Firewall placement in cloud data centers. - Seungjoon Lee; Manish Purohit; Barna Saha (SoCC, 2013)

Improved algorithms and analysis for the laminar matroid secretary problem - David G. Harris 001; Manish Purohit (CoRR, 2013)

Betweenness computation in the single graph representation of hypergraphs. - Rami Puzis; Manish Purohit; V. S. Subrahmanian (Soc. Networks, 2013)

Fast influence-based coarsening for large networks. - Manish Purohit; B. Aditya Prakash; Chanhyun Kang; Yao Zhang 003; V. S. Subrahmanian (KDD, 2014)

Analyzing the Optimal Neighborhood: Algorithms for Budgeted and Partial Connected Dominating Set Problems. - Samir Khuller; Manish Purohit; Kanthi K. Sarpatwar (SODA, 2014)

On Correcting Inputs: Inverse Optimization for Online Structured Prediction. - Hal Daumé III; Samir Khuller; Manish Purohit; Gregory Sanders (FSTTCS, 2015)

Brief Announcement: Improved Approximation Algorithms for Scheduling Co-Flows. - Samir Khuller; Manish Purohit (SPAA, 2016)

Data-Aware Scheduling in Datacenters. - Manish Purohit (2016)

On Scheduling Coflows - (Extended Abstract). - Saba Ahmadi; Samir Khuller; Manish Purohit; Sheng Yang 005 (IPCO, 2017)

On the Approximability of Digraph Ordering. - Sreyash Kenkre; Vinayaka Pandit; Manish Purohit; Rishi Saket (Algorithmica, 2017)

A Tight Approximation for Co-flow Scheduling for Minimizing Total Weighted Completion Time. - Sungjin Im; Manish Purohit (CoRR, 2017)

Improving Online Algorithms via ML Predictions. - Manish Purohit; Zoya Svitkina; Ravi Kumar 001 (NeurIPS, 2018)

On maximum leaf trees and connections to connected maximum cut problems. - Rajiv Gandhi; Mohammad Taghi Hajiaghayi; Guy Kortsarz; Manish Purohit; Kanthi K. Sarpatwar (Inf. Process. Lett., 2018)

Matroid Coflow Scheduling. - Sungjin Im; Benjamin Moseley; Kirk Pruhs; Manish Purohit (ICALP, 2019)

Hiring Under Uncertainty. - Manish Purohit; Sreenivas Gollapudi; Manish Raghavan (ICML, 2019)

Semi-Online Bipartite Matching. - Ravi Kumar 001; Manish Purohit; Aaron Schild; Zoya Svitkina; Erik Vee (ITCS, 2019)

Efficient Rematerialization for Deep Networks. - Ravi Kumar 001; Manish Purohit; Zoya Svitkina; Erik Vee; Joshua R. Wang (NeurIPS, 2019)

Near Optimal Coflow Scheduling in Networks. - Mosharaf Chowdhury; Samir Khuller; Manish Purohit; Sheng Yang 005; Jie You (SPAA, 2019)

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

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

Interleaved Caching with Access Graphs. - Ravi Kumar 001; Manish Purohit; Zoya Svitkina; Erik Vee (SODA, 2020)

On Scheduling Coflows. - Saba Ahmadi; Samir Khuller; Manish Purohit; Sheng Yang 005 (Algorithmica, 2020)

Scale-Free Allocation, Amortized Convexity, and Myopic Weighted Paging. - Nikhil Bansal 001; Christian Coester; Ravi Kumar 001; Manish Purohit; Erik Vee (CoRR, 2020)

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

Analyzing the Optimal Neighborhood: Algorithms for Partial and Budgeted Connected Dominating Set Problems. - Samir Khuller; Manish Purohit; Kanthi K. Sarpatwar (SIAM J. Discret. Math., 2020)

Approximation algorithms for connected maximum cut and related problems. - MohammadTaghi Hajiaghayi; Guy Kortsarz; Robert MacDavid; Manish Purohit; Kanthi K. Sarpatwar (Theor. Comput. Sci., 2020)

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

Revenue Maximization in Transportation Networks. - Kshipra Bhawalkar; Kostas Kollias; Manish Purohit (APPROX-RANDOM, 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)

Online Knapsack with Frequency Predictions. - Sungjin Im; Ravi Kumar 001; Mahshid Montazer Qaem; Manish Purohit (NeurIPS, 2021)

NP-completeness of the Active Time Scheduling Problem. - Sagnik Saha; Manish Purohit (CoRR, 2021)

Caching with Reserves. - Sharat Ibrahimpur; Manish Purohit; Zoya Svitkina; Erik Vee; Joshua R. Wang (APPROX/RANDOM, 2022)

Parsimonious Learning-Augmented Caching. - Sungjin Im; Ravi Kumar 001; Aditya Petety; Manish Purohit (ICML, 2022)

Learning-Augmented Weighted Paging. - Nikhil Bansal 001; Christian Coester; Ravi Kumar 001; Manish Purohit; Erik Vee (SODA, 2022)

Scheduling with Communication Delay in Near-Linear Time. - Quanquan C. Liu; Manish Purohit; Zoya Svitkina; Erik Vee; Joshua R. Wang (STACS, 2022)

Efficient Caching with Reserves via Marking. - Sharat Ibrahimpur; Manish Purohit; Zoya Svitkina; Erik Vee; Joshua R. Wang (ICALP, 2023)

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

Optimizing Memory Mapping Using Deep Reinforcement Learning. - Pengming Wang 001; Mikita Sazanovich; Berkin Ilbeyi; Phitchaya Mangpo Phothilimthana; Manish Purohit; Han Yang Tay; Ngân Vu; Miaosen Wang; Cosmin Paduraru; Edouard Leurent; Anton Zhernov; Julian Schrittwieser; et al. (CoRR, 2023)

New Tools for Peak Memory Scheduling. - Ce Jin 001; Manish Purohit; Zoya Svitkina; Erik Vee; Joshua R. Wang (CoRR, 2023)

Strategy-proof and envy-free mechanisms for house allocation. - Priyanka Shende; Manish Purohit (J. Econ. Theory, 2023)

Non-clairvoyant Scheduling with Predictions. - Sungjin Im; Ravi Kumar 001; Mahshid Montazer Qaem; Manish Purohit (ACM Trans. Parallel Comput., 2023)

Online Load and Graph Balancing for Random Order Inputs. - Sungjin Im; Ravi Kumar 001; Shi Li 001; Aditya Petety; Manish Purohit (SPAA, 2024)

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

Non-preemptive Throughput Maximization under Time-varying Capacity. - Aniket Murhekar; Manish Purohit; Zoya Svitkina; Erik Vee; Joshua R. Wang (SPAA, 2025)