Selective classification via one-sided prediction A Gangrade, A Kag, V Saligrama International Conference on Artificial Intelligence and Statistics, 2179-2187, 2021 | 41 | 2021 |
Universal inference meets random projections: a scalable test for log-concavity R Dunn, A Gangrade, L Wasserman, A Ramdas arXiv preprint arXiv:2111.09254, 2021 | 13 | 2021 |
Piecewise linear regression via a difference of convex functions A Siahkamari, A Gangrade, B Kulis, V Saligrama International conference on machine learning, 8895-8904, 2020 | 12 | 2020 |
Efficient near-optimal testing of community changes in balanced stochastic block models A Gangrade, P Venkatesh, B Nazer, V Saligrama Advances in Neural Information Processing Systems 32, 2019 | 11* | 2019 |
A sequential test for log-concavity A Gangrade, A Rinaldo, A Ramdas arXiv preprint arXiv:2301.03542, 2023 | 9 | 2023 |
Budget learning via bracketing DAE Acar, A Gangrade, V Saligrama International Conference on Artificial Intelligence and Statistics, 4109-4119, 2020 | 9 | 2020 |
Strategies for safe multi-armed bandits with logarithmic regret and risk T Chen, A Gangrade, V Saligrama International Conference on Machine Learning, 3123-3148, 2022 | 8 | 2022 |
Doubly-Optimistic Play for Safe Linear Bandits T Chen, A Gangrade, V Saligrama arXiv preprint arXiv:2209.13694, 2022 | 7* | 2022 |
Lower bounds for two-sample structural change detection in ising and Gaussian models A Gangrade, B Nazer, V Saligrama 2017 55th Annual Allerton Conference on Communication, Control, and …, 2017 | 7 | 2017 |
Efficient edge inference by selective query A Kag, I Fedorov International Conference on Learning Representations, 2023 | 6* | 2023 |
Online selective classification with limited feedback A Gangrade, A Kag, A Cutkosky, V Saligrama Advances in Neural Information Processing Systems 34, 14529-14541, 2021 | 6 | 2021 |
Scaffolding a student to instill knowledge A Kag, DAE Acar, A Gangrade, V Saligrama The Eleventh International Conference on Learning Representations, 2022 | 4 | 2022 |
Two-Sample Testing can be as Hard as Structure Learning in Ising Models: Minimax Lower Bounds A Gangrade, B Nazer, V Saligrama 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 3 | 2018 |
Counterfactually comparing abstaining classifiers YJ Choe, A Gangrade, A Ramdas Advances in Neural Information Processing Systems 36, 2024 | 1 | 2024 |
Scaffolding a student to instill knowledge V Saligrama, A Kag, DAE Acar, A Gangrade | | 2023 |
Two studies in resource-efficient inference: structural testing of networks, and selective classification A Gangrade Boston University, 2022 | | 2022 |
Limits on testing structural changes in Ising models A Gangrade, B Nazer, V Saligrama Advances in Neural Information Processing Systems 33, 9878-9889, 2020 | | 2020 |
Online Contextual Learning with Limited Feedback SR Chowdhury, A Gangrade, A Cutkosky, V Saligrama | | |