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Books and Theses
- 2021
[b1]Aldo Pacchiano:
Model Selection for Contextual Bandits and Reinforcement Learning. University of California, Berkeley, USA, 2021
Journal Articles
- 2024
- [j2]Jonathan Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill:
Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity. Trans. Mach. Learn. Res. 2024 (2024) - 2012
- [j1]Pavel Etingof
, Sherry Gong, Aldo Pacchiano, Qingchun Ren, Travis Schedler:
Computational Approaches to Poisson Traces Associated to Finite Subgroups of SP2n(ℂ). Exp. Math. 21(2): 141-170 (2012)
Conference and Workshop Papers
- 2024
- [c50]Christoph Dann, Claudio Gentile, Aldo Pacchiano:
Data-Driven Online Model Selection With Regret Guarantees. AISTATS 2024: 1531-1539 - [c49]Sinong Geng, Aldo Pacchiano, Andrey Kolobov, Ching-An Cheng:
Improving Offline RL by Blending Heuristics. ICLR 2024 - [c48]Dipendra Misra, Aldo Pacchiano, Robert E. Schapire:
Provable Interactive Learning with Hindsight Instruction Feedback. ICML 2024 - 2023
- [c47]Aadirupa Saha, Aldo Pacchiano, Jonathan Lee:
Dueling RL: Reinforcement Learning with Trajectory Preferences. AISTATS 2023: 6263-6289 - [c46]Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. ALT 2023: 1166-1215 - [c45]Aldo Pacchiano, Drausin Wulsin, Robert A. Barton, Luis F. Voloch:
Neural Design for Genetic Perturbation Experiments. ICLR 2023 - [c44]Andrew Wagenmaker, Aldo Pacchiano:
Leveraging Offline Data in Online Reinforcement Learning. ICML 2023: 35300-35338 - [c43]Jonathan Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill:
Supervised Pretraining Can Learn In-Context Reinforcement Learning. NeurIPS 2023 - [c42]Nataly Brukhim, Miro Dudík, Aldo Pacchiano, Robert E. Schapire:
A Unified Model and Dimension for Interactive Estimation. NeurIPS 2023 - [c41]Parnian Kassraie, Nicolas Emmenegger, Andreas Krause, Aldo Pacchiano:
Anytime Model Selection in Linear Bandits. NeurIPS 2023 - [c40]Aldo Pacchiano, Jonathan Lee, Emma Brunskill:
Experiment Planning with Function Approximation. NeurIPS 2023 - 2022
- [c39]Robert Müller, Aldo Pacchiano:
Meta Learning MDPs with linear transition models. AISTATS 2022: 5928-5948 - [c38]Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano:
Towards an Understanding of Default Policies in Multitask Policy Optimization. AISTATS 2022: 10661-10686 - [c37]Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan:
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. ICML 2022: 13441-13467 - [c36]Abhishek Gupta, Aldo Pacchiano, Yuexiang Zhai, Sham M. Kakade, Sergey Levine:
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity. NeurIPS 2022 - [c35]Aldo Pacchiano, Christoph Dann, Claudio Gentile:
Best of Both Worlds Model Selection. NeurIPS 2022 - [c34]Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip J. Ball, Oleh Rybkin, Stephen Roberts, Tim Rocktäschel, Edward Grefenstette:
Learning General World Models in a Handful of Reward-Free Deployments. NeurIPS 2022 - 2021
- [c33]Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan:
Robustness Guarantees for Mode Estimation with an Application to Bandits. AAAI 2021: 9277-9284 - [c32]Heinrich Jiang, Qijia Jiang, Aldo Pacchiano:
Learning the Truth From Only One Side of the Story. AISTATS 2021: 2413-2421 - [c31]Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang:
Stochastic Bandits with Linear Constraints. AISTATS 2021: 2827-2835 - [c30]Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill:
Online Model Selection for Reinforcement Learning with Function Approximation. AISTATS 2021: 3340-3348 - [c29]Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Claudio Gentile, Aldo Pacchiano, Manish Purohit:
Dynamic Balancing for Model Selection in Bandits and RL. ICML 2021: 2276-2285 - [c28]Dhruv Malik, Aldo Pacchiano, Vishwak Srinivasan, Yuanzhi Li:
Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity. ICML 2021: 7412-7422 - [c27]Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum:
Near Optimal Policy Optimization via REPS. NeurIPS 2021: 1100-1110 - [c26]Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Reinforcement Learning with Once-per-Episode Feedback. NeurIPS 2021: 3401-3412 - [c25]Aldo Pacchiano, Shaun Singh, Edward Chou, Alexander C. Berg, Jakob N. Foerster:
Neural Pseudo-Label Optimism for the Bank Loan Problem. NeurIPS 2021: 6580-6593 - [c24]Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan:
Tactical Optimism and Pessimism for Deep Reinforcement Learning. NeurIPS 2021: 12849-12863 - [c23]Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta:
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection. NeurIPS 2021: 16371-16383 - [c22]Aldo Pacchiano, Philip J. Ball, Jack Parker-Holder, Krzysztof Choromanski, Stephen Roberts:
Towards tractable optimism in model-based reinforcement learning. UAI 2021: 1413-1423 - 2020
- [c21]Silvia Chiappa, Ray Jiang, Tom Stepleton, Aldo Pacchiano, Heinrich Jiang, John Aslanides:
A General Approach to Fairness with Optimal Transport. AAAI 2020: 3633-3640 - [c20]Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang:
Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes. AISTATS 2020: 1363-1374 - [c19]Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan:
Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference. AISTATS 2020: 3003-3014 - [c18]Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Yunhao Tang:
ES-MAML: Simple Hessian-Free Meta Learning. ICLR 2020 - [c17]Philip J. Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen J. Roberts:
Ready Policy One: World Building Through Active Learning. ICML 2020: 591-601 - [c16]Krzysztof Choromanski, David Cheikhi, Jared Davis, Valerii Likhosherstov, Achille Nazaret, Achraf Bahamou, Xingyou Song, Mrugank Akarte, Jack Parker-Holder, Jacob Bergquist, Yuan Gao, Aldo Pacchiano, Tamás Sarlós, Adrian Weller, Vikas Sindhwani:
Stochastic Flows and Geometric Optimization on the Orthogonal Group. ICML 2020: 1918-1928 - [c15]Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
Accelerated Message Passing for Entropy-Regularized MAP Inference. ICML 2020: 5736-5746 - [c14]Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett:
On Approximate Thompson Sampling with Langevin Algorithms. ICML 2020: 6797-6807 - [c13]Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, Michael I. Jordan:
Learning to Score Behaviors for Guided Policy Optimization. ICML 2020: 7445-7454 - [c12]Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvári:
Model Selection in Contextual Stochastic Bandit Problems. NeurIPS 2020 - [c11]Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alexander Peysakhovich, Aldo Pacchiano, Jakob N. Foerster:
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian. NeurIPS 2020 - [c10]Jack Parker-Holder, Aldo Pacchiano, Krzysztof Marcin Choromanski, Stephen J. Roberts:
Effective Diversity in Population Based Reinforcement Learning. NeurIPS 2020 - 2019
- [c9]Krzysztof Choromanski, Aldo Pacchiano, Jeffrey Pennington, Yunhao Tang:
KAMA-NNs: Low-dimensional Rotation Based Neural Networks. AISTATS 2019: 236-245 - [c8]Aldo Pacchiano, Yoram Bachrach:
Computing Stable Solutions in Threshold Network Flow Games With Bounded Treewidth. AAMAS 2019: 2153-2155 - [c7]Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Deepali Jain, Yuxiang Yang, Atil Iscen, Jasmine Hsu, Vikas Sindhwani:
Provably Robust Blackbox Optimization for Reinforcement Learning. CoRL 2019: 683-696 - [c6]Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett:
Online learning with kernel losses. ICML 2019: 971-980 - [c5]Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Vikas Sindhwani:
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization. NeurIPS 2019: 10299-10309 - [c4]Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa:
Wasserstein Fair Classification. UAI 2019: 862-872 - 2018
- [c3]Mark Rowland, Krzysztof Choromanski, François Chalus, Aldo Pacchiano, Tamás Sarlós, Richard E. Turner, Adrian Weller:
Geometrically Coupled Monte Carlo Sampling. NeurIPS 2018: 195-205 - [c2]Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan:
Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation. NeurIPS 2018: 7016-7025 - 2017
- [c1]Mark Rowland, Aldo Pacchiano, Adrian Weller:
Conditions beyond treewidth for tightness of higher-order LP relaxations. AISTATS 2017: 10-18
Editorship
- 2024
- [e1]Reneta P. Barneva, Valentin E. Brimkov, Claudio Gentile, Aldo Pacchiano:
Artificial Intelligence and Image Analysis - 18th International Symposium on Artificial Intelligence and Mathematics, ISAIM 2024, and 22nd International Workshop on Combinatorial Image Analysis, IWCIA 2024, Fort Lauderdale, FL, USA, January 8-10, 2024, Revised Selected Papers. Lecture Notes in Computer Science 14494, Springer 2024, ISBN 978-3-031-63734-6 [contents]
Informal and Other Publications
- 2024
- [i64]Aldo Pacchiano, Jonathan N. Lee, Emma Brunskill:
Experiment Planning with Function Approximation. CoRR abs/2401.05193 (2024) - [i63]Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett:
Contextual Bandits with Stage-wise Constraints. CoRR abs/2401.08016 (2024) - [i62]Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari:
A Framework for Partially Observed Reward-States in RLHF. CoRR abs/2402.03282 (2024) - [i61]Nirjhar Das, Souradip Chakraborty, Aldo Pacchiano, Sayak Ray Chowdhury:
Provably Sample Efficient RLHF via Active Preference Optimization. CoRR abs/2402.10500 (2024) - [i60]Yilei Chen, Aldo Pacchiano, Ioannis Ch. Paschalidis:
Multiple-policy Evaluation via Density Estimation. CoRR abs/2404.00195 (2024) - [i59]Dipendra Misra, Aldo Pacchiano, Robert E. Schapire:
Provable Interactive Learning with Hindsight Instruction Feedback. CoRR abs/2404.09123 (2024) - [i58]Aida Afshar, Aldo Pacchiano:
Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non-Stationary Objectives. CoRR abs/2408.04046 (2024) - [i57]Aldo Pacchiano:
Second Order Bounds for Contextual Bandits with Function Approximation. CoRR abs/2409.16197 (2024) - [i56]Mingyu Chen, Aldo Pacchiano, Xuezhou Zhang:
State-free Reinforcement Learning. CoRR abs/2409.18439 (2024) - 2023
- [i55]Jonathan N. Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill:
Estimating Optimal Policy Value in General Linear Contextual Bandits. CoRR abs/2302.09451 (2023) - [i54]Sinong Geng, Aldo Pacchiano, Andrey Kolobov, Ching-An Cheng:
Improving Offline RL by Blending Heuristics. CoRR abs/2306.00321 (2023) - [i53]Aldo Pacchiano, Christoph Dann, Claudio Gentile:
Data-Driven Regret Balancing for Online Model Selection in Bandits. CoRR abs/2306.02869 (2023) - [i52]Nataly Brukhim, Miroslav Dudík, Aldo Pacchiano, Robert E. Schapire:
A Unified Model and Dimension for Interactive Estimation. CoRR abs/2306.06184 (2023) - [i51]Jonathan N. Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill:
Supervised Pretraining Can Learn In-Context Reinforcement Learning. CoRR abs/2306.14892 (2023) - [i50]Parnian Kassraie, Aldo Pacchiano, Nicolas Emmenegger, Andreas Krause:
Anytime Model Selection in Linear Bandits. CoRR abs/2307.12897 (2023) - [i49]Elena Gal, Shaun Singh, Aldo Pacchiano, Ben Walker, Terry J. Lyons, Jakob N. Foerster:
Unbiased Decisions Reduce Regret: Adversarial Domain Adaptation for the Bank Loan Problem. CoRR abs/2308.08051 (2023) - 2022
- [i48]Robert Müller, Aldo Pacchiano:
Meta Learning MDPs with Linear Transition Models. CoRR abs/2201.08732 (2022) - [i47]Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan:
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. CoRR abs/2205.07217 (2022) - [i46]Aldo Pacchiano, Ofir Nachum, Nilesh Tripuraneni, Peter L. Bartlett:
Joint Representation Training in Sequential Tasks with Shared Structure. CoRR abs/2206.12441 (2022) - [i45]Aldo Pacchiano, Christoph Dann, Claudio Gentile:
Best of Both Worlds Model Selection. CoRR abs/2206.14912 (2022) - [i44]Aldo Pacchiano, Drausin Wulsin, Robert A. Barton, Luis F. Voloch:
Neural Design for Genetic Perturbation Experiments. CoRR abs/2207.12805 (2022) - [i43]Abhishek Gupta, Aldo Pacchiano, Yuexiang Zhai, Sham M. Kakade, Sergey Levine:
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity. CoRR abs/2210.09579 (2022) - [i42]Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip J. Ball, Oleh Rybkin, Stephen J. Roberts, Tim Rocktäschel, Edward Grefenstette:
Learning General World Models in a Handful of Reward-Free Deployments. CoRR abs/2210.12719 (2022) - [i41]Andrew Wagenmaker, Aldo Pacchiano:
Leveraging Offline Data in Online Reinforcement Learning. CoRR abs/2211.04974 (2022) - [i40]Abhi Gupta, Ted Moskovitz, David Alvarez-Melis, Aldo Pacchiano:
Transfer RL via the Undo Maps Formalism. CoRR abs/2211.14469 (2022) - 2021
- [i39]Silvia Chiappa, Aldo Pacchiano:
Fairness with Continuous Optimal Transport. CoRR abs/2101.02084 (2021) - [i38]Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Daiyi Peng, Deepali Jain, Wenbo Gao, Aldo Pacchiano, Tamás Sarlós, Yuxiang Yang:
ES-ENAS: Combining Evolution Strategies with Neural Architecture Search at No Extra Cost for Reinforcement Learning. CoRR abs/2101.07415 (2021) - [i37]Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel:
Deep Reinforcement Learning with Dynamic Optimism. CoRR abs/2102.03765 (2021) - [i36]Krzysztof Choromanski, Deepali Jain, Jack Parker-Holder, Xingyou Song, Valerii Likhosherstov, Anirban Santara, Aldo Pacchiano, Yunhao Tang, Adrian Weller:
Unlocking Pixels for Reinforcement Learning via Implicit Attention. CoRR abs/2102.04353 (2021) - [i35]Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum:
Near Optimal Policy Optimization via REPS. CoRR abs/2103.09756 (2021) - [i34]Jeffrey Chan, Aldo Pacchiano, Nilesh Tripuraneni, Yun S. Song, Peter L. Bartlett, Michael I. Jordan:
Parallelizing Contextual Linear Bandits. CoRR abs/2105.10590 (2021) - [i33]Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
On the Theory of Reinforcement Learning with Once-per-Episode Feedback. CoRR abs/2105.14363 (2021) - [i32]Dhruv Malik, Aldo Pacchiano, Vishwak Srinivasan, Yuanzhi Li:
Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity. CoRR abs/2106.07814 (2021) - [i31]Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta:
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection. CoRR abs/2110.14798 (2021) - [i30]Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano:
Towards an Understanding of Default Policies in Multitask Policy Optimization. CoRR abs/2111.02994 (2021) - [i29]Aldo Pacchiano, Aadirupa Saha, Jonathan Lee:
Dueling RL: Reinforcement Learning with Trajectory Preferences. CoRR abs/2111.04850 (2021) - [i28]Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. CoRR abs/2111.04873 (2021) - [i27]Aldo Pacchiano, Shaun Singh, Edward Chou, Alexander C. Berg, Jakob N. Foerster:
Neural Pseudo-Label Optimism for the Bank Loan Problem. CoRR abs/2112.02185 (2021) - 2020
- [i26]Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts:
Effective Diversity in Population-Based Reinforcement Learning. CoRR abs/2002.00632 (2020) - [i25]Philip J. Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts:
Ready Policy One: World Building Through Active Learning. CoRR abs/2002.02693 (2020) - [i24]Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan:
On Thompson Sampling with Langevin Algorithms. CoRR abs/2002.10002 (2020) - [i23]Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvári:
Model Selection in Contextual Stochastic Bandit Problems. CoRR abs/2003.01704 (2020) - [i22]Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan:
Robustness Guarantees for Mode Estimation with an Application to Bandits. CoRR abs/2003.02932 (2020) - [i21]Krzysztof Choromanski, David Cheikhi, Jared Davis, Valerii Likhosherstov, Achille Nazaret, Achraf Bahamou, Xingyou Song, Mrugank Akarte, Jack Parker-Holder, Jacob Bergquist, Yuan Gao, Aldo Pacchiano, Tamás Sarlós, Adrian Weller, Vikas Sindhwani:
Stochastic Flows and Geometric Optimization on the Orthogonal Group. CoRR abs/2003.13563 (2020) - [i20]Heinrich Jiang, Qijia Jiang, Aldo Pacchiano:
Learning the Truth From Only One Side of the Story. CoRR abs/2006.04858 (2020) - [i19]Yasin Abbasi-Yadkori, Aldo Pacchiano, My Phan:
Regret Balancing for Bandit and RL Model Selection. CoRR abs/2006.05491 (2020) - [i18]Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang:
Stochastic Bandits with Linear Constraints. CoRR abs/2006.10185 (2020) - [i17]Aldo Pacchiano, Philip J. Ball, Jack Parker-Holder, Krzysztof Choromanski, Stephen Roberts:
On Optimism in Model-Based Reinforcement Learning. CoRR abs/2006.11911 (2020) - [i16]Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan:
Accelerated Message Passing for Entropy-Regularized MAP Inference. CoRR abs/2007.00699 (2020) - [i15]Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alex Peysakhovich, Aldo Pacchiano, Jakob N. Foerster:
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian. CoRR abs/2011.06505 (2020) - [i14]Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill:
Online Model Selection for Reinforcement Learning with Function Approximation. CoRR abs/2011.09750 (2020) - [i13]Aldo Pacchiano, Christoph Dann, Claudio Gentile, Peter L. Bartlett:
Regret Bound Balancing and Elimination for Model Selection in Bandits and RL. CoRR abs/2012.13045 (2020) - 2019
- [i12]Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Jasmine Hsu, Atil Iscen, Deepali Jain, Vikas Sindhwani:
When random search is not enough: Sample-Efficient and Noise-Robust Blackbox Optimization of RL Policies. CoRR abs/1903.02993 (2019) - [i11]Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang:
Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces. CoRR abs/1903.04268 (2019) - [i10]Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang:
Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes. CoRR abs/1905.12667 (2019) - [i9]Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Anna Choromanska, Krzysztof Choromanski, Michael I. Jordan:
Wasserstein Reinforcement Learning. CoRR abs/1906.04349 (2019) - [i8]Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan:
Approximate Sherali-Adams Relaxations for MAP Inference via Entropy Regularization. CoRR abs/1907.01127 (2019) - [i7]Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Wenbo Gao, Aldo Pacchiano, Tamás Sarlós, Deepali Jain, Yuxiang Yang:
Reinforcement Learning with Chromatic Networks. CoRR abs/1907.06511 (2019) - [i6]Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa:
Wasserstein Fair Classification. CoRR abs/1907.12059 (2019) - [i5]Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Yunhao Tang:
ES-MAML: Simple Hessian-Free Meta Learning. CoRR abs/1910.01215 (2019) - 2018
- [i4]Mohammed Amin Abdullah, Aldo Pacchiano, Moez Draief:
A note on reinforcement learning with Wasserstein distance regularisation, with applications to multipolicy learning. CoRR abs/1802.03976 (2018) - [i3]Aldo Pacchiano, Niladri S. Chatterji, Peter L. Bartlett:
Online learning with kernel losses. CoRR abs/1802.09732 (2018) - [i2]Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan:
Gen-Oja: A Simple and Efficient Algorithm for Streaming Generalized Eigenvector Computation. CoRR abs/1811.08393 (2018) - 2015
- [i1]Aldo Pacchiano, Oliver Williams:
Real time clustering of time series using triangular potentials. CoRR abs/1502.05090 (2015)
Coauthor Index
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