Nati Srebro
45 papers · 2011–2023 · 3 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π§ Keyword Pioneer π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (16) π Conference Polyglot (3)
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(16)
π
Conference Polyglot
(3)
π§
Keyword Pioneer
π
Keyword Trendsetter Combo
(6)
π
Conference Loyalist
(41)
π¬
Deep Specialist
(21)
π
Keyword Champion
(2)
π
Trend Setter
π₯
Unstoppable
(6)
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Conference Pioneer
β‘
Prolific Year
(5)
π
Century Club
(45)
ποΈ
Keyword Collector
(52)
β
The Questioner
Conferences
NIPS (41)
ICML (3)
AISTATS (1)
Top co-authors
Research topics
Keywords
gradient descent
(7)
stochastic optimization
(7)
convex optimization
(5)
sample complexity
(5)
learning theory
(5)
stochastic gradient descent
(5)
implicit bia
(4)
distributed optimization
(4)
online learning
(4)
matrix factorization
(4)
uniform convergence
(4)
gradient flow
(3)
regret bound
(3)
matrix completion
(3)
generalized linear model
(3)
neural network
(3)
query complexity
(3)
support vector machine
(3)
generalization bound
(3)
linear regression
(2)
Papers
Uniform Convergence with Square-Root Lipschitz Loss
NIPS 2023
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks
NIPS 2023
Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy
NIPS 2023
When is Agnostic Reinforcement Learning Statistically Tractable?
NIPS 2023
Most Neural Networks Are Almost Learnable
NIPS 2023
Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization
NIPS 2022
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models
NIPS 2022
Pessimism for Offline Linear Contextual Bandits using $\ell_p$ Confidence Sets
NIPS 2022
The Sample Complexity of One-Hidden-Layer Neural Networks
NIPS 2022
Towards Optimal Communication Complexity in Distributed Non-Convex Optimization
NIPS 2022
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization
NIPS 2022
On Margin Maximization in Linear and ReLU Networks
NIPS 2022
Exponential Family Model-Based Reinforcement Learning via Score Matching
NIPS 2022
Understanding the Eluder Dimension
NIPS 2022
On Uniform Convergence and Low-Norm Interpolation Learning
NIPS 2020
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy
NIPS 2020
Reducing Adversarially Robust Learning to Non-Robust PAC Learning
NIPS 2020
Minibatch vs Local SGD for Heterogeneous Distributed Learning
NIPS 2020
The Everlasting Database: Statistical Validity at a Fair Price
NIPS 2018
Implicit Bias of Gradient Descent on Linear Convolutional Networks
NIPS 2018
On preserving non-discrimination when combining expert advice
NIPS 2018
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
NIPS 2018
Stochastic Approximation for Canonical Correlation Analysis
NIPS 2017
Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data
AISTATS 2017
The Marginal Value of Adaptive Gradient Methods in Machine Learning
NIPS 2017
Implicit Regularization in Matrix Factorization
NIPS 2017
Exploring Generalization in Deep Learning
NIPS 2017
Normalized Spectral Map Synchronization
NIPS 2016
Global Optimality of Local Search for Low Rank Matrix Recovery
NIPS 2016
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations
NIPS 2016
Equality of Opportunity in Supervised Learning
NIPS 2016
Tight Complexity Bounds for Optimizing Composite Objectives
NIPS 2016
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis
NIPS 2016
Path-SGD: Path-Normalized Optimization in Deep Neural Networks
NIPS 2015
Communication-Efficient Distributed Optimization using an Approximate Newton-type Method
ICML 2014
Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm
NIPS 2014
The Power of Asymmetry in Binary Hashing
NIPS 2013
Learning Optimally Sparse Support Vector Machines
ICML 2013
Mini-Batch Primal and Dual Methods for SVMs
ICML 2013
Stochastic Optimization of PCA with Capped MSG
NIPS 2013
Auditing: Active Learning with Outcome-Dependent Query Costs
NIPS 2013
Beating SGD: Learning SVMs in Sublinear Time
NIPS 2011
Learning with the weighted trace-norm under arbitrary sampling distributions
NIPS 2011
Better Mini-Batch Algorithms via Accelerated Gradient Methods
NIPS 2011
On the Universality of Online Mirror Descent
NIPS 2011