Aldo Pacchiano
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
39
Venues
8
Active years
2017–2025
Best venue rank
A*
Where they publish
Papers
39 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | AISTATS | Pure Exploration with Feedback Graphs. | Alessio Russo, Yichen Song, Aldo Pacchiano |
| 2025 | COLT | On the Hardness of Bandit Learning. | Nataly Brukhim, Aldo Pacchiano, Miroslav Dudk, Robert E. Schapire |
| 2025 | ICLR | A Theoretical Framework for Partially-Observed Reward States in RLHF. | Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari |
| 2025 | ICLR | Second Order Bounds for Contextual Bandits with Function Approximation. | Aldo Pacchiano |
| 2025 | ICLR | ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization. | Chen Bo Calvin Zhang, Zhang-Wei Hong, Aldo Pacchiano, Pulkit Agrawal |
| 2025 | ICML | Multiple-policy Evaluation via Density Estimation. | Yilei Chen, Aldo Pacchiano, Ioannis Paschalidis |
| 2025 | ICML | Feasible Action Search for Bandit Linear Programs via Thompson Sampling. | Aditya Gangrade, Aldo Pacchiano, Clayton Scott, Venkatesh Saligrama |
| 2025 | ICML | Adaptive Exploration for Multi-Reward Multi-Policy Evaluation. | Alessio Russo, Aldo Pacchiano |
| 2024 | AISTATS | Data-Driven Online Model Selection With Regret Guarantees. | Christoph Dann, Claudio Gentile, Aldo Pacchiano |
| 2024 | ICLR | Improving Offline RL by Blending Heuristics. | Sinong Geng, Aldo Pacchiano, Andrey Kolobov, Ching-An Cheng |
| 2024 | ICML | Provable Interactive Learning with Hindsight Instruction Feedback. | Dipendra Misra, Aldo Pacchiano, Robert E. Schapire |
| 2023 | AISTATS | Dueling RL: Reinforcement Learning with Trajectory Preferences. | Aadirupa Saha, Aldo Pacchiano, Jonathan Lee |
| 2023 | ALT | An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. | Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan |
| 2023 | ICLR | Neural Design for Genetic Perturbation Experiments. | Aldo Pacchiano, Drausin Wulsin, Robert A. Barton, Luis F. Voloch |
| 2023 | ICML | Leveraging Offline Data in Online Reinforcement Learning. | Andrew Wagenmaker, Aldo Pacchiano |
| 2022 | AISTATS | Towards an Understanding of Default Policies in Multitask Policy Optimization. | Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano |
| 2022 | AISTATS | Meta Learning MDPs with linear transition models. | Robert Mller, Aldo Pacchiano |
| 2022 | ICML | Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. | Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan |
| 2021 | AAAI | Robustness Guarantees for Mode Estimation with an Application to Bandits. | Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan |
| 2021 | AISTATS | Learning the Truth From Only One Side of the Story. | Heinrich Jiang, Qijia Jiang, Aldo Pacchiano |
| 2021 | AISTATS | Online Model Selection for Reinforcement Learning with Function Approximation. | Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill |
| 2021 | AISTATS | Stochastic Bandits with Linear Constraints. | Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang |
| 2021 | ICML | Dynamic Balancing for Model Selection in Bandits and RL. | Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Claudio Gentile, Aldo Pacchiano, Manish Purohit |
| 2021 | ICML | Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity. | Dhruv Malik, Aldo Pacchiano, Vishwak Srinivasan, Yuanzhi Li |
| 2021 | UAI | Towards tractable optimism in model-based reinforcement learning. | Aldo Pacchiano, Philip J. Ball, Jack Parker-Holder, Krzysztof Choromanski, Stephen Roberts |
| 2020 | AAAI | A General Approach to Fairness with Optimal Transport. | Silvia Chiappa, Ray Jiang, Tom Stepleton, Aldo Pacchiano, Heinrich Jiang, John Aslanides |
| 2020 | AISTATS | Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes. | Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang |
| 2020 | AISTATS | Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference. | Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan |
| 2020 | ICLR | ES-MAML: Simple Hessian-Free Meta Learning. | Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Yunhao Tang |
| 2020 | ICML | Ready Policy One: World Building Through Active Learning. | Philip J. Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen J. Roberts |
| 2020 | ICML | 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, Tams Sarls, Adrian Weller, Vikas Sindhwani |
| 2020 | ICML | Accelerated Message Passing for Entropy-Regularized MAP Inference. | Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan |
| 2020 | ICML | On Approximate Thompson Sampling with Langevin Algorithms. | Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett |
| 2020 | ICML | Learning to Score Behaviors for Guided Policy Optimization. | Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, Michael I. Jordan |
| 2019 | AISTATS | KAMA-NNs: Low-dimensional Rotation Based Neural Networks. | Krzysztof Choromanski, Aldo Pacchiano, Jeffrey Pennington, Yunhao Tang |
| 2019 | CoRL | 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 |
| 2019 | ICML | Online learning with kernel losses. | Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett |
| 2019 | UAI | Wasserstein Fair Classification. | Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa |
| 2017 | AISTATS | Conditions beyond treewidth for tightness of higher-order LP relaxations. | Mark Rowland, Aldo Pacchiano, Adrian Weller |