← Authors

Jelena Diakonikolas

28 papers · 2018–2025 · 6 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (10) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (7) 🐝 Cross-Pollinator (13) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸ”¬ Deep Specialist (15) 🧬 Topic Evolution πŸ‘‘ Triple Crown πŸ† Keyword Champion (2) πŸ—ƒοΈ Keyword Collector (109) ⚑ Prolific Year (6) πŸ’Ž Century Club (28) πŸ”₯ Unstoppable (8)

Conferences

NIPS (10) ICML (8) COLT (4) AISTATS (3) ICLR (2) JMLR (1)

Top co-authors

Ilias Diakonikolas (8) Chaobing Song (6) Nikos Zarifis (6) Puqian Wang (6) Xufeng Cai (5) Cheuk Yin Lin (4) Stephen Wright (3) CristΓ³bal GuzmΓ‘n (3) Alejandro Carderera (2) Michael I. Jordan (2)

Research topics

Probability (1)

Keywords

convex optimization (8) variance reduction (7) sample complexity (4) primal-dual algorithm (3) robust learning (3) adversarial label noise (3) conditional gradient (2) robust optimization (2) randomized algorithm (2) convergence analysis (2) stochastic optimization (2) composite optimization (2) projection-free optimization (2) distributionally robust optimization (2) strong convexity (2) statistical query (2) coordinate descent (2) learning theory (2) nonconvex optimization (2) oracle complexity (2)

Papers

Last Iterate Convergence of Incremental Methods as a Model of Forgetting ICLR 2025
Robustly Learning Monotone Generalized Linear Models via Data Augmentation COLT 2025
Robustly Learning Single-Index Models via Alignment Sharpness ICML 2024
Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions ICLR 2024
Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models NIPS 2024
Tighter Convergence Bounds for Shuffled SGD via Primal-Dual Perspective NIPS 2024
Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise NIPS 2024
Drago: Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization NIPS 2024
Block-Coordinate Methods and Restarting for Solving Extensive-Form Games NIPS 2023
Accelerated Cyclic Coordinate Dual Averaging with Extrapolation for Composite Convex Optimization ICML 2023
Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise NIPS 2023
Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing NIPS 2023
Robustly Learning a Single Neuron via Sharpness ICML 2023
Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization ICML 2023
Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise COLT 2023
Coordinate Linear Variance Reduction for Generalized Linear Programming NIPS 2022
Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions NIPS 2022
A Fast Scale-Invariant Algorithm for Non-negative Least Squares with Non-negative Data NIPS 2022
Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization AISTATS 2021
Parameter-free Locally Accelerated Conditional Gradients ICML 2021
Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums ICML 2021
Lower Bounds for Parallel and Randomized Convex Optimization JMLR 2020
Langevin Monte Carlo without smoothness AISTATS 2020
Locally Accelerated Conditional Gradients AISTATS 2020
Halpern Iteration for Near-Optimal and Parameter-Free Monotone Inclusion and Strong Solutions to Variational Inequalities COLT 2020
Lower Bounds for Parallel and Randomized Convex Optimization COLT 2019
Alternating Randomized Block Coordinate Descent ICML 2018
On Acceleration with Noise-Corrupted Gradients ICML 2018