Aldo Pacchiano

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 106

Top Venues

CoRR

NeurIPS

AISTATS

ICML

Publications

Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari
A Framework for Partially Observed Reward-States in RLHF. CoRR (2024)
Nirjhar Das, Souradip Chakraborty, Aldo Pacchiano, Sayak Ray Chowdhury
Provably Sample Efficient RLHF via Active Preference Optimization. CoRR (2024)
Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett
Contextual Bandits with Stage-wise Constraints. CoRR (2024)
Aldo Pacchiano, Jonathan N. Lee, Emma Brunskill
Experiment Planning with Function Approximation. CoRR (2024)
Sinong Geng, Aldo Pacchiano, Andrey Kolobov, Ching-An Cheng
Improving Offline RL by Blending Heuristics. CoRR (2023)
Aldo Pacchiano, Jonathan Lee, Emma Brunskill
Experiment Planning with Function Approximation. NeurIPS (2023)
Elena Gal, Shaun Singh, Aldo Pacchiano, Ben Walker, Terry Lyons, Jakob N. Foerster
Unbiased Decisions Reduce Regret: Adversarial Domain Adaptation for the Bank Loan Problem. CoRR (2023)
Parnian Kassraie, Nicolas Emmenegger, Andreas Krause, Aldo Pacchiano
Anytime Model Selection in Linear Bandits. NeurIPS (2023)
Nataly Brukhim, Miro Dudík, Aldo Pacchiano, Robert E. Schapire
A Unified Model and Dimension for Interactive Estimation. NeurIPS (2023)
Parnian Kassraie, Aldo Pacchiano, Nicolas Emmenegger, Andreas Krause
Anytime Model Selection in Linear Bandits. CoRR (2023)
Nataly Brukhim, Miroslav Dudík, Aldo Pacchiano, Robert E. Schapire
A Unified Model and Dimension for Interactive Estimation. CoRR (2023)
Aadirupa Saha, Aldo Pacchiano, Jonathan Lee
Dueling RL: Reinforcement Learning with Trajectory Preferences. AISTATS (2023)
Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. ALT (2023)
Aldo Pacchiano, Christoph Dann, Claudio Gentile
Data-Driven Regret Balancing for Online Model Selection in Bandits. CoRR (2023)
Jonathan N. Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill
Estimating Optimal Policy Value in General Linear Contextual Bandits. CoRR (2023)
Andrew Wagenmaker, Aldo Pacchiano
Leveraging Offline Data in Online Reinforcement Learning. ICML (2023)
Aldo Pacchiano, Drausin Wulsin, Robert A. Barton, Luis F. Voloch
Neural Design for Genetic Perturbation Experiments. ICLR (2023)
Jonathan Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill
Supervised Pretraining Can Learn In-Context Reinforcement Learning. NeurIPS (2023)
Jonathan N. Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill
Supervised Pretraining Can Learn In-Context Reinforcement Learning. CoRR (2023)
Robert Müller, Aldo Pacchiano
Meta Learning MDPs with Linear Transition Models. CoRR (2022)
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)
Abhi Gupta, Ted Moskovitz, David Alvarez-Melis, Aldo Pacchiano
Transfer RL via the Undo Maps Formalism. CoRR (2022)
Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. ICML (2022)
Robert Müller, Aldo Pacchiano
Meta Learning MDPs with linear transition models. AISTATS (2022)
Aldo Pacchiano, Christoph Dann, Claudio Gentile
Best of Both Worlds Model Selection. NeurIPS (2022)
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)
Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. CoRR (2022)
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 (2022)
Andrew Wagenmaker, Aldo Pacchiano
Leveraging Offline Data in Online Reinforcement Learning. CoRR (2022)
Aldo Pacchiano, Christoph Dann, Claudio Gentile
Best of Both Worlds Model Selection. CoRR (2022)
Aldo Pacchiano, Ofir Nachum, Nilesh Tripuraneni, Peter L. Bartlett
Joint Representation Training in Sequential Tasks with Shared Structure. CoRR (2022)
Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano
Towards an Understanding of Default Policies in Multitask Policy Optimization. AISTATS (2022)
Abhishek Gupta, Aldo Pacchiano, Yuexiang Zhai, Sham M. Kakade, Sergey Levine
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity. CoRR (2022)
Aldo Pacchiano, Drausin Wulsin, Robert A. Barton, Luis F. Voloch
Neural Design for Genetic Perturbation Experiments. CoRR (2022)
Dhruv Malik, Aldo Pacchiano, Vishwak Srinivasan, Yuanzhi Li
Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity. ICML (2021)
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 (2021)
Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum
Near Optimal Policy Optimization via REPS. NeurIPS (2021)
Aldo Pacchiano, Shaun Singh, Edward Chou, Alexander C. Berg, Jakob N. Foerster
Neural Pseudo-Label Optimism for the Bank Loan Problem. CoRR (2021)
Aldo Pacchiano, Aadirupa Saha, Jonathan Lee
Dueling RL: Reinforcement Learning with Trajectory Preferences. CoRR (2021)
Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection. NeurIPS (2021)
Jeffrey Chan, Aldo Pacchiano, Nilesh Tripuraneni, Yun S. Song, Peter L. Bartlett, Michael I. Jordan
Parallelizing Contextual Linear Bandits. CoRR (2021)
Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang
Stochastic Bandits with Linear Constraints. AISTATS (2021)
Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano
Towards an Understanding of Default Policies in Multitask Policy Optimization. CoRR (2021)
Dhruv Malik, Aldo Pacchiano, Vishwak Srinivasan, Yuanzhi Li
Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity. CoRR (2021)
Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
On the Theory of Reinforcement Learning with Once-per-Episode Feedback. CoRR (2021)
Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan
Robustness Guarantees for Mode Estimation with an Application to Bandits. AAAI (2021)
Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan
Tactical Optimism and Pessimism for Deep Reinforcement Learning. NeurIPS (2021)
Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. CoRR (2021)
Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Claudio Gentile, Aldo Pacchiano, Manish Purohit
Dynamic Balancing for Model Selection in Bandits and RL. ICML (2021)
Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection. CoRR (2021)
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 (2021)
Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum
Near Optimal Policy Optimization via REPS. CoRR (2021)
Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill
Online Model Selection for Reinforcement Learning with Function Approximation. AISTATS (2021)
Aldo Pacchiano, Philip Ball, Jack Parker-Holder, Krzysztof Choromanski, Stephen Roberts
Towards tractable optimism in model-based reinforcement learning. UAI (2021)
Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel
Deep Reinforcement Learning with Dynamic Optimism. CoRR (2021)
Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
On the Theory of Reinforcement Learning with Once-per-Episode Feedback. NeurIPS (2021)
Heinrich Jiang, Qijia Jiang, Aldo Pacchiano
Learning the Truth From Only One Side of the Story. AISTATS (2021)
Silvia Chiappa, Aldo Pacchiano
Fairness with Continuous Optimal Transport. CoRR (2021)
Aldo Pacchiano, Shaun Singh, Edward Chou, Alexander C. Berg, Jakob N. Foerster
Neural Pseudo-Label Optimism for the Bank Loan Problem. NeurIPS (2021)
Heinrich Jiang, Qijia Jiang, Aldo Pacchiano
Learning the Truth From Only One Side of the Story. CoRR (2020)
Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
Accelerated Message Passing for Entropy-Regularized MAP Inference. CoRR (2020)
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes. AISTATS (2020)
Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Yunhao Tang
ES-MAML: Simple Hessian-Free Meta Learning. ICLR (2020)
Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvári
Model Selection in Contextual Stochastic Bandit Problems. CoRR (2020)
Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan
On Thompson Sampling with Langevin Algorithms. CoRR (2020)
Aldo Pacchiano, Philip J. Ball, Jack Parker-Holder, Krzysztof Choromanski, Stephen Roberts
On Optimism in Model-Based Reinforcement Learning. CoRR (2020)
Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang
Stochastic Bandits with Linear Constraints. CoRR (2020)
Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts
Effective Diversity in Population-Based Reinforcement Learning. CoRR (2020)
Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
Accelerated Message Passing for Entropy-Regularized MAP Inference. ICML (2020)
Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan
Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference. AISTATS (2020)
Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, Michael I. Jordan
Learning to Score Behaviors for Guided Policy Optimization. ICML (2020)
Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan
Robustness Guarantees for Mode Estimation with an Application to Bandits. CoRR (2020)
Yasin Abbasi-Yadkori, Aldo Pacchiano, My Phan
Regret Balancing for Bandit and RL Model Selection. CoRR (2020)
Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett
On Approximate Thompson Sampling with Langevin Algorithms. ICML (2020)
Jack Parker-Holder, Aldo Pacchiano, Krzysztof Marcin Choromanski, Stephen J. Roberts
Effective Diversity in Population Based Reinforcement Learning. NeurIPS (2020)
Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvári
Model Selection in Contextual Stochastic Bandit Problems. NeurIPS (2020)
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)
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)
Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill
Online Model Selection for Reinforcement Learning with Function Approximation. CoRR (2020)
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 (2020)
Philip J. Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen J. Roberts
Ready Policy One: World Building Through Active Learning. ICML (2020)
Silvia Chiappa, Ray Jiang, Tom Stepleton, Aldo Pacchiano, Heinrich Jiang, John Aslanides
A General Approach to Fairness with Optimal Transport. AAAI (2020)
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 (2020)
Aldo Pacchiano, Christoph Dann, Claudio Gentile, Peter L. Bartlett
Regret Bound Balancing and Elimination for Model Selection in Bandits and RL. CoRR (2020)
Philip J. Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts
Ready Policy One: World Building Through Active Learning. CoRR (2020)
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 (2019)
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces. CoRR (2019)
Aldo Pacchiano, Yoram Bachrach
Computing Stable Solutions in Threshold Network Flow Games With Bounded Treewidth. AAMAS (2019)
Krzysztof Choromanski, Aldo Pacchiano, Jeffrey Pennington, Yunhao Tang
KAMA-NNs: Low-dimensional Rotation Based Neural Networks. AISTATS (2019)
Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Yunhao Tang
ES-MAML: Simple Hessian-Free Meta Learning. CoRR (2019)
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)
Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Anna Choromanska, Krzysztof Choromanski, Michael I. Jordan
Wasserstein Reinforcement Learning. CoRR (2019)
Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan
Approximate Sherali-Adams Relaxations for MAP Inference via Entropy Regularization. CoRR (2019)
Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa
Wasserstein Fair Classification. CoRR (2019)
Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa
Wasserstein Fair Classification. UAI (2019)
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Vikas Sindhwani
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization. NeurIPS (2019)
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes. CoRR (2019)
Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett
Online learning with kernel losses. ICML (2019)
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 (2019)
Aldo Pacchiano, Niladri S. Chatterji, Peter L. Bartlett
Online learning with kernel losses. CoRR (2018)

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Publication Activity (10 Years)

Top Topics

Top Venues

Aldo Pacchiano