no code implementations • 12 Oct 2015 • Hal Daumé III, Samir Khuller, Manish Purohit, Gregory Sanders
Algorithm designers typically assume that the input data is correct, and then proceed to find "optimal" or "sub-optimal" solutions using this input data.
no code implementations • NeurIPS 2018 • Manish Purohit, Zoya Svitkina, Ravi Kumar
In this work we study the problem of using machine-learned predictions to improve performance of online algorithms.
no code implementations • NeurIPS 2019 • Ravi Kumar, Manish Purohit, Zoya Svitkina, Erik Vee, Joshua Wang
When training complex neural networks, memory usage can be an important bottleneck.
no code implementations • ICML 2020 • Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
We consider a variant of the classical online linear optimization problem in which at every step, the online player receives a "hint" vector before choosing the action for that round.
no code implementations • NeurIPS 2020 • Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
We study an online linear optimization (OLO) problem in which the learner is provided access to $K$ "hint" vectors in each round prior to making a decision.
no code implementations • 30 Oct 2020 • Priyanka Shende, Manish Purohit
In this paper, we are motivated by the following question: Is there a strategy-proof and envy-free random assignment mechanism more efficient than equal division?
no code implementations • 24 Dec 2020 • Ashok Cutkosky, Abhimanyu Das, Manish Purohit
We provide a simple method to combine stochastic bandit algorithms.
no code implementations • NeurIPS 2021 • Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
We consider the online linear optimization problem, where at every step the algorithm plays a point $x_t$ in the unit ball, and suffers loss $\langle c_t, x_t\rangle$ for some cost vector $c_t$ that is then revealed to the algorithm.
no code implementations • NeurIPS 2021 • Sungjin Im, Ravi Kumar, Mahshid Montazer Qaem, Manish Purohit
There has been recent interest in using machine-learned predictions to improve the worst-case guarantees of online algorithms.
no code implementations • 9 Feb 2022 • Sungjin Im, Ravi Kumar, Aditya Petety, Manish Purohit
Learning-augmented algorithms -- in which, traditional algorithms are augmented with machine-learned predictions -- have emerged as a framework to go beyond worst-case analysis.
no code implementations • 11 May 2023 • Pengming Wang, Mikita Sazanovich, Berkin Ilbeyi, Phitchaya Mangpo Phothilimthana, Manish Purohit, Han Yang Tay, Ngân Vũ, Miaosen Wang, Cosmin Paduraru, Edouard Leurent, Anton Zhernov, Po-Sen Huang, Julian Schrittwieser, Thomas Hubert, Robert Tung, Paula Kurylowicz, Kieran Milan, Oriol Vinyals, Daniel J. Mankowitz
We also introduce a Reinforcement Learning agent, mallocMuZero, and show that it is capable of playing this game to discover new and improved memory mapping solutions that lead to faster execution times on real ML workloads on ML accelerators.