Computational Approaches to Poisson Traces Associated to Finite Subgroups of SP2n(ℂ). - Pavel Etingof; Sherry Gong; Aldo Pacchiano; Qingchun Ren; Travis Schedler (Exp. Math., 2012)

Real time clustering of time series using triangular potentials. - Aldo Pacchiano; Oliver Williams (CoRR, 2015)

Conditions beyond treewidth for tightness of higher-order LP relaxations. - Mark Rowland 001; Aldo Pacchiano; Adrian Weller (AISTATS, 2017)

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 001; Krzysztof Choromanski; François Chalus; Aldo Pacchiano; Tamás Sarlós; Richard E. Turner; Adrian Weller (NeurIPS, 2018)

A note on reinforcement learning with Wasserstein distance regularisation, with applications to multipolicy learning. - Mohammed Amin Abdullah 001; Aldo Pacchiano; Moez Draief (CoRR, 2018)

Gen-Oja: A Simple and Efficient Algorithm for Streaming Generalized Eigenvector Computation. - Kush Bhatia; Aldo Pacchiano; Nicolas Flammarion; Peter L. Bartlett; Michael I. Jordan (CoRR, 2018)

KAMA-NNs: Low-dimensional Rotation Based Neural Networks. - Krzysztof Choromanski; Aldo Pacchiano; Jeffrey Pennington; Yunhao Tang (AISTATS, 2019)

Computing Stable Solutions in Threshold Network Flow Games With Bounded Treewidth. - Aldo Pacchiano; Yoram Bachrach (AAMAS, 2019)

Provably Robust Blackbox Optimization for Reinforcement Learning. - Krzysztof Choromanski; Aldo Pacchiano; Jack Parker-Holder; Yunhao Tang; Deepali Jain; Yuxiang Yang 007; Atil Iscen; Jasmine Hsu; Vikas Sindhwani (CoRL, 2019)

Online learning with kernel losses. - Niladri S. Chatterji; Aldo Pacchiano; Peter L. Bartlett (ICML, 2019)

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

Wasserstein Fair Classification. - Ray Jiang; Aldo Pacchiano; Tom Stepleton; Heinrich Jiang; Silvia Chiappa (UAI, 2019)

When random search is not enough: Sample-Efficient and Noise-Robust Blackbox Optimization of RL Policies. - Krzysztof Choromanski; Aldo Pacchiano; Jack Parker-Holder; Jasmine Hsu; Atil Iscen; Deepali Jain; Vikas Sindhwani (CoRR, 2019)

Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces. - Krzysztof Choromanski; Aldo Pacchiano; Jack Parker-Holder; Yunhao Tang (CoRR, 2019)

Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes. - Krzysztof Choromanski; Aldo Pacchiano; Jack Parker-Holder; Yunhao Tang (CoRR, 2019)

Wasserstein Reinforcement Learning. - Aldo Pacchiano; Jack Parker-Holder; Yunhao Tang; Anna Choromanska; Krzysztof Choromanski; Michael I. Jordan (CoRR, 2019)

Approximate Sherali-Adams Relaxations for MAP Inference via Entropy Regularization. - Jonathan N. Lee; Aldo Pacchiano; Michael I. Jordan (CoRR, 2019)

Reinforcement Learning with Chromatic Networks. - Xingyou Song; Krzysztof Choromanski; Jack Parker-Holder; Yunhao Tang; Wenbo Gao; Aldo Pacchiano; Tamás Sarlós; Deepali Jain; Yuxiang Yang 007 (CoRR, 2019)

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

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

Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference. - Jonathan N. Lee; Aldo Pacchiano; Michael I. Jordan (AISTATS, 2020)

ES-MAML: Simple Hessian-Free Meta Learning. - Xingyou Song; Wenbo Gao; Yuxiang Yang 007; Krzysztof Choromanski; Aldo Pacchiano; Yunhao Tang (ICLR, 2020)

Ready Policy One: World Building Through Active Learning. - Philip J. Ball; Jack Parker-Holder; Aldo Pacchiano; Krzysztof Choromanski; Stephen J. Roberts (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 038; Aldo Pacchiano; et al. (ICML, 2020)

Accelerated Message Passing for Entropy-Regularized MAP Inference. - Jonathan N. Lee; Aldo Pacchiano; Peter L. Bartlett; Michael I. Jordan (ICML, 2020)

On Approximate Thompson Sampling with Langevin Algorithms. - Eric Mazumdar; Aldo Pacchiano; Yi-An Ma; Michael I. Jordan; Peter L. Bartlett (ICML, 2020)

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

Model Selection in Contextual Stochastic Bandit Problems. - Aldo Pacchiano; My Phan; Yasin Abbasi-Yadkori; Anup Rao 002; Julian Zimmert; Tor Lattimore; Csaba Szepesvári (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 N. Foerster (NeurIPS, 2020)

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

On Thompson Sampling with Langevin Algorithms. - Eric Mazumdar; Aldo Pacchiano; Yi-An Ma; Peter L. Bartlett; Michael I. Jordan (CoRR, 2020)

Regret Balancing for Bandit and RL Model Selection. - Yasin Abbasi-Yadkori; Aldo Pacchiano; My Phan (CoRR, 2020)

On Optimism in Model-Based Reinforcement Learning. - Aldo Pacchiano; Philip J. Ball; Jack Parker-Holder; Krzysztof Choromanski; Stephen Roberts 001 (CoRR, 2020)

Regret Bound Balancing and Elimination for Model Selection in Bandits and RL. - Aldo Pacchiano; Christoph Dann; Claudio Gentile; Peter L. Bartlett (CoRR, 2020)

Robustness Guarantees for Mode Estimation with an Application to Bandits. - Aldo Pacchiano; Heinrich Jiang; Michael I. Jordan (AAAI, 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 N. Lee; Aldo Pacchiano; Vidya Muthukumar; Weihao Kong; Emma Brunskill (AISTATS, 2021)

Stochastic Bandits with Linear Constraints. - Aldo Pacchiano; Mohammad Ghavamzadeh; Peter L. Bartlett; Heinrich Jiang (AISTATS, 2021)

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

Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity. - Dhruv Malik; Aldo Pacchiano; Vishwak Srinivasan; Yuanzhi Li (ICML, 2021)

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

Tactical Optimism and Pessimism for Deep Reinforcement Learning. - Ted Moskovitz; Jack Parker-Holder; Aldo Pacchiano; Michael Arbel; Michael I. Jordan (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; Alexander C. Berg; Jakob N. Foerster (NeurIPS, 2021)

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

Towards tractable optimism in model-based reinforcement learning. - Aldo Pacchiano; Philip J. Ball; Jack Parker-Holder; Krzysztof Choromanski; Stephen Roberts 001 (UAI, 2021)

Fairness with Continuous Optimal Transport. - Silvia Chiappa; Aldo Pacchiano (CoRR, 2021)

ES-ENAS: Combining Evolution Strategies with Neural Architecture Search at No Extra Cost for Reinforcement Learning. - Xingyou Song; Krzysztof Choromanski; Jack Parker-Holder; Yunhao Tang; Daiyi Peng; Deepali Jain; Wenbo Gao; Aldo Pacchiano; Tamás Sarlós; Yuxiang Yang 007 (CoRR, 2021)

Deep Reinforcement Learning with Dynamic Optimism. - Ted Moskovitz; Jack Parker-Holder; Aldo Pacchiano; Michael Arbel (CoRR, 2021)

Unlocking Pixels for Reinforcement Learning via Implicit Attention. - Krzysztof Choromanski; Deepali Jain; Jack Parker-Holder; Xingyou Song; Valerii Likhosherstov; Anirban Santara; Aldo Pacchiano; Yunhao Tang; Adrian Weller (CoRR, 2021)

Parallelizing Contextual Linear Bandits. - Jeffrey Chan; Aldo Pacchiano; Nilesh Tripuraneni; Yun S. Song; Peter L. Bartlett; Michael I. Jordan (CoRR, 2021)

Model Selection for Contextual Bandits and Reinforcement Learning - Aldo Pacchiano (2021)

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

Meta Learning MDPs with linear transition models. - Robert Müller; Aldo Pacchiano (AISTATS, 2022)

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

Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity. - Abhishek Gupta 004; Aldo Pacchiano; Yuexiang Zhai; Sham M. Kakade; Sergey Levine (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 J. Ball; Oleh Rybkin; Stephen Roberts 001; Tim Rocktäschel; Edward Grefenstette (NeurIPS, 2022)

Joint Representation Training in Sequential Tasks with Shared Structure. - Aldo Pacchiano; Ofir Nachum; Nilesh Tripuraneni; Peter L. Bartlett (CoRR, 2022)

Transfer RL via the Undo Maps Formalism. - Abhi Gupta; Ted Moskovitz; David Alvarez-Melis; Aldo Pacchiano (CoRR, 2022)

Dueling RL: Reinforcement Learning with Trajectory Preferences. - Aadirupa Saha; Aldo Pacchiano; Jonathan Lee 002 (AISTATS, 2023)

An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. - Aldo Pacchiano; Peter L. Bartlett; Michael I. Jordan (ALT, 2023)

Neural Design for Genetic Perturbation Experiments. - Aldo Pacchiano; Drausin Wulsin; Robert A. Barton; Luis F. Voloch (ICLR, 2023)

Leveraging Offline Data in Online Reinforcement Learning. - Andrew Wagenmaker; Aldo Pacchiano (ICML, 2023)

Supervised Pretraining Can Learn In-Context Reinforcement Learning. - Jonathan Lee 002; Annie Xie; Aldo Pacchiano; Yash Chandak; Chelsea Finn; Ofir Nachum; Emma Brunskill (NeurIPS, 2023)

A Unified Model and Dimension for Interactive Estimation. - Nataly Brukhim; Miro Dudík; Aldo Pacchiano; Robert E. Schapire (NeurIPS, 2023)

Anytime Model Selection in Linear Bandits. - Parnian Kassraie; Nicolas Emmenegger; Andreas Krause 001; Aldo Pacchiano (NeurIPS, 2023)

Experiment Planning with Function Approximation. - Aldo Pacchiano; Jonathan Lee 002; Emma Brunskill (NeurIPS, 2023)

Estimating Optimal Policy Value in General Linear Contextual Bandits. - Jonathan N. Lee; Weihao Kong; Aldo Pacchiano; Vidya Muthukumar; Emma Brunskill (CoRR, 2023)

Data-Driven Regret Balancing for Online Model Selection in Bandits. - Aldo Pacchiano; Christoph Dann; Claudio Gentile (CoRR, 2023)

Unbiased Decisions Reduce Regret: Adversarial Domain Adaptation for the Bank Loan Problem. - Elena Gal; Shaun Singh; Aldo Pacchiano; Ben Walker; Terry J. Lyons; Jakob N. Foerster (CoRR, 2023)

Data-Driven Online Model Selection With Regret Guarantees. - Christoph Dann; Claudio Gentile; Aldo Pacchiano (AISTATS, 2024)

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

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

State-free Reinforcement Learning. - Mingyu Chen 012; Aldo Pacchiano; Xuezhou Zhang (NeurIPS, 2024)

Contextual Bandits with Stage-wise Constraints. - Aldo Pacchiano; Mohammad Ghavamzadeh; Peter L. Bartlett (CoRR, 2024)

A Framework for Partially Observed Reward-States in RLHF. - Chinmaya Kausik; Mirco Mutti; Aldo Pacchiano; Ambuj Tewari (CoRR, 2024)

Provably Sample Efficient RLHF via Active Preference Optimization. - Nirjhar Das; Souradip Chakraborty; Aldo Pacchiano; Sayak Ray Chowdhury (CoRR, 2024)

Multiple-policy Evaluation via Density Estimation. - Yilei Chen; Aldo Pacchiano; Ioannis Ch. Paschalidis (CoRR, 2024)

Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non-Stationary Objectives. - Aida Afshar; Aldo Pacchiano (CoRR, 2024)

Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity. - Jonathan Lee 002; Weihao Kong; Aldo Pacchiano; Vidya Muthukumar; Emma Brunskill (Trans. Mach. Learn. Res., 2024)

Pure Exploration with Feedback Graphs. - Alessio Russo; Yichen Song; Aldo Pacchiano (AISTATS, 2025)

On the Hardness of Bandit Learning. - Nataly Brukhim; Aldo Pacchiano; Miroslav Dudík; Robert E. Schapire (COLT, 2025)

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

Second Order Bounds for Contextual Bandits with Function Approximation. - Aldo Pacchiano (ICLR, 2025)

ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization. - Chen Bo Calvin Zhang; Zhang-Wei Hong; Aldo Pacchiano; Pulkit Agrawal 001 (ICLR, 2025)

Adaptive Exploration for Multi-Reward Multi-Policy Evaluation. - Alessio Russo; Aldo Pacchiano (CoRR, 2025)

Language Model Personalization via Reward Factorization. - Idan Shenfeld; Felix Faltings; Pulkit Agrawal 001; Aldo Pacchiano (CoRR, 2025)

Learning to Explore: An In-Context Learning Approach for Pure Exploration. - Alessio Russo; Ryan Welch; Aldo Pacchiano (CoRR, 2025)

Meet Me at the Arm: The Cooperative Multi-Armed Bandits Problem with Shareable Arms. - Xinyi Hu; Aldo Pacchiano (CoRR, 2025)