Aldo Pacchiano
Title
Cited by
Cited by
Year
Wasserstein fair classification
R Jiang, A Pacchiano, T Stepleton, H Jiang, S Chiappa
Uncertainty in Artificial Intelligence, 862-872, 2020
362020
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
282019
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
192019
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
172018
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
102020
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
82019
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
72017
Effective diversity in population-based reinforcement learning
J Parker-Holder, A Pacchiano, K Choromanski, S Roberts
arXiv preprint arXiv:2002.00632, 2020
62020
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
52020
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
42020
Regret balancing for bandit and rl model selection
Y Abbasi-Yadkori, A Pacchiano, M Phan
arXiv preprint arXiv:2006.05491, 2020
42020
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
32020
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
32020
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