Sotabase

Career

· Associate Professor of Statistics and Electrical Engineering, Stanford University2021–
· Assistant Professor of Statistics and Electrical Engineering, Stanford University2014–2021
· Instructor, University of California, Berkeley2011–
· Software Engineer, Google2009–2014
· Research Assistant, University of California, Berkeley2008–2014
· Software Engineer, Google2007–2008
· Research Assistant, Stanford University2006–2007

Publications (375)

Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
Journal of machine learning research · 2011
11,102
cited
Efficient projections onto the l1-ball for learning in high dimensions
International Conference on Machine Learning · 2008
1,534
cited
Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling
IEEE Transactions on Automatic Control · 2010
1,275
cited
Local Privacy and Statistical Minimax Rates
IEEE Annual Symposium on Foundations of Computer Science · 2013
1,239
cited
Certifying Some Distributional Robustness with Principled Adversarial Training
International Conference on Learning Representations · 2017
919
cited
Generalizing to Unseen Domains via Adversarial Data Augmentation
Neural Information Processing Systems · 2018
879
cited
Unlabeled Data Improves Adversarial Robustness
Neural Information Processing Systems · 2019
794
cited
Efficient Online and Batch Learning Using Forward Backward Splitting
Journal of machine learning research · 2009
660
cited
Distributed delayed stochastic optimization
IEEE Conference on Decision and Control · 2011
642
cited
Communication-efficient algorithms for statistical optimization
IEEE Conference on Decision and Control · 2012
600
cited
Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations
IEEE Transactions on Information Theory · 2013
549
cited
Minimax Optimal Procedures for Locally Private Estimation
arXiv.org · 2016
479
cited
Learning Models with Uniform Performance via Distributionally Robust Optimization
arXiv.org · 2018
478
cited
Lower bounds for non-convex stochastic optimization
Mathematical programming · 2019
415
cited
Divide and conquer kernel ridge regression: a distributed algorithm with minimax optimal rates
Journal of machine learning research · 2013
398
cited
Protection Against Reconstruction and Its Applications in Private Federated Learning
arXiv.org · 2018
393
cited
Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences
Neural Information Processing Systems · 2016
386
cited
MLbase: A Distributed Machine-learning System
Conference on Innovative Data Systems Research · 2013
377
cited
Certifiable Distributional Robustness with Principled Adversarial Training
arXiv.org · 2017
367
cited
Variance-based Regularization with Convex Objectives
Neural Information Processing Systems · 2016
365
cited
Sotabase