Aldo Pacchiano
Cited by
Cited by
Wasserstein fair classification
R Jiang, A Pacchiano, T Stepleton, H Jiang, S Chiappa
Uncertainty in Artificial Intelligence, 862-872, 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
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
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
Geometrically coupled monte carlo sampling
M Rowland, KM Choromanski, F Chalus, A Pacchiano, T Sarlós, ...
Advances in Neural Information Processing Systems, 195-206, 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
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
A General Approach to Fairness with Optimal Transport.
S Chiappa, R Jiang, T Stepleton, A Pacchiano, H Jiang, J Aslanides
AAAI, 3633-3640, 2020
Conditions beyond treewidth for tightness of higher-order LP relaxations
M Rowland, A Pacchiano, A Weller
Artificial Intelligence and Statistics, 10-18, 2017
Online learning with kernel losses
N Chatterji, A Pacchiano, P Bartlett
International Conference on Machine Learning, 971-980, 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
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
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
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
On optimism in model-based reinforcement learning
A Pacchiano, P Ball, J Parker-Holder, K Choromanski, S Roberts
arXiv preprint arXiv:2006.11911, 2020
Effective Diversity in Population-Based Reinforcement Learning
J Parker-Holder, A Pacchiano, K Choromanski, S Roberts
arXiv preprint arXiv:2002.00632, 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
On Thompson Sampling with Langevin Algorithms
E Mazumdar, A Pacchiano, Y Ma, PL Bartlett, MI Jordan
arXiv preprint arXiv:2002.10002, 2020
Reinforcement Learning with Wasserstein Distance Regularisation, with Applications to Multipolicy Learning
MA Abdullah, A Pacchiano, M Draief
arXiv preprint arXiv:1802.03976, 2018
Trace reconstruction problem
A Pacchiano
Massachusetts Institute of Technology, 2014
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