Search Results for author: Manish Purohit

Found 10 papers, 0 papers with code

Parsimonious Learning-Augmented Caching

no code implementations9 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.

Online Knapsack with Frequency Predictions

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.

Logarithmic Regret from Sublinear Hints

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.

online learning

Strategy-proof and Envy-free Mechanisms for House Allocation

no code implementations30 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?

Fairness

Online Linear Optimization with Many Hints

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.

Online Learning with Imperfect Hints

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.

online learning

Improving Online Algorithms via ML Predictions

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.

On Correcting Inputs: Inverse Optimization for Online Structured Prediction

no code implementations12 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.

online learning Structured Prediction

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