Jelena Diakonikolas
28 papers · 2018–2025 · 6 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+10 more ↓ Show less ↑
🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🗺️ Taxonomy Completionist (10) 🧭 Keyword Pioneer 🌍 Conference Polyglot (6)
🧭
Keyword Pioneer
🐣
Hot Topic Early Bird
🔬
Deep Specialist
(15)
🧬
Topic Evolution
👑
Triple Crown
🏆
Keyword Champion
(2)
🗃️
Keyword Collector
(109)
⚡
Prolific Year
(6)
💎
Century Club
(28)
🔥
Unstoppable
(8)
Conferences
NIPS (10)
ICML (8)
COLT (4)
AISTATS (3)
ICLR (2)
JMLR (1)
Top co-authors
Research topics
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