| Fairalm: Augmented lagrangian method for training fair models with little regret VS Lokhande, AK Akash, SN Ravi, V Singh European Conference on Computer Vision, 365-381, 2020 | 31 | 2020 |
| Local policies for efficiently patrolling a triangulated region by a robot swarm D Maftuleac, SK Lee, SP Fekete, AK Akash, A López-Ortiz, J McLurkin 2015 IEEE International Conference on Robotics and Automation (ICRA), 1809-1815, 2015 | 11 | 2015 |
| Wasserstein barycenter-based model fusion and linear mode connectivity of neural networks AK Akash, S Li, NG Trillos arXiv preprint arXiv:2210.06671, 2022 | 10 | 2022 |
| Stochastic bandits with delayed composite anonymous feedback S Garg, AK Akash arXiv preprint arXiv:1910.01161, 2019 | 9 | 2019 |
| Learning invariant representations using inverse contrastive loss AK Akash, VS Lokhande, SN Ravi, V Singh Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 6582-6591, 2021 | 7 | 2021 |
| Lower bounds for graph exploration using local policies AK Akash, SP Fekete, SK Lee, A López-Ortiz, D Maftuleac, J McLurkin WALCOM: Algorithms and Computation: 10th International Workshop, WALCOM 2016 …, 2016 | 1 | 2016 |
| FairALM: Augmented Lagrangian Method for Training Fair Models with Little Regret SUPPLEMENTARYMATERIAL VS Lokhande, AK Akash, SN Ravi, V Singh | | |
