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Aldo Pacchiano
Aldo Pacchiano
Microsoft Research
Dirección de correo verificada de microsoft.com - Página principal
Título
Citado por
Citado por
Año
Wasserstein fair classification
R Jiang, A Pacchiano, T Stepleton, H Jiang, S Chiappa
Uncertainty in Artificial Intelligence, 862-872, 2020
922020
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
632019
Effective diversity in population based reinforcement learning
J Parker-Holder, A Pacchiano, KM Choromanski, SJ Roberts
Advances in Neural Information Processing Systems 33, 18050-18062, 2020
622020
Model selection in contextual stochastic bandit problems
A Pacchiano, M Phan, Y Abbasi Yadkori, A Rao, J Zimmert, T Lattimore, ...
Advances in Neural Information Processing Systems 33, 10328-10337, 2020
522020
A General Approach to Fairness with Optimal Transport.
S Chiappa, R Jiang, T Stepleton, A Pacchiano, H Jiang, J Aslanides
AAAI, 3633-3640, 2020
33*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 32, 2019
302019
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
282020
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
262020
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
26*2020
Geometrically coupled monte carlo sampling
M Rowland, KM Choromanski, F Chalus, A Pacchiano, T Sarlos, ...
Advances in Neural Information Processing Systems 31, 2018
252018
Online model selection for reinforcement learning with function approximation
J Lee, A Pacchiano, V Muthukumar, W Kong, E Brunskill
International Conference on Artificial Intelligence and Statistics, 3340-3348, 2021
202021
Towards tractable optimism in model-based reinforcement learning
A Pacchiano, P Ball, J Parker-Holder, K Choromanski, S Roberts
Uncertainty in Artificial Intelligence, 1413-1423, 2021
19*2021
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
19*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
19*2020
Stochastic bandits with linear constraints
A Pacchiano, M Ghavamzadeh, P Bartlett, H Jiang
International Conference on Artificial Intelligence and Statistics, 2827-2835, 2021
162021
Dynamic balancing for model selection in bandits and rl
A Cutkosky, C Dann, A Das, C Gentile, A Pacchiano, M Purohit
International Conference on Machine Learning, 2276-2285, 2021
152021
Regret balancing for bandit and rl model selection
Y Abbasi-Yadkori, A Pacchiano, M Phan
arXiv preprint arXiv:2006.05491, 2020
152020
Online learning with kernel losses
N Chatterji, A Pacchiano, P Bartlett
International Conference on Machine Learning, 971-980, 2019
14*2019
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
14*2018
Ridge rider: Finding diverse solutions by following eigenvectors of the hessian
J Parker-Holder, L Metz, C Resnick, H Hu, A Lerer, A Letcher, ...
Advances in Neural Information Processing Systems 33, 753-765, 2020
132020
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Artículos 1–20