Ashok Cutkosky

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 96

Top Topics

Top Venues

Publications

Qinzi Zhang, Ashok Cutkosky
Random Scaling and Momentum for Non-smooth Non-convex Optimization. CoRR (2024)
Qinzi Zhang, Hoang Tran, Ashok Cutkosky
Private Zeroth-Order Nonsmooth Nonconvex Optimization. ICLR (2024)
Andrew Jacobsen, Ashok Cutkosky
Online Linear Regression in Dynamic Environments via Discounting. CoRR (2024)
Ashok Cutkosky, Zakaria Mhammedi
Fully Unconstrained Online Learning. CoRR (2024)
Aaron Defazio, Xingyu Yang, Harsh Mehta, Konstantin Mishchenko, Ahmed Khaled, Ashok Cutkosky
The Road Less Scheduled. CoRR (2024)
Kwangjun Ahn, Ashok Cutkosky
Adam with model exponential moving average is effective for nonconvex optimization. CoRR (2024)
Qinzi Zhang, Hoang Tran, Ashok Cutkosky
Private Zeroth-Order Nonsmooth Nonconvex Optimization. CoRR (2024)
Hoang Tran, Qinzi Zhang, Ashok Cutkosky
Empirical Tests of Optimization Assumptions in Deep Learning. CoRR (2024)
Zhiyu Zhang, Heng Yang, Ashok Cutkosky, Ioannis Ch. Paschalidis
Improving Adaptive Online Learning Using Refined Discretization. ALT (2024)
Ashok Cutkosky, Abhimanyu Das, Weihao Kong, Chansoo Lee, Rajat Sen
Blackbox optimization of unimodal functions. UAI (2023)
Harsh Mehta, Walid Krichene, Abhradeep Guha Thakurta, Alexey Kurakin, Ashok Cutkosky
Differentially Private Image Classification from Features. Trans. Mach. Learn. Res. 2023 (2023)
Zhiyu Zhang, Ashok Cutkosky, Ioannis Ch. Paschalidis
Unconstrained Dynamic Regret via Sparse Coding. CoRR (2023)
Ashok Cutkosky, Harsh Mehta, Francesco Orabona
Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion. ICML (2023)
Zhiyu Zhang, Ashok Cutkosky, Yannis Paschalidis
Unconstrained Dynamic Regret via Sparse Coding. NeurIPS (2023)
Andrew Jacobsen, Ashok Cutkosky
Unconstrained Online Learning with Unbounded Losses. CoRR (2023)
Ashok Cutkosky, Aaron Defazio, Harsh Mehta
Mechanic: A Learning Rate Tuner. CoRR (2023)
Andrew Jacobsen, Ashok Cutkosky
Unconstrained Online Learning with Unbounded Losses. ICML (2023)
Harsh Mehta, Abhradeep Guha Thakurta, Alexey Kurakin, Ashok Cutkosky
Towards Large Scale Transfer Learning for Differentially Private Image Classification. Trans. Mach. Learn. Res. 2023 (2023)
Ashok Cutkosky, Aaron Defazio, Harsh Mehta
Mechanic: A Learning Rate Tuner. NeurIPS (2023)
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
Bandit Online Linear Optimization with Hints and Queries. ICML (2023)
Volkan Cevher, Ashok Cutkosky, Ali Kavis, Georgios Piliouras, Stratis Skoulakis, Luca Viano
Alternation makes the adversary weaker in two-player games. NeurIPS (2023)
Ashok Cutkosky, Harsh Mehta, Francesco Orabona
Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion. CoRR (2023)
Aaron Defazio, Ashok Cutkosky, Harsh Mehta, Konstantin Mishchenko
When, Why and How Much? Adaptive Learning Rate Scheduling by Refinement. CoRR (2023)
Harsh Mehta, Ankit Gupta, Ashok Cutkosky, Behnam Neyshabur
Long Range Language Modeling via Gated State Spaces. ICLR (2023)
Zhiyu Zhang, Heng Yang, Ashok Cutkosky, Ioannis Ch. Paschalidis
Improving Adaptive Online Learning Using Refined Discretization. CoRR (2023)
Keyi Chen, Ashok Cutkosky, Francesco Orabona
Implicit Parameter-free Online Learning with Truncated Linear Models. ALT (2022)
Andrew Jacobsen, Ashok Cutkosky
Parameter-free Mirror Descent. CoRR (2022)
Zhiyu Zhang, Ashok Cutkosky, Yannis Paschalidis
Optimal Comparator Adaptive Online Learning with Switching Cost. NeurIPS (2022)
Keyi Chen, Ashok Cutkosky, Francesco Orabona
Implicit Parameter-free Online Learning with Truncated Linear Models. CoRR (2022)
Harsh Mehta, Walid Krichene, Abhradeep Thakurta, Alexey Kurakin, Ashok Cutkosky
Differentially Private Image Classification from Features. CoRR (2022)
Jiujia Zhang, Ashok Cutkosky
Parameter-free Regret in High Probability with Heavy Tails. CoRR (2022)
Zhiyu Zhang, Ashok Cutkosky, Ioannis Ch. Paschalidis
PDE-Based Optimal Strategy for Unconstrained Online Learning. CoRR (2022)
Zhiyu Zhang, Ashok Cutkosky, Ioannis Ch. Paschalidis
Optimal Parameter-free Online Learning with Switching Cost. CoRR (2022)
Zhiyu Zhang, Ashok Cutkosky, Ioannis Ch. Paschalidis
Adversarial Tracking Control via Strongly Adaptive Online Learning with Memory. AISTATS (2022)
Harsh Mehta, Ankit Gupta, Ashok Cutkosky, Behnam Neyshabur
Long Range Language Modeling via Gated State Spaces. CoRR (2022)
Andrew Jacobsen, Ashok Cutkosky
Parameter-free Mirror Descent. COLT (2022)
Hoang Tran, Ashok Cutkosky
Momentum Aggregation for Private Non-convex ERM. NeurIPS (2022)
Qinzi Zhang, Hoang Tran, Ashok Cutkosky
Differentially Private Online-to-batch for Smooth Losses. NeurIPS (2022)
Zhenxun Zhuang, Mingrui Liu, Ashok Cutkosky, Francesco Orabona
Understanding AdamW through Proximal Methods and Scale-Freeness. CoRR (2022)
Zhiyu Zhang, Ashok Cutkosky, Ioannis Ch. Paschalidis
PDE-Based Optimal Strategy for Unconstrained Online Learning. ICML (2022)
Harsh Mehta, Abhradeep Thakurta, Alexey Kurakin, Ashok Cutkosky
Large Scale Transfer Learning for Differentially Private Image Classification. CoRR (2022)
Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Qiuyi (Richard) Zhang
Leveraging Initial Hints for Free in Stochastic Linear Bandits. ALT (2022)
Hoang Tran, Ashok Cutkosky
Better SGD using Second-order Momentum. NeurIPS (2022)
Hoang Tran, Ashok Cutkosky
Momentum Aggregation for Private Non-convex ERM. CoRR (2022)
Jiujia Zhang, Ashok Cutkosky
Parameter-free Regret in High Probability with Heavy Tails. NeurIPS (2022)
Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Qiuyi (Richard) Zhang
Leveraging Initial Hints for Free in Stochastic Linear Bandits. CoRR (2022)
Qinzi Zhang, Hoang Tran, Ashok Cutkosky
Differentially Private Online-to-Batch for Smooth Losses. CoRR (2022)
Zhenxun Zhuang, Mingrui Liu, Ashok Cutkosky, Francesco Orabona
Understanding AdamW through Proximal Methods and Scale-Freeness. Trans. Mach. Learn. Res. 2022 (2022)
Hoang Tran, Ashok Cutkosky
Correcting Momentum with Second-order Information. CoRR (2021)
Harsh Mehta, Ashok Cutkosky, Behnam Neyshabur
Extreme Memorization via Scale of Initialization. ICLR (2021)
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
Logarithmic Regret from Sublinear Hints. NeurIPS (2021)
Zhiyu Zhang, Ashok Cutkosky, Ioannis Ch. Paschalidis
Strongly Adaptive OCO with Memory. CoRR (2021)
Aditya Gangrade, Anil Kag, Ashok Cutkosky, Venkatesh Saligrama
Online Selective Classification with Limited Feedback. CoRR (2021)
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
Power of Hints for Online Learning with Movement Costs. AISTATS (2021)
Ashok Cutkosky, Harsh Mehta
High-probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails. NeurIPS (2021)
Ayya Alieva, Ashok Cutkosky, Abhimanyu Das
Robust Pure Exploration in Linear Bandits with Limited Budget. ICML (2021)
Aditya Gangrade, Anil Kag, Ashok Cutkosky, Venkatesh Saligrama
Online Selective Classification with Limited Feedback. NeurIPS (2021)
Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Claudio Gentile, Aldo Pacchiano, Manish Purohit
Dynamic Balancing for Model Selection in Bandits and RL. ICML (2021)
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
Logarithmic Regret from Sublinear Hints. CoRR (2021)
Ashok Cutkosky, Harsh Mehta
High-probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails. CoRR (2021)
Ashok Cutkosky, Abhimanyu Das, Manish Purohit
Upper Confidence Bounds for Combining Stochastic Bandits. CoRR (2020)
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
Online Linear Optimization with Many Hints. NeurIPS (2020)
Ashok Cutkosky, Harsh Mehta
Momentum Improves Normalized SGD. ICML (2020)
Ashok Cutkosky
Adaptive Online Learning with Varying Norms. CoRR (2020)
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
Online Linear Optimization with Many Hints. CoRR (2020)
Dirk van der Hoeven, Ashok Cutkosky, Haipeng Luo
Comparator-Adaptive Convex Bandits. NeurIPS (2020)
Ashok Cutkosky
Parameter-free, Dynamic, and Strongly-Adaptive Online Learning. ICML (2020)
Ashok Cutkosky
Better Full-Matrix Regret via Parameter-Free Online Learning. NeurIPS (2020)
Ashok Cutkosky, Harsh Mehta
Momentum Improves Normalized SGD. CoRR (2020)
Dirk van der Hoeven, Ashok Cutkosky, Haipeng Luo
Comparator-adaptive Convex Bandits. CoRR (2020)
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
Online Learning with Imperfect Hints. CoRR (2020)
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
Online Learning with Imperfect Hints. ICML (2020)
Harsh Mehta, Ashok Cutkosky, Behnam Neyshabur
Extreme Memorization via Scale of Initialization. CoRR (2020)
Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration. CoRR (2019)
Ashok Cutkosky
Anytime Online-to-Batch Conversions, Optimism, and Acceleration. CoRR (2019)
Ashok Cutkosky, Tamás Sarlós
Matrix-Free Preconditioning in Online Learning. ICML (2019)
Zhenxun Zhuang, Ashok Cutkosky, Francesco Orabona
Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization. ICML (2019)
Ashok Cutkosky
Artificial Constraints and Hints for Unbounded Online Learning. COLT (2019)
Ashok Cutkosky, Tamás Sarlós
Matrix-Free Preconditioning in Online Learning. CoRR (2019)
Ashok Cutkosky
Combining Online Learning Guarantees. COLT (2019)
Ashok Cutkosky, Francesco Orabona
Momentum-Based Variance Reduction in Non-Convex SGD. CoRR (2019)
Zhenxun Zhuang, Ashok Cutkosky, Francesco Orabona
Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization. CoRR (2019)
Ashok Cutkosky
Anytime Online-to-Batch, Optimism and Acceleration. ICML (2019)
Ashok Cutkosky, Francesco Orabona
Momentum-Based Variance Reduction in Non-Convex SGD. NeurIPS (2019)
Ashok Cutkosky
Combining Online Learning Guarantees. CoRR (2019)
Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration. NeurIPS (2019)
Ashok Cutkosky
Artificial Constraints and Lipschitz Hints for Unconstrained Online Learning. CoRR (2019)
Ashok Cutkosky, Róbert Busa-Fekete
Distributed Stochastic Optimization via Adaptive SGD. NeurIPS (2018)
Ashok Cutkosky, Francesco Orabona
Black-Box Reductions for Parameter-free Online Learning in Banach Spaces. COLT (2018)
Ashok Cutkosky, Francesco Orabona
Black-Box Reductions for Parameter-free Online Learning in Banach Spaces. CoRR (2018)
Ashok Cutkosky, Róbert Busa-Fekete
Distributed Stochastic Optimization via Adaptive Stochastic Gradient Descent. CoRR (2018)
Ashok Cutkosky, Kwabena Boahen
Stochastic and Adversarial Online Learning without Hyperparameters. NIPS (2017)
Ashok Cutkosky, Kwabena Boahen
Online Learning Without Prior Information. COLT (2017)
Ashok Cutkosky, Kwabena Boahen
Online Learning Without Prior Information. CoRR (2017)
Ashok Cutkosky, Kwabena Boahen
Online Convex Optimization with Unconstrained Domains and Losses. CoRR (2017)
Ashok Cutkosky, Kwabena Boahen
Online Convex Optimization with Unconstrained Domains and Losses. NIPS (2016)