| Improving online algorithms via ML predictions M Purohit, Z Svitkina, R Kumar Advances in Neural Information Processing Systems 31, 2018 | 372* | 2018 |
| Fast influence-based coarsening for large networks M Purohit, BA Prakash, C Kang, Y Zhang, VS Subrahmanian Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014 | 84 | 2014 |
| Online learning with imperfect hints A Bhaskara, A Cutkosky, R Kumar, M Purohit International Conference on Machine Learning, 822-831, 2020 | 64 | 2020 |
| Non-clairvoyant scheduling with predictions S Im, R Kumar, MM Qaem, M Purohit ACM Transactions on Parallel Computing 10 (4), 1-26, 2023 | 60 | 2023 |
| Efficient rematerialization for deep networks R Kumar, M Purohit, Z Svitkina, E Vee, J Wang Advances in Neural Information Processing Systems 32, 2019 | 58 | 2019 |
| Online knapsack with frequency predictions S Im, R Kumar, M Montazer Qaem, M Purohit Advances in neural information processing systems 34, 2733-2743, 2021 | 51 | 2021 |
| Analyzing the optimal neighborhood: Algorithms for budgeted and partial connected dominating set problems S Khuller, M Purohit, KK Sarpatwar Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete …, 2014 | 50 | 2014 |
| On scheduling coflows S Ahmadi, S Khuller, M Purohit, S Yang Algorithmica 82 (12), 3604-3629, 2020 | 44 | 2020 |
| Semi-online bipartite matching R Kumar, M Purohit, A Schild, Z Svitkina, E Vee arXiv preprint arXiv:1812.00134, 2018 | 43 | 2018 |
| Dynamic balancing for model selection in bandits and rl A Cutkosky, C Dann, A Das, C Gentile, A Pacchiano, M Purohit International Conference on Machine Learning, 2276-2285, 2021 | 40 | 2021 |
| Brief announcement: Improved approximation algorithms for scheduling co-flows S Khuller, M Purohit Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and …, 2016 | 37 | 2016 |
| Near optimal coflow scheduling in networks M Chowdhury, S Khuller, M Purohit, S Yang, J You The 31st ACM Symposium on Parallelism in Algorithms and Architectures, 123-134, 2019 | 30 | 2019 |
| Parsimonious learning-augmented caching S Im, R Kumar, A Petety, M Purohit International Conference on Machine Learning, 9588-9601, 2022 | 29 | 2022 |
| Learning-augmented weighted paging N Bansal, C Coester, R Kumar, M Purohit, E Vee Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms …, 2022 | 25 | 2022 |
| Matroid Coflow Scheduling. S Im, B Moseley, K Pruhs, M Purohit ICALP, 2019 | 20 | 2019 |
| Approximation algorithms for connected maximum cut and related problems MT Hajiaghayi, G Kortsarz, R MacDavid, M Purohit, K Sarpatwar Algorithms-ESA 2015: 23rd Annual European Symposium, Patras, Greece …, 2015 | 20* | 2015 |
| Hiring under uncertainty M Purohit, S Gollapudi, M Raghavan International Conference on Machine Learning, 5181-5189, 2019 | 19 | 2019 |
| Online linear optimization with many hints A Bhaskara, A Cutkosky, R Kumar, M Purohit Advances in neural information processing systems 33, 9530-9539, 2020 | 18 | 2020 |
| Analyzing the optimal neighborhood: Algorithms for partial and budgeted connected dominating set problems S Khuller, M Purohit, KK Sarpatwar SIAM Journal on Discrete Mathematics 34 (1), 251-270, 2020 | 17 | 2020 |
| Logarithmic regret from sublinear hints A Bhaskara, A Cutkosky, R Kumar, M Purohit Advances in Neural Information Processing Systems 34, 28222-28232, 2021 | 16 | 2021 |
Ravi KumarGoogleVerified email at google.com
