Ashok Cutkosky

52 papers · 2016–2025 · 6 conferences · across top CS/AI conferences

Achievements

🏃 Academic Marathon (9) 🌈 Renaissance Researcher (6) 🌉 Interdisciplinary Bridge 🗺️ Taxonomy Completionist (10) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (14) 🌍 Conference Polyglot (6) 🐺 Lone Wolf (5) 🏠 Conference Loyalist (22) 👑 Triple Crown 🔬 Deep Specialist (25) 🏆 Keyword Champion (7) ⚡ Prolific Year (7) 🗃️ Keyword Collector (133) 📈 Trend Setter 🔥 Unstoppable (10) 💎 Century Club (52)

Conferences

NIPS (22) ICML (16) COLT (5) ICLR (4) ALT (3) AISTATS (2)

Top co-authors

Manish Purohit (7) Harsh Mehta (7) Ravi Kumar (6) Aditya Bhaskara (6) Francesco Orabona (6) Zhiyu Zhang (5) Hoang Tran (4) Abhimanyu Das (3) Qinzi Zhang (3) Andrew Jacobsen (3)

Research topics

Differential Privacy (2)

Keywords

regret bound (24) online learning (17) online convex optimization (9) online linear optimization (7) non-convex optimization (7) stochastic optimization (6) stochastic gradient descent (6) gradient descent (6) parameter-free algorithm (4) dynamic regret (4) adaptive learning rate (3) learning rate (3) convex optimization (3) adaptive algorithm (3) online algorithm (3) momentum method (3) multi-armed bandit (2) differential privacy (2) stochastic convex optimization (2) adversarial learning (2)

Papers

General framework for online-to-nonconvex conversion: Schedule-free SGD is also effective for nonconvex optimization ICML 2025

Descent with Misaligned Gradients and Applications to Hidden Convexity ICLR 2025

Unconstrained Robust Online Convex Optimization ICML 2025

Private Zeroth-Order Nonsmooth Nonconvex Optimization ICLR 2024

Improving Adaptive Online Learning Using Refined Discretization ALT 2024

Adam with model exponential moving average is effective for nonconvex optimization NIPS 2024

The Road Less Scheduled NIPS 2024

Fully Unconstrained Online Learning NIPS 2024

Random Scaling and Momentum for Non-smooth Non-convex Optimization ICML 2024

Online Linear Regression in Dynamic Environments via Discounting ICML 2024

Bandit Online Linear Optimization with Hints and Queries ICML 2023

Alternation makes the adversary weaker in two-player games NIPS 2023

Mechanic: A Learning Rate Tuner NIPS 2023

Unconstrained Dynamic Regret via Sparse Coding NIPS 2023

Long Range Language Modeling via Gated State Spaces ICLR 2023

Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion ICML 2023

Unconstrained Online Learning with Unbounded Losses ICML 2023

Parameter-free Regret in High Probability with Heavy Tails NIPS 2022

Momentum Aggregation for Private Non-convex ERM NIPS 2022

Adversarial Tracking Control via Strongly Adaptive Online Learning with Memory AISTATS 2022

Implicit Parameter-free Online Learning with Truncated Linear Models ALT 2022

Leveraging Initial Hints for Free in Stochastic Linear Bandits ALT 2022

Differentially Private Online-to-batch for Smooth Losses NIPS 2022

Parameter-free Mirror Descent COLT 2022

PDE-Based Optimal Strategy for Unconstrained Online Learning ICML 2022

Optimal Comparator Adaptive Online Learning with Switching Cost NIPS 2022

Better SGD using Second-order Momentum NIPS 2022

Robust Pure Exploration in Linear Bandits with Limited Budget ICML 2021

Power of Hints for Online Learning with Movement Costs AISTATS 2021

Logarithmic Regret from Sublinear Hints NIPS 2021

High-probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails NIPS 2021

Dynamic Balancing for Model Selection in Bandits and RL ICML 2021

Extreme Memorization via Scale of Initialization ICLR 2021

Online Selective Classification with Limited Feedback NIPS 2021

Comparator-Adaptive Convex Bandits NIPS 2020

Online Linear Optimization with Many Hints NIPS 2020

Better Full-Matrix Regret via Parameter-Free Online Learning NIPS 2020

Online Learning with Imperfect Hints ICML 2020

Parameter-free, Dynamic, and Strongly-Adaptive Online Learning ICML 2020

Momentum Improves Normalized SGD ICML 2020

Momentum-Based Variance Reduction in Non-Convex SGD NIPS 2019

Combining Online Learning Guarantees COLT 2019

Artificial Constraints and Hints for Unbounded Online Learning COLT 2019

Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration NIPS 2019

Matrix-Free Preconditioning in Online Learning ICML 2019

Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization ICML 2019

Anytime Online-to-Batch, Optimism and Acceleration ICML 2019

Distributed Stochastic Optimization via Adaptive SGD NIPS 2018

Black-Box Reductions for Parameter-free Online Learning in Banach Spaces COLT 2018

Online Learning Without Prior Information COLT 2017

Stochastic and Adversarial Online Learning without Hyperparameters NIPS 2017

Online Convex Optimization with Unconstrained Domains and Losses NIPS 2016