science

#5630

World Rank

#5946

Historical Rank

Machine Learning

#1566

World Rank

#1588

Historical Rank

Artificial Intelligence

#1798

World Rank

#1830

Historical Rank

Database

#2774

World Rank

#2898

Historical Rank

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Computer Science

Why Is Nathan Srebro Influential?

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Nathan Srebro's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Equality of Opportunity in Supervised Learning (2016) (2810)
Pegasos: primal estimated sub-gradient solver for SVM (2007) (2275)
Maximum-Margin Matrix Factorization (2004) (1151)
Fast maximum margin matrix factorization for collaborative prediction (2005) (1071)
Exploring Generalization in Deep Learning (2017) (924)
The Marginal Value of Adaptive Gradient Methods in Machine Learning (2017) (838)
Weighted Low-Rank Approximations (2003) (803)
The Implicit Bias of Gradient Descent on Separable Data (2017) (677)
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning (2014) (492)
Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm (2013) (478)
Norm-Based Capacity Control in Neural Networks (2015) (472)
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks (2017) (464)
Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks (2018) (454)
Communication-Efficient Distributed Optimization using an Approximate Newton-type Method (2013) (441)
Learnability, Stability and Uniform Convergence (2010) (394)
Rank, Trace-Norm and Max-Norm (2005) (377)
Uncovering shared structures in multiclass classification (2007) (351)
Global Optimality of Local Search for Low Rank Matrix Recovery (2016) (349)
Implicit Regularization in Matrix Factorization (2017) (333)
SVM optimization: inverse dependence on training set size (2008) (326)
Implicit Bias of Gradient Descent on Linear Convolutional Networks (2018) (306)
Characterizing Implicit Bias in Terms of Optimization Geometry (2018) (290)
Better Mini-Batch Algorithms via Accelerated Gradient Methods (2011) (285)
Stochastic Convex Optimization (2009) (279)
Learning Non-Discriminatory Predictors (2017) (272)
Learning with matrix factorizations (2004) (256)
Path-SGD: Path-Normalized Optimization in Deep Neural Networks (2015) (236)
Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm (2010) (235)
Smoothness, Low Noise and Fast Rates (2010) (233)
Mini-Batch Primal and Dual Methods for SVMs (2013) (195)
Kernel and Rich Regimes in Overparametrized Models (2019) (192)
Tight Complexity Bounds for Optimizing Composite Objectives (2016) (173)
Lower bounds for non-convex stochastic optimization (2019) (170)
Trading Accuracy for Sparsity in Optimization Problems with Sparsity Constraints (2010) (164)
Practical Large-Scale Optimization for Max-norm Regularization (2010) (159)
Is Local SGD Better than Minibatch SGD? (2020) (155)
Learning Markov networks: maximum bounded tree-width graphs (2001) (147)
Sparse Prediction with the $k$-Support Norm (2012) (147)
On Symmetric and Asymmetric LSHs for Inner Product Search (2014) (146)
Stochastic optimization for PCA and PLS (2012) (145)
K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data (2002) (137)
Fast Rates for Regularized Objectives (2008) (136)
Distributed stochastic optimization and learning (2014) (134)
Loss Functions for Preference Levels : Regression with Discrete Ordered Labels (2005) (134)
On the Universality of Online Mirror Descent (2011) (132)
Maximum likelihood bounded tree-width Markov networks (2001) (131)
Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices (2004) (128)
Convergence of Gradient Descent on Separable Data (2018) (120)
Complexity of Inference in Graphical Models (2008) (118)
How do infinite width bounded norm networks look in function space? (2019) (116)
Geometry of Optimization and Implicit Regularization in Deep Learning (2017) (116)
Minibatch vs Local SGD for Heterogeneous Distributed Learning (2020) (114)
Efficient Distributed Learning with Sparsity (2016) (113)
SPECTRALLY-NORMALIZED MARGIN BOUNDS FOR NEURAL NETWORKS (2018) (102)
Learning Bounds for Support Vector Machines with Learned Kernels (2006) (97)
A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case (2019) (97)
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization (2018) (93)
VC Classes are Adversarially Robustly Learnable, but Only Improperly (2019) (93)
Semi-supervised learning with the graph Laplacian: the limit of infinite unlabelled data (2009) (90)
A theory of learning with similarity functions (2008) (87)
Stochastic Optimization of PCA with Capped MSG (2013) (83)
From Fair Decision Making To Social Equality (2018) (82)
Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate (2018) (80)
Statistical Analysis of Semi-Supervised Learning: The Limit of Infinite Unlabelled Data (2009) (80)
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints (2018) (75)
Concentration-Based Guarantees for Low-Rank Matrix Reconstruction (2011) (75)
Does Invariant Risk Minimization Capture Invariance? (2021) (72)
The Power of Asymmetry in Binary Hashing (2013) (69)
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models (2019) (68)
Improved Guarantees for Learning via Similarity Functions (2008) (66)
Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss (2012) (63)
Stochastic optimization for deep CCA via nonlinear orthogonal iterations (2015) (62)
Beating SGD: Learning SVMs in Sublinear Time (2011) (60)
Learning with the weighted trace-norm under arbitrary sampling distributions (2011) (59)
Semi-Cyclic Stochastic Gradient Descent (2019) (59)
Optimistic Rates for Learning with a Smooth Loss (2010) (58)
Distributed Multi-Task Learning (2016) (57)
Explicit Approximations of the Gaussian Kernel (2011) (57)
A GPU-tailored approach for training kernelized SVMs (2011) (55)
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy (2020) (52)
Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox (2017) (50)
Distributed Mini-Batch SDCA (2015) (49)
Learning Optimally Sparse Support Vector Machines (2013) (46)
Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data (2016) (46)
Normalized Spectral Map Synchronization (2016) (42)
PRISMA: PRoximal Iterative SMoothing Algorithm (2012) (40)
Time-Varying Topic Models using Dependent Dirichlet Processes (2005) (40)
l1 Regularization in Infinite Dimensional Feature Spaces (2007) (39)
Fair Learning with Private Demographic Data (2020) (38)
The Complexity of Making the Gradient Small in Stochastic Convex Optimization (2019) (38)
An iterated graph laplacian approach for ranking on manifolds (2011) (37)
Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis (2017) (37)
Stochastic gradient descent and the randomized Kaczmarz algorithm (2013) (36)
` 1 Regularization in Infinite Dimensional Feature Spaces (2007) (36)
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent (2021) (34)
A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates (2018) (34)
Clustering using Max-norm Constrained Optimization (2012) (33)
Semi-supervised Learning with Density Based Distances (2011) (32)
Stochastic Approximation for Canonical Correlation Analysis (2017) (32)
Kernel and Deep Regimes in Overparametrized Models (2019) (31)
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis (2016) (29)
On preserving non-discrimination when combining expert advice (2018) (28)
An investigation of computational and informational limits in Gaussian mixture clustering (2006) (27)
Distribution of short paired duplications in mammalian genomes. (2004) (27)
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations (2016) (27)
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds, and Benign Overfitting (2021) (26)
Maximum likelihood Markov networks : an algorithmic approach (2000) (25)
Distributed Multi-Task Learning with Shared Representation (2016) (24)
Lower Bound for Randomized First Order Convex Optimization (2017) (24)
Dropout: Explicit Forms and Capacity Control (2020) (24)
Learnability and Stability in the General Learning Setting (2009) (23)
Efficiently Learning Adversarially Robust Halfspaces with Noise (2020) (23)
Stochastic Canonical Correlation Analysis (2017) (22)
Distributed Stochastic Multi-Task Learning with Graph Regularization (2018) (22)
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels (2021) (22)
Data-Dependent Path Normalization in Neural Networks (2015) (22)
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (2021) (21)
Matrix reconstruction with the local max norm (2012) (20)
Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity (2020) (20)
On Uniform Convergence and Low-Norm Interpolation Learning (2020) (20)
Reducing Adversarially Robust Learning to Non-Robust PAC Learning (2020) (19)
Stochastic Nonconvex Optimization with Large Minibatches (2017) (18)
How Good Is a Kernel When Used as a Similarity Measure? (2007) (18)
Efficient coordinate-wise leading eigenvector computation (2017) (16)
On Data Dependence in Distributed Stochastic Optimization (2016) (16)
Distributed Multitask Learning (2015) (15)
Error Analysis of Laplacian Eigenmaps for Semi-supervised Learning (2011) (15)
Active collaborative permutation learning (2014) (14)
The Kernelized Stochastic Batch Perceptron (2012) (14)
Fast Margin Maximization via Dual Acceleration (2021) (14)
On the Power of Differentiable Learning versus PAC and SQ Learning (2021) (13)
Mirrorless Mirror Descent: A More Natural Discretization of Riemannian Gradient Flow (2020) (13)
Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent (2020) (13)
Adversarially Robust Learning with Unknown Perturbation Sets (2021) (12)
On Margin Maximization in Linear and ReLU Networks (2021) (12)
An accelerated communication-efficient primal-dual optimization framework for structured machine learning (2017) (11)
Efficient Training of Structured SVMs via Soft Constraints (2015) (11)
Generalized Low-Rank Approximations (2003) (11)
Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation? (2007) (10)
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis (2019) (10)
Data-Dependent Convergence for Consensus Stochastic Optimization (2017) (10)
On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning (2010) (10)
Linear Dependent Dimensionality Reduction (2003) (9)
Implicit Bias of the Step Size in Linear Diagonal Neural Networks (2022) (9)
Globally Convergent Stochastic Optimization for Canonical Correlation Analysis (2016) (9)
Reducing Label Complexity by Learning From Bags (2010) (9)
Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression (2021) (9)
Tight Sample Complexity of Large-Margin Learning (2010) (9)
Approximate Inference by Intersecting Semidefinite Bound and Local Polytope (2012) (9)
An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning (2021) (8)
Large-Margin Matrix Factorization (2004) (8)
Eluder Dimension and Generalized Rank (2021) (8)
Distribution-dependent sample complexity of large margin learning (2012) (8)
Representation Costs of Linear Neural Networks: Analysis and Design (2021) (8)
When is Clustering Hard ? (2005) (7)
Auditing: Active Learning with Outcome-Dependent Query Costs (2013) (7)
Simple Surveys: Response Retrieval Inspired by Recommendation Systems (2018) (7)
Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data (2022) (6)
Clustering, Hamming Embedding, Generalized LSH and the Max Norm (2014) (6)
Sparse Matrix Factorization of Gene Expression Data (2001) (6)
Commentary on \Towards a Noncommutative Arithmetic-Geometric Mean Inequality" by B. Recht and C. R e (2012) (6)
Transductive Robust Learning Guarantees (2021) (5)
Fast-rate and optimistic-rate error bounds for L1-regularized regression (2011) (5)
Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory (2019) (5)
Training Fairness-Constrained Classifiers to Generalize (2018) (5)
A simpler and better LSH for Maximum Inner Product Search (MIPS) (2014) (4)
Sparse Matrix Factorization for Analyzing Gene Expression Patterns (2001) (4)
Fast Convergence Rates for Excess Regularized Risk with Application to SVM (2008) (4)
Fast and Scalable Structural SVM with Slack Rescaling (2015) (4)
The Sample Complexity of One-Hidden-Layer Neural Networks (2022) (4)
A Stochastic Newton Algorithm for Distributed Convex Optimization (2021) (4)
Methods and Experiments With Bounded Tree-width Markov Networks (2004) (4)
Pessimism for Offline Linear Contextual Bandits using 𝓁p Confidence Sets (2022) (3)
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization (2022) (3)
Fixed-Structure H ∞ Controller Design based on Distributed Probabilistic Model-Building Genetic Algorithm (2011) (3)
On Convex Optimization , Fat Shattering and Learning (2013) (3)
Reducing Runtime by Recycling Samples (2016) (2)
The Everlasting Database: Statistical Validity at a Fair Price (2018) (2)
Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization (2022) (2)
- ary Clustering with Optimal Leaf Ordering for Gene Expression Data (2)
Learning sparse low-threshold linear classifiers (2012) (2)
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models (2022) (1)
Exponential Family Model-Based Reinforcement Learning via Score Matching (2021) (1)
Sparse Prediction with the k-Overlap Norm (2012) (1)
Characterizing the Sample Complexity of Large-Margin Learning With Second-Order Statistics (2012) (1)
Iterative Loss Minimization with ` 1-Norm Constraint and Guarantees on Sparsity (2008) (1)
Data-dependent bounds on network gradient descent (2016) (1)
Normalized Hierarchical SVM (2015) (1)
Stochastic Optimization and Learning (2014) (1)
Learning with Multiple Similarity Functions (2008) (1)
Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization (2023) (1)
Adaptive Gaussian Kernel SVMs (2005) (0)
Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm (2015) (0)
Understanding the Eluder Dimension (2021) (0)
Generalization (2020) (0)
Predictive Value Generalization Bounds (2020) (0)
Exploiting Geometric Structure of High Dimensional Data for Learning : An Empirical Study (2008) (0)
Generalized Low-Rank Approximations Nathan Srebro and Tommi Jaakkola (2003) (0)
Maximum Margin Matrix Factorization using Smooth Semidefinite Optimization (2005) (0)
Interpolation Learning With Minimum Description Length (2023) (0)
Improved Prediction of HIV Resistance In-Vitro by Biochemically-Driven Models (0)
Industrial and Systems Engineering Distributed Mini-Batch SDCA (2015) (0)
Approximate Inference by Intersecting Semidenite Bound and (2012) (0)
Fast and Scalable Structural SVM with SlackRescaling (2016) (0)
Related Work Our “ Guess and Check ” ( GnC ) framework draws inspiration from the Thresholdout method of Dwork (2019) (0)
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks (2023) (0)
Maximum Likelihood Markov Hypertrees (2001) (0)
Sparse Data Reconstruction, Missing Value and Multiple Imputation through Matrix Factorization (2022) (0)
Tutorials (2019) (0)
Efficiently Learning Neural Networks: What Assumptions May Suffice? (2023) (0)

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Other Resources About Nathan Srebro

ttic.uchicago.edu

What Schools Are Affiliated With Nathan Srebro?

Nathan Srebro is affiliated with the following schools:

Nathan Srebro's Academic­Influence.com Rankings

Why Is Nathan Srebro Influential?

Nathan Srebro's Published Works

Published Works

Other Resources About Nathan Srebro

What Schools Are Affiliated With Nathan Srebro?

Nathan Srebro's AcademicInfluence.com Rankings