Ashok Cutkosky
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I am an assistant professor at Boston University in the ECE department.
Previously, I was a research scientist at Google.
I earned a PhD in computer science at Stanford
University in 2018 under the supervision of Kwabena Boahen, and a AB in mathematics from Harvard in 2013. I am also a master of medicine.
I'm currently excited about optimization algorithms for machine learning. I have recently worked on non-convex optimization as well as adaptive online learning.
My email address is ashok (at) cutkosky (dot) com.
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Selected Publications
- The Road Less Scheduled, Aaron Defazio, Xingyu Alice Yang, Harsh Mehta, Konstantin Mishchenko, Ahmed Khaled, Ashok Cutkosky. Neural Information Processinng Systems (NeurIPS), 2024.
- Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion, Ashok Cutkosky, Harsh Mehta and Francesco Orabona. International Conference on Machine Learning (ICML), 2023.
- Mechanic: A Learning Rate Tuner, Ashok Cutkosky, Aaron Defazio and Harsh Mehta. Neural Information Processsing Systems (NeurIPS), 2023.
- Momentum-Based Variance Reduction in Non-Convex SGD, Ashok Cutkosky and Francesco Orabona. Neural Information Processsing Systems (NeurIPS), 2019.
- Anytime Online-to-Batch, Optimism, and Acceleration, Ashok Cutkosky. International Conference on Machine Learning (ICML), 2019.
- Black-Box Reductions for Parameter-free Online Learning in Banach Spaces, Ashok Cutkosky and Francesco Orabona, Conference on Learning Theory (COLT), 2018
Current Students
- Hoang Tran
- Jiujia Zhang
- Qinzi Zhang
- Elly Wang
- Peter Gu
Past Students (PhD)
Past Students (undergraduate)
- Fangrui Huang
- Rana Boustany
- Michelle Zyman
- Vance Raiti
Teaching
- Spring 2023-2024: EC414 Introduction to Machine Learning
- Fall 2021-2024: EC525 Optimization for Machine Learning (website)
- Fall 2020: EC524 Deep Learning (co-taught with Brian Kulis)
All Publications
In 2023 I achieved my stretch-goal for academic productivity: I became too lazy to keep this list fully up-to-date. If it looks out of date, please check out my
google scholar profile. Try sorting by year rather than citation to see what I've been working on recently.
- Mechanic: a Learning Rate Tuner, Ashok Cutkosky, Aaron Defazio, Harsh Mehta. Neural Information Processing Systems (NeurIPS) 2023.
- Alternation makes the adversary weaker in two-player games, Volkan Cevher, Ashok Cutkosky, Ali Kavis, Georgios Piliouras, Stratis Skoulakis, Luca Viano. Neural Information Processing Systems (NeurIPS) 2023.
- Unconstrained dynamic regret via sparse coding, Zhiyu Zhang, Ashok Cutkosky, Ioannis Ch Paschalidis. Neural Information Processing Systems (NeurIPS) 2023.
- Bandit Online Linear Optimization with Hints and Queries, Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit. International Conference on Machine Learning (ICML) 2023.
- Unconstrained Online Learning with Unbounded Losses, Andrew Jacobsen, Ashok Cutkosky. International Conference on Machine Learning (ICML) 2023.
- Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversion, Ashok Cutkosky, Harsh Mehta, Francesco Orabona. International Conference on Machine Learning (ICML) 2023.
- Blackbox optimization of unimodal functions, Ashok Cutkosky, Abhimanyu Das, Weihao Kong, Chansoo Lee, Rajat Sen. Uncertainty in Artificial Intelligence (UAI) 2023.
- Optimal Comparator Adaptive Online Learning with Switching Cost, Zhiyu Zhang, Ashok Cutkosky, Yannis Paschalidis. Neural Information Processing Systems (NeurIPS) 2022.
- Parameter-free regret in high probability with heavy tails, Jiujia Zhang, Ashok Cutkosky. Neural Information Processing Systems (NeurIPS) 2022.
- Momentum aggregation for private non-convex erm, Hoang Tran, Ashok Cutkosky. International Conference on Machine Learning (ICML) 2023.
- Better sgd using second-order momentum, Hoang Tran, Ashok Cutkosky. Neural Information Processing Systems (NeurIPS) 2022.
- Differentially Private Online-to-Batch for Smooth Losses, Qinzi Zhang, Hoang Tran, Ashok Cutkosky. Neural Information Processing Systems (NeurIPS) 2022.
- PDE-Based Optimal Strategy for Unconstrained Online Learning, Zhiyu Zhang, Ashok Cutkosky, and Yannis Paschalidis. International Conference on Machine Learning (ICML) 2022.
- Parameter-Free Mirror Descent, Andrew Jacobsen and Ashok Cutkosky. Conference on Learning Theory (COLT) 2022.
- Leveraging Initial Hints for Free in Stochastic Linear Bandits, Ashok Cutkosky, Chris Dann, Abhimanyu Das, Qiuyi (Richard) Zhang. International Conference on Algorithmic Learning Theory (ALT) 2022.
- Implicit Parameter-free Online Learning with Truncated Linear Models, Keyi Chen, Ashok Cutkosky, Francesco Orabona. International Conference on Algorithmic Learning Theory (ALT) 2022.
- Adversarial Tracking Control via Strongly Adaptive Online Learning with Memory, Zhiyu Zhang, Ashok Cutkosky, Yannis Paschalidis. International Conference on Artificial Intillgence and Statistics (AISTATS) 2022.
- High-probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails, Ashok Cutkosky, Harsh Mehta. Neural Information Processing Systems (NeurIPS) 2021. [oral]
- Logarithmic Regret from Sublinear Hints, Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit. Neural Information Processing Systems (NeurIPS) 2021.
- Online Selective Classification with Limited Feedback, Aditya Gangrade, Anil Kag, Ashok Cutkosky, Venkatesh Saligrama. Neural Information Processing Systems (NeurIPS) 2021. [spotlight]
- Dynamic Balancing for Model Selection in Bandits and RL, Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Claudio Gentile, Aldo Pacchiano, Manish Purohit. International Conference on Machine Learning (ICML) 2021.
- Robust Pure Exploration in Linear Bandits with Limited Budget, Ayya Alieva, Ashok Cutkosky, Abhimanyu Das. International Conference on Machine Learning (ICML) 2021.
- Power of Hints for Online Learning with Movement Costs, Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit. International Conference on Artificial Intillgence and Statistics (AISTATS) 2021.
- Extreme Memorization via Scale of Initialization. Harsh Mehta, Ashok Cutkosky, Benham Neyshabur. International Conference on Learning Representations (ICLR) 2021.
- Online Linear Optimization with Many Hints, Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit. Advances Neural Information Processing Systems (NeurIPS), 2020.
- Comparator-Adaptive Convex Bandits, Dirk van der Hoeven, Ashok Cutkosky, Haipeng Luo. Advances in Neural Information Processing Systems (NeurIPS), 2020.
- Better Full-Matrix Regret via Parameter-free Online Learning, Ashok Cutkosky. Advances in Neural Information Processing Systems (NeurIPS), 2020.
- Momentum Improves Normalized SGD, Ashok Cutkosky and Harsh Mehta. International Conference on Machine Learning (ICML), 2020.
- Online Learning with Imperfect Hints, Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, and Manish Purohit. International Conference on Machine Learning (ICML), 2020
- Parameter-Free, Dynamic, and Strongly-Adaptive Online Learning, Ashok Cutkosky. International Conference on Machine Learning (ICML), 2020
- Momentum-Based Variance Reduction in Non-Convex SGD, Ashok Cutkosky and Francesco Orabona. Neural Information Processsing Systems (NeurIPS), 2019.
- Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration, Kwang-Sung Jun, Ashok Cutkosky, Francesco Orabona. Neural Information Processing Systems (NeurIPS), 2019.
- Matrix-Free Preconditioning in Online Learning, Ashok Cutkosky and Tamas Sarlos. International Conference on Machine Learning (ICML), 2019. [code] [long talk]
- Anytime Online-to-Batch, Optimism, and Acceleration, Ashok Cutkosky. International Conference on Machine Learning (ICML), 2019. [long talk]
- Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization, Zhenxun Zhuang, Ashok Cutkosky, and Francesco Orabona. International Conference on Machine Learning (ICML), 2019.
- Artificial Constraints and Hints for Unbounded Online Learning, Ashok Cutkosky. Conference on Learning Theory (COLT), 2019.
- Combining Online Learning Guarantees, Ashok Cutkosky. Conference on Learning Theory (COLT), 2019.
- Distributed Stochastic Optimization via Adaptive Stochastic Gradient Descent, Ashok Cutkosky and Robert Busa-Fekete. Advances in Neural Information Processing Systems (NeurIPS) 2018.
- Black-Box Reductions for Parameter-free Online Learning in Banach Spaces, Ashok Cutkosky and Francesco Orabona, Conference on Learning Theory (COLT), 2018
- Stochastic and Adversarial Online Learning Without Hyperparameters, Ashok Cutkosky and Kwabena Boahen, Advances in Neural Information Processing Systems (NIPS), 2017.
- Online Learning Without Prior Information, Ashok Cutkosky and Kwabena Boahen, Conference on Learning Theory (COLT), 2017 [Best Student Paper Award]. [code]
- Online Convex Optimization with Unconstrained Domains and Losses, Ashok
Cutkosky and Kwabena Boahen, Advances in Neural Information Processing Systems (NIPS), 2016. [magical video]
- Bloom Features, Ashok Cutkosky and Kwabena
Boahen, IEEE International Conference on
Computation Science and Computational Intelligence, 2015.
- Chromatin extrusion explains key features of loop and domain formation in wild-type and engineered genomes, Adrian Sanborn et al, Proceedings of the National Academy of Sciences, 2015.
Other