Pacchiano, Aldo

72 publications

ICLR 2025 A Theoretical Framework for Partially-Observed Reward States in RLHF Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari

ECML-PKDD 2025 Active Preference Optimization for Sample Efficient RLHF Nirjhar Das, Souradip Chakraborty, Aldo Pacchiano, Sayak Ray Chowdhury

ICML 2025 Adaptive Exploration for Multi-Reward Multi-Policy Evaluation Alessio Russo, Aldo Pacchiano

JMLR 2025 Contextual Bandits with Stage-Wise Constraints Aldo Pacchiano, Mohammad Ghavamzadeh, Peter Bartlett

ICML 2025 Feasible Action Search for Bandit Linear Programs via Thompson Sampling Aditya Gangrade, Aldo Pacchiano, Clayton Scott, Venkatesh Saligrama

ICML 2025 Multiple-Policy Evaluation via Density Estimation Yilei Chen, Aldo Pacchiano, Ioannis Paschalidis

ICLR 2025 ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization Chen Bo Calvin Zhang, Zhang-Wei Hong, Aldo Pacchiano, Pulkit Agrawal

COLT 2025 On the Hardness of Bandit Learning Nataly Brukhim, Aldo Pacchiano, Miroslav Dudik, Robert Schapire

NeurIPS 2025 Principled Fine-Tuning of LLMs from User-Edits: A Medley of Preference, Supervision, and Reward Dipendra Misra, Aldo Pacchiano, Ta-Chung Chi, Ge Gao

AISTATS 2025 Pure Exploration with Feedback Graphs Alessio Russo, Yichen Song, Aldo Pacchiano

ICLR 2025 Second Order Bounds for Contextual Bandits with Function Approximation Aldo Pacchiano

ICMLW 2024 A Theoretical Framework for Partially Observed Reward-States in RLHF Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari

ICMLW 2024 A Theoretical Framework for Partially-Observed Reward States in RLHF Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari

ICMLW 2024 Active Preference Optimization for Sample Efficient RLHF Nirjhar Das, Souradip Chakraborty, Aldo Pacchiano, Sayak Ray Chowdhury

AISTATS 2024 Data-Driven Online Model Selection with Regret Guarantees Chris Dann, Claudio Gentile, Aldo Pacchiano

TMLR 2024 Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity Jonathan Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill

ICLR 2024 Improving Offline RL by Blending Heuristics Sinong Geng, Aldo Pacchiano, Andrey Kolobov, Ching-An Cheng

ICMLW 2024 Multiple-Policy Evaluation via Density Estimation Yilei Chen, Aldo Pacchiano, Ioannis Paschalidis

ICMLW 2024 ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization Chen Bo Calvin Zhang, Zhang-Wei Hong, Aldo Pacchiano, Pulkit Agrawal

ICML 2024 Provable Interactive Learning with Hindsight Instruction Feedback Dipendra Misra, Aldo Pacchiano, Robert E. Schapire

NeurIPS 2024 State-Free Reinforcement Learning Mingyu Chen, Aldo Pacchiano, Xuezhou Zhang

NeurIPS 2023 A Unified Model and Dimension for Interactive Estimation Nataly Brukhim, Miro Dudik, Aldo Pacchiano, Robert E. Schapire

ALT 2023 An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit Aldo Pacchiano, Peter Bartlett, Michael Jordan

NeurIPS 2023 Anytime Model Selection in Linear Bandits Parnian Kassraie, Nicolas Emmenegger, Andreas Krause, Aldo Pacchiano

NeurIPSW 2023 Anytime Model Selection in Linear Bandits Parnian Kassraie, Nicolas Emmenegger, Andreas Krause, Aldo Pacchiano

AISTATS 2023 Dueling RL: Reinforcement Learning with Trajectory Preferences Aadirupa Saha, Aldo Pacchiano, Jonathan Lee

NeurIPS 2023 Experiment Planning with Function Approximation Aldo Pacchiano, Jonathan Lee, Emma Brunskill

ICMLW 2023 In-Context Decision-Making from Supervised Pretraining Jonathan Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill

ICML 2023 Leveraging Offline Data in Online Reinforcement Learning Andrew Wagenmaker, Aldo Pacchiano

ICLR 2023 Neural Design for Genetic Perturbation Experiments Aldo Pacchiano, Drausin Wulsin, Robert A Barton, Luis Voloch

NeurIPS 2023 Supervised Pretraining Can Learn In-Context Reinforcement Learning Jonathan Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill

ICMLW 2023 Undo Maps: A Tool for Adapting Policies to Perceptual Distortions Abhi Gupta, Ted Moskovitz, David Alvarez-Melis, Aldo Pacchiano

AISTATS 2022 Meta Learning MDPs with Linear Transition Models Robert Müller, Aldo Pacchiano

AISTATS 2022 Towards an Understanding of Default Policies in Multitask Policy Optimization Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano

NeurIPS 2022 Best of Both Worlds Model Selection Aldo Pacchiano, Christoph Dann, Claudio Gentile

NeurIPS 2022 Learning General World Models in a Handful of Reward-Free Deployments Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip Ball, Oleh Rybkin, S Roberts, Tim Rocktäschel, Edward Grefenstette

ICML 2022 Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael Jordan

NeurIPS 2022 Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity Abhishek Gupta, Aldo Pacchiano, Yuexiang Zhai, Sham Kakade, Sergey Levine

AISTATS 2021 Learning the Truth from Only One Side of the Story Heinrich Jiang, Qijia Jiang, Aldo Pacchiano

AISTATS 2021 Online Model Selection for Reinforcement Learning with Function Approximation Jonathan Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill

AISTATS 2021 Stochastic Bandits with Linear Constraints Aldo Pacchiano, Mohammad Ghavamzadeh, Peter Bartlett, Heinrich Jiang

ICML 2021 Dynamic Balancing for Model Selection in Bandits and RL Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Claudio Gentile, Aldo Pacchiano, Manish Purohit

NeurIPS 2021 Near Optimal Policy Optimization via REPS Aldo Pacchiano, Jonathan N Lee, Peter L. Bartlett, Ofir Nachum

NeurIPS 2021 Neural Pseudo-Label Optimism for the Bank Loan Problem Aldo Pacchiano, Shaun Singh, Edward Chou, Alex Berg, Jakob Foerster

NeurIPS 2021 On the Theory of Reinforcement Learning with Once-per-Episode Feedback Niladri Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan

NeurIPS 2021 Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta

AAAI 2021 Robustness Guarantees for Mode Estimation with an Application to Bandits Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan

ICML 2021 Sample Efficient Reinforcement Learning in Continuous State Spaces: A Perspective Beyond Linearity Dhruv Malik, Aldo Pacchiano, Vishwak Srinivasan, Yuanzhi Li

NeurIPS 2021 Tactical Optimism and Pessimism for Deep Reinforcement Learning Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan

UAI 2021 Towards Tractable Optimism in Model-Based Reinforcement Learning Aldo Pacchiano, Philip Ball, Jack Parker-Holder, Krzysztof Choromanski, Stephen Roberts

AAAI 2020 A General Approach to Fairness with Optimal Transport Silvia Chiappa, Ray Jiang, Tom Stepleton, Aldo Pacchiano, Heinrich Jiang, John Aslanides

ICML 2020 Accelerated Message Passing for Entropy-Regularized MAP Inference Jonathan Lee, Aldo Pacchiano, Peter Bartlett, Michael Jordan

AISTATS 2020 Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference Jonathan Lee, Aldo Pacchiano, Michael Jordan

ICLR 2020 ES-MAML: Simple Hessian-Free Meta Learning Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Yunhao Tang

NeurIPS 2020 Effective Diversity in Population Based Reinforcement Learning Jack Parker-Holder, Aldo Pacchiano, Krzysztof M Choromanski, Stephen J. Roberts

ICMLW 2020 Effective Diversity in Population Based Reinforcement Learning Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts

ICML 2020 Learning to Score Behaviors for Guided Policy Optimization Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, Michael Jordan

NeurIPS 2020 Model Selection in Contextual Stochastic Bandit Problems Aldo Pacchiano, My Phan, Yasin Abbasi Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvari

ICML 2020 On Approximate Thompson Sampling with Langevin Algorithms Eric Mazumdar, Aldo Pacchiano, Yian Ma, Michael Jordan, Peter Bartlett

AISTATS 2020 Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang

ICML 2020 Ready Policy One: World Building Through Active Learning Philip Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts

NeurIPS 2020 Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alexander Peysakhovich, Aldo Pacchiano, Jakob Foerster

ICML 2020 Stochastic Flows and Geometric Optimization on the Orthogonal Group 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, Tamas Sarlos, Adrian Weller, Vikas Sindhwani

ICMLW 2020 Taming the Herd: Multi-Modal Meta-Learning with a Population of Agents Robert Müller, Jack Parker-Holder, Aldo Pacchiano

NeurIPS 2019 From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization Krzysztof M Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Vikas Sindhwani

AISTATS 2019 KAMA-NNs: Low-Dimensional Rotation Based Neural Networks Krzysztof Choromanski, Aldo Pacchiano, Jeffrey Pennington, Yunhao Tang

ICML 2019 Online Learning with Kernel Losses Niladri Chatterji, Aldo Pacchiano, Peter Bartlett

CoRL 2019 Provably Robust Blackbox Optimization for Reinforcement Learning Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Deepali Jain, Yuxiang Yang, Atil Iscen, Jasmine Hsu, Vikas Sindhwani

UAI 2019 Wasserstein Fair Classification Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa

NeurIPS 2018 Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L Bartlett, Michael I Jordan

NeurIPS 2018 Geometrically Coupled Monte Carlo Sampling Mark Rowland, Krzysztof M Choromanski, François Chalus, Aldo Pacchiano, Tamas Sarlos, Richard E Turner, Adrian Weller

AISTATS 2017 Conditions Beyond Treewidth for Tightness of Higher-Order LP Relaxations Mark Rowland, Aldo Pacchiano, Adrian Weller