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Nati Srebro

45 papers · 2011–2023 · 3 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (6) 🐝 Cross-Pollinator (7) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (12) πŸ—ΊοΈ Taxonomy Completionist (16) 🌍 Conference Polyglot (3) 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (6) 🏠 Conference Loyalist (41) πŸ”¬ Deep Specialist (21) πŸ† Keyword Champion (2) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (6) πŸš€ Conference Pioneer ⚑ Prolific Year (5) πŸ’Ž Century Club (45) πŸ—ƒοΈ Keyword Collector (52) ❓ The Questioner

Conferences

NIPS (41) ICML (3) AISTATS (1)

Top co-authors

Blake E Woodworth (7) Behnam Neyshabur (6) Gal Vardi (5) Ohad Shamir (5) Gene Li (4) Ruslan Salakhutdinov (4) Suriya Gunasekar (4) Jialei Wang (3) Lijia Zhou (3) Srinadh Bhojanapalli (3)

Research topics

Optimization (1)

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