| Wasserstein fair classification R Jiang, A Pacchiano, T Stepleton, H Jiang, S Chiappa Uncertainty in Artificial Intelligence, 862-872, 2020 | 36 | 2020 |
| Es-maml: Simple hessian-free meta learning X Song, W Gao, Y Yang, K Choromanski, A Pacchiano, Y Tang arXiv preprint arXiv:1910.01215, 2019 | 28 | 2019 |
| From complexity to simplicity: Adaptive es-active subspaces for blackbox optimization K Choromanski, A Pacchiano, J Parker-Holder, Y Tang arXiv preprint arXiv:1903.04268, 2019 | 19 | 2019 |
| Provably robust blackbox optimization for reinforcement learning K Choromanski, A Pacchiano, J Parker-Holder, Y Tang, D Jain, Y Yang, ... Conference on Robot Learning, 683-696, 2020 | 18* | 2020 |
| Geometrically coupled monte carlo sampling M Rowland, K Choromanski, F Chalus, A Pacchiano, T Sarlos, RE Turner, ... Proceedings of the 32nd International Conference on Neural Information …, 2018 | 17 | 2018 |
| Model selection in contextual stochastic bandit problems A Pacchiano, M Phan, Y Abbasi-Yadkori, A Rao, J Zimmert, T Lattimore, ... arXiv preprint arXiv:2003.01704, 2020 | 10 | 2020 |
| Gen-Oja: A Two-time-scale approach for Streaming CCA K Bhatia, A Pacchiano, N Flammarion, PL Bartlett, MI Jordan arXiv preprint arXiv:1811.08393, 2018 | 9* | 2018 |
| KAMA-NNs: Low-dimensional rotation based neural networks K Choromanski, A Pacchiano, J Pennington, Y Tang The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 8 | 2019 |
| Computational approaches to Poisson traces associated to finite subgroups of P Etingof, S Gong, A Pacchiano, Q Ren, T Schedler Experimental Mathematics 21 (2), 141-170, 2012 | 8* | 2012 |
| Conditions beyond treewidth for tightness of higher-order LP relaxations M Rowland, A Pacchiano, A Weller Artificial Intelligence and Statistics, 10-18, 2017 | 7 | 2017 |
| Effective diversity in population-based reinforcement learning J Parker-Holder, A Pacchiano, K Choromanski, S Roberts arXiv preprint arXiv:2002.00632, 2020 | 6 | 2020 |
| Learning to score behaviors for guided policy optimization A Pacchiano, J Parker-Holder, Y Tang, K Choromanski, A Choromanska, ... International Conference on Machine Learning, 7445-7454, 2020 | 5* | 2020 |
| Ready policy one: World building through active learning P Ball, J Parker-Holder, A Pacchiano, K Choromanski, S Roberts International Conference on Machine Learning, 591-601, 2020 | 5 | 2020 |
| Online learning with kernel losses N Chatterji, A Pacchiano, P Bartlett International Conference on Machine Learning, 971-980, 2019 | 5* | 2019 |
| On optimism in model-based reinforcement learning A Pacchiano, P Ball, J Parker-Holder, K Choromanski, S Roberts arXiv preprint arXiv:2006.11911, 2020 | 4 | 2020 |
| Regret balancing for bandit and rl model selection Y Abbasi-Yadkori, A Pacchiano, M Phan arXiv preprint arXiv:2006.05491, 2020 | 4 | 2020 |
| Regret Bound Balancing and Elimination for Model Selection in Bandits and RL A Pacchiano, C Dann, C Gentile, P Bartlett arXiv preprint arXiv:2012.13045, 2020 | 3 | 2020 |
| On Approximate Thompson Sampling with Langevin Algorithms E Mazumdar, A Pacchiano, Y Ma, M Jordan, P Bartlett International Conference on Machine Learning, 6797-6807, 2020 | 3* | 2020 |
| Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes K Choromanski, A Pacchiano, J Parker-Holder, Y Tang International Conference on Artificial Intelligence and Statistics, 1363-1374, 2020 | 3* | 2020 |
| A general approach to fairness with optimal transport C Silvia, J Ray, S Tom, P Aldo, J Heinrich, A John Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3633-3640, 2020 | 3 | 2020 |
Krzysztof ChoromanskiGoogle Brain Robotics New York & Columbia UniversityVerified email at columbia.edu
