| Wasserstein fair classification R Jiang, A Pacchiano, T Stepleton, H Jiang, S Chiappa Uncertainty in Artificial Intelligence, 862-872, 2020 | 26 | 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 | 20 | 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 | 16* | 2020 |
| From complexity to simplicity: Adaptive es-active subspaces for blackbox optimization KM Choromanski, A Pacchiano, J Parker-Holder, Y Tang, V Sindhwani Advances in Neural Information Processing Systems, 10299-10309, 2019 | 16 | 2019 |
| Geometrically coupled monte carlo sampling M Rowland, KM Choromanski, F Chalus, A Pacchiano, T Sarlós, ... Advances in Neural Information Processing Systems, 195-206, 2018 | 16 | 2018 |
| Gen-Oja: A Two-time-scale approach for Streaming CCA K Bhatia, A Pacchiano, N Flammarion, PL Bartlett, MI Jordan arXiv, arXiv: 1811.08393, 2018 | 8* | 2018 |
| 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 |
| A General Approach to Fairness with Optimal Transport. S Chiappa, R Jiang, T Stepleton, A Pacchiano, H Jiang, J Aslanides AAAI, 3633-3640, 2020 | 7 | 2020 |
| Conditions beyond treewidth for tightness of higher-order LP relaxations M Rowland, A Pacchiano, A Weller Artificial Intelligence and Statistics, 10-18, 2017 | 6 | 2017 |
| Online learning with kernel losses N Chatterji, A Pacchiano, P Bartlett International Conference on Machine Learning, 971-980, 2019 | 5* | 2019 |
| 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 | 5 | 2019 |
| 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 | 4 | 2020 |
| Ready Policy One: World Building Through Active Learning P Ball, J Parker-Holder, A Pacchiano, K Choromanski, S Roberts arXiv preprint arXiv:2002.02693, 2020 | 4 | 2020 |
| Learning to Score Behaviors for Guided Policy Optimization A Pacchiano, J Parker-Holder, Y Tang, A Choromanska, K Choromanski, ... arXiv preprint arXiv:1906.04349, 2019 | 4* | 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 | 3 | 2020 |
| Effective Diversity in Population-Based Reinforcement Learning J Parker-Holder, A Pacchiano, K Choromanski, S Roberts arXiv preprint arXiv:2002.00632, 2020 | 3 | 2020 |
| Stochastic Flows and Geometric Optimization on the Orthogonal Group K Choromanski, D Cheikhi, J Davis, V Likhosherstov, A Nazaret, ... arXiv preprint arXiv:2003.13563, 2020 | 2 | 2020 |
| On Thompson Sampling with Langevin Algorithms E Mazumdar, A Pacchiano, Y Ma, PL Bartlett, MI Jordan arXiv preprint arXiv:2002.10002, 2020 | 2 | 2020 |
| Reinforcement Learning with Wasserstein Distance Regularisation, with Applications to Multipolicy Learning MA Abdullah, A Pacchiano, M Draief arXiv preprint arXiv:1802.03976, 2018 | 2* | 2018 |
| Trace reconstruction problem A Pacchiano Massachusetts Institute of Technology, 2014 | 2 | 2014 |
Krzysztof ChoromanskiGoogle Brain Robotics New York & Columbia UniversityVerified email at columbia.edu
