Online Convex Optimization with Unconstrained Domains and Losses. - Ashok Cutkosky; Kwabena Boahen 001 (NIPS, 2016)

Online Learning Without Prior Information. - Ashok Cutkosky; Kwabena Boahen 001 (COLT, 2017)

Stochastic and Adversarial Online Learning without Hyperparameters. - Ashok Cutkosky; Kwabena Boahen 001 (NIPS, 2017)

Black-Box Reductions for Parameter-free Online Learning in Banach Spaces. - Ashok Cutkosky; Francesco Orabona (COLT, 2018)

Distributed Stochastic Optimization via Adaptive SGD. - Ashok Cutkosky; Róbert Busa-Fekete (NeurIPS, 2018)

Distributed Stochastic Optimization via Adaptive Stochastic Gradient Descent. - Ashok Cutkosky; Róbert Busa-Fekete (CoRR, 2018)

Algorithms and lower bounds for parameter-free online learning. - Ashok Cutkosky (2018)

Artificial Constraints and Hints for Unbounded Online Learning. - Ashok Cutkosky (COLT, 2019)

Combining Online Learning Guarantees. - Ashok Cutkosky (COLT, 2019)

Anytime Online-to-Batch, Optimism and Acceleration. - Ashok Cutkosky (ICML, 2019)

Matrix-Free Preconditioning in Online Learning. - Ashok Cutkosky; Tamás Sarlós (ICML, 2019)

Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization. - Zhenxun Zhuang; Ashok Cutkosky; Francesco Orabona (ICML, 2019)

Momentum-Based Variance Reduction in Non-Convex SGD. - Ashok Cutkosky; Francesco Orabona (NeurIPS, 2019)

Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration. - Kwang-Sung Jun; Ashok Cutkosky; Francesco Orabona (NeurIPS, 2019)

Artificial Constraints and Lipschitz Hints for Unconstrained Online Learning. - Ashok Cutkosky (CoRR, 2019)

Anytime Online-to-Batch Conversions, Optimism, and Acceleration. - Ashok Cutkosky (CoRR, 2019)

Online Learning with Imperfect Hints. - Aditya Bhaskara; Ashok Cutkosky; Ravi Kumar 001; Manish Purohit (ICML, 2020)

Parameter-free, Dynamic, and Strongly-Adaptive Online Learning. - Ashok Cutkosky (ICML, 2020)

Momentum Improves Normalized SGD. - Ashok Cutkosky; Harsh Mehta (ICML, 2020)

Online Linear Optimization with Many Hints. - Aditya Bhaskara; Ashok Cutkosky; Ravi Kumar 001; Manish Purohit (NeurIPS, 2020)

Better Full-Matrix Regret via Parameter-Free Online Learning. - Ashok Cutkosky (NeurIPS, 2020)

Comparator-Adaptive Convex Bandits. - Dirk van der Hoeven; Ashok Cutkosky; Haipeng Luo (NeurIPS, 2020)

Adaptive Online Learning with Varying Norms. - Ashok Cutkosky (CoRR, 2020)

Upper Confidence Bounds for Combining Stochastic Bandits. - Ashok Cutkosky; Abhimanyu Das; Manish Purohit (CoRR, 2020)

Power of Hints for Online Learning with Movement Costs. - Aditya Bhaskara; Ashok Cutkosky; Ravi Kumar 001; Manish Purohit (AISTATS, 2021)

Extreme Memorization via Scale of Initialization. - Harsh Mehta; Ashok Cutkosky; Behnam Neyshabur (ICLR, 2021)

Robust Pure Exploration in Linear Bandits with Limited Budget. - Ayya Alieva; Ashok Cutkosky; Abhimanyu Das (ICML, 2021)

Dynamic Balancing for Model Selection in Bandits and RL. - Ashok Cutkosky; Christoph Dann; Abhimanyu Das; Claudio Gentile; Aldo Pacchiano; Manish Purohit (ICML, 2021)

Logarithmic Regret from Sublinear Hints. - Aditya Bhaskara; Ashok Cutkosky; Ravi Kumar 001; Manish Purohit (NeurIPS, 2021)

High-probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails. - Ashok Cutkosky; Harsh Mehta (NeurIPS, 2021)

Online Selective Classification with Limited Feedback. - Aditya Gangrade; Anil Kag; Ashok Cutkosky; Venkatesh Saligrama (NeurIPS, 2021)

Strongly Adaptive OCO with Memory. - Zhiyu Zhang 003; Ashok Cutkosky; Ioannis Ch. Paschalidis (CoRR, 2021)

Correcting Momentum with Second-order Information. - Hoang Tran; Ashok Cutkosky (CoRR, 2021)

Adversarial Tracking Control via Strongly Adaptive Online Learning with Memory. - Zhiyu Zhang 003; Ashok Cutkosky; Ioannis Ch. Paschalidis (AISTATS, 2022)

Implicit Parameter-free Online Learning with Truncated Linear Models. - Keyi Chen 001; Ashok Cutkosky; Francesco Orabona (ALT, 2022)

Leveraging Initial Hints for Free in Stochastic Linear Bandits. - Ashok Cutkosky; Christoph Dann; Abhimanyu Das; Qiuyi (Richard) Zhang (ALT, 2022)

Parameter-free Mirror Descent. - Andrew Jacobsen; Ashok Cutkosky (COLT, 2022)

PDE-Based Optimal Strategy for Unconstrained Online Learning. - Zhiyu Zhang 003; Ashok Cutkosky; Ioannis Ch. Paschalidis (ICML, 2022)

Optimal Comparator Adaptive Online Learning with Switching Cost. - Zhiyu Zhang 003; Ashok Cutkosky; Yannis Paschalidis (NeurIPS, 2022)

Better SGD using Second-order Momentum. - Hoang Tran; Ashok Cutkosky (NeurIPS, 2022)

Momentum Aggregation for Private Non-convex ERM. - Hoang Tran; Ashok Cutkosky (NeurIPS, 2022)

Parameter-free Regret in High Probability with Heavy Tails. - Jiujia Zhang; Ashok Cutkosky (NeurIPS, 2022)

Differentially Private Online-to-batch for Smooth Losses. - Qinzi Zhang; Hoang Tran; Ashok Cutkosky (NeurIPS, 2022)

Large Scale Transfer Learning for Differentially Private Image Classification. - Harsh Mehta; Abhradeep Thakurta; Alexey Kurakin; Ashok Cutkosky (CoRR, 2022)

Optimal Parameter-free Online Learning with Switching Cost. - Zhiyu Zhang 003; Ashok Cutkosky; Ioannis Ch. Paschalidis (CoRR, 2022)

Understanding AdamW through Proximal Methods and Scale-Freeness. - Zhenxun Zhuang; Mingrui Liu; Ashok Cutkosky; Francesco Orabona (Trans. Mach. Learn. Res., 2022)

Long Range Language Modeling via Gated State Spaces. - Harsh Mehta; Ankit Gupta 001; Ashok Cutkosky; Behnam Neyshabur (ICLR, 2023)

Bandit Online Linear Optimization with Hints and Queries. - Aditya Bhaskara; Ashok Cutkosky; Ravi Kumar 001; Manish Purohit (ICML, 2023)

Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion. - Ashok Cutkosky; Harsh Mehta; Francesco Orabona (ICML, 2023)

Unconstrained Online Learning with Unbounded Losses. - Andrew Jacobsen; Ashok Cutkosky (ICML, 2023)

Alternation makes the adversary weaker in two-player games. - Volkan Cevher; Ashok Cutkosky; Ali Kavis; Georgios Piliouras; Stratis Skoulakis; Luca Viano (NeurIPS, 2023)

Mechanic: A Learning Rate Tuner. - Ashok Cutkosky; Aaron Defazio; Harsh Mehta (NeurIPS, 2023)

Unconstrained Dynamic Regret via Sparse Coding. - Zhiyu Zhang 003; Ashok Cutkosky; Yannis Paschalidis (NeurIPS, 2023)

Blackbox optimization of unimodal functions. - Ashok Cutkosky; Abhimanyu Das; Weihao Kong; Chansoo Lee; Rajat Sen (UAI, 2023)

When, Why and How Much? Adaptive Learning Rate Scheduling by Refinement. - Aaron Defazio; Ashok Cutkosky; Harsh Mehta; Konstantin Mishchenko (CoRR, 2023)

Differentially Private Image Classification from Features. - Harsh Mehta; Walid Krichene; Abhradeep Guha Thakurta; Alexey Kurakin; Ashok Cutkosky (Trans. Mach. Learn. Res., 2023)

Towards Large Scale Transfer Learning for Differentially Private Image Classification. - Harsh Mehta; Abhradeep Guha Thakurta; Alexey Kurakin; Ashok Cutkosky (Trans. Mach. Learn. Res., 2023)

Improving Adaptive Online Learning Using Refined Discretization. - Zhiyu Zhang 003; Heng Yang; Ashok Cutkosky; Ioannis Ch. Paschalidis (ALT, 2024)

Private Zeroth-Order Nonsmooth Nonconvex Optimization. - Qinzi Zhang; Hoang Tran; Ashok Cutkosky (ICLR, 2024)

Online Linear Regression in Dynamic Environments via Discounting. - Andrew Jacobsen; Ashok Cutkosky (ICML, 2024)

Random Scaling and Momentum for Non-smooth Non-convex Optimization. - Qinzi Zhang; Ashok Cutkosky (ICML, 2024)

Adam with model exponential moving average is effective for nonconvex optimization. - Kwangjun Ahn; Ashok Cutkosky (NeurIPS, 2024)

Fully Unconstrained Online Learning. - Ashok Cutkosky; Zakaria Mhammedi (NeurIPS, 2024)

The Road Less Scheduled. - Aaron Defazio; Xingyu Yang; Ahmed Khaled 001; Konstantin Mishchenko; Harsh Mehta; Ashok Cutkosky (NeurIPS, 2024)

Empirical Tests of Optimization Assumptions in Deep Learning. - Hoang Tran; Qinzi Zhang; Ashok Cutkosky (CoRR, 2024)

General framework for online-to-nonconvex conversion: Schedule-free SGD is also effective for nonconvex optimization. - Kwangjun Ahn; Gagik Magakyan; Ashok Cutkosky (CoRR, 2024)

Descent with Misaligned Gradients and Applications to Hidden Convexity. - Aditya Bhaskara; Ashok Cutkosky; Ravi Kumar 001; Manish Purohit (ICLR, 2025)

Unconstrained Robust Online Convex Optimization. - Jiujia Zhang; Ashok Cutkosky (CoRR, 2025)