Aldo Pacchiano

143 papers A* 42A 15B 1Misc 2Journal 81Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Bayesian Online Model Selection. CoRR Aida Afshar, Yuke Zhang, Aldo Pacchiano
2026	J	`jnl`	In-Context Learning for Pure Exploration in Continuous Spaces. CoRR Alessio Russo, Yin-Ching Lee, Ryan Welch, Aldo Pacchiano
2026	J	`jnl`	Principled Fine-tuning of LLMs from User-Edits: A Medley of Preference, Supervision, and Reward. CoRR Dipendra Misra, Aldo Pacchiano, Ta-Chung Chi, Ge Gao
2026	J	`jnl`	Scaling In-Context Online Learning Capability of LLMs via Cross-Episode Meta-RL. CoRR Xiaofeng Lin, Sirou Zhu, Yilei Chen, Mingyu Chen, Hejian Sang, Ioannis Paschalidis, Zhipeng Wang, Aldo Pacchiano, Xuezhou Zhang
2025	A*	`conf`	A Theoretical Framework for Partially-Observed Reward States in RLHF. ICLR Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari
2025	—	`conf`	Active Preference Optimization for Sample Efficient RLHF. ECML/PKDD (5) Nirjhar Das, Souradip Chakraborty, Aldo Pacchiano, Sayak Ray Chowdhury
2025	A*	`conf`	Adaptive Exploration for Multi-Reward Multi-Policy Evaluation. ICML Alessio Russo, Aldo Pacchiano
2025	J	`jnl`	Adaptive Exploration for Multi-Reward Multi-Policy Evaluation. CoRR Alessio Russo, Aldo Pacchiano
2025	J	`jnl`	Contextual Bandits with Stage-wise Constraints. J. Mach. Learn. Res. Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett
2025	J	`jnl`	Enhancing Diversity in Large Language Models via Determinantal Point Processes. CoRR Yilei Chen, Souradip Chakraborty, Lorenz Wolf, Ioannis Ch. Paschalidis, Aldo Pacchiano
2025	A*	`conf`	Feasible Action Search for Bandit Linear Programs via Thompson Sampling. ICML Aditya Gangrade, Aldo Pacchiano, Clayton Scott, Venkatesh Saligrama
2025	J	`jnl`	Improved Training Mechanism for Reinforcement Learning via Online Model Selection. CoRR Aida Afshar, Aldo Pacchiano
2025	J	`jnl`	Language Model Personalization via Reward Factorization. CoRR Idan Shenfeld, Felix Faltings, Pulkit Agrawal, Aldo Pacchiano
2025	J	`jnl`	Learning to Explore: An In-Context Learning Approach for Pure Exploration. CoRR Alessio Russo, Ryan Welch, Aldo Pacchiano
2025	J	`jnl`	Meet Me at the Arm: The Cooperative Multi-Armed Bandits Problem with Shareable Arms. CoRR Xinyi Hu, Aldo Pacchiano
2025	A*	`conf`	Multiple-policy Evaluation via Density Estimation. ICML Yilei Chen, Aldo Pacchiano, Ioannis Paschalidis
2025	A*	`conf`	ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization. ICLR Chen Bo Calvin Zhang, Zhang-Wei Hong, Aldo Pacchiano, Pulkit Agrawal
2025	A*	`conf`	On the Hardness of Bandit Learning. COLT Nataly Brukhim, Aldo Pacchiano, Miroslav Dudík, Robert E. Schapire
2025	J	`jnl`	On the Hardness of Bandit Learning. CoRR Nataly Brukhim, Aldo Pacchiano, Miroslav Dudík, Robert E. Schapire
2025	A	`conf`	Pure Exploration with Feedback Graphs. AISTATS Alessio Russo, Yichen Song, Aldo Pacchiano
2025	J	`jnl`	Pure Exploration with Feedback Graphs. CoRR Alessio Russo, Yichen Song, Aldo Pacchiano
2025	A*	`conf`	Second Order Bounds for Contextual Bandits with Function Approximation. ICLR Aldo Pacchiano
2025	J	`jnl`	The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification. CoRR Tavor Z. Baharav, Spyros Dragazis, Aldo Pacchiano
2024	J	`jnl`	A Framework for Partially Observed Reward-States in RLHF. CoRR Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari
2024	Misc	`ed.`	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 ISAIM Reneta P. Barneva, Valentin E. Brimkov, Claudio Gentile, Aldo Pacchiano
2024	J	`jnl`	Contextual Bandits with Stage-wise Constraints. CoRR Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett
2024	A	`conf`	Data-Driven Online Model Selection With Regret Guarantees. AISTATS Christoph Dann, Claudio Gentile, Aldo Pacchiano
2024	J	`jnl`	Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity. Trans. Mach. Learn. Res. Jonathan Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill
2024	J	`jnl`	Experiment Planning with Function Approximation. CoRR Aldo Pacchiano, Jonathan N. Lee, Emma Brunskill
2024	A*	`conf`	Improving Offline RL by Blending Heuristics. ICLR Sinong Geng, Aldo Pacchiano, Andrey Kolobov, Ching-An Cheng
2024	J	`jnl`	Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non-Stationary Objectives. CoRR Aida Afshar, Aldo Pacchiano
2024	J	`jnl`	Multiple-policy Evaluation via Density Estimation. CoRR Yilei Chen, Aldo Pacchiano, Ioannis Ch. Paschalidis
2024	J	`jnl`	ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization. CoRR Chen Bo Calvin Zhang, Zhang-Wei Hong, Aldo Pacchiano, Pulkit Agrawal
2024	A*	`conf`	Provable Interactive Learning with Hindsight Instruction Feedback. ICML Dipendra Misra, Aldo Pacchiano, Robert E. Schapire
2024	J	`jnl`	Provable Interactive Learning with Hindsight Instruction Feedback. CoRR Dipendra Misra, Aldo Pacchiano, Robert E. Schapire
2024	J	`jnl`	Provably Sample Efficient RLHF via Active Preference Optimization. CoRR Nirjhar Das, Souradip Chakraborty, Aldo Pacchiano, Sayak Ray Chowdhury
2024	J	`jnl`	Second Order Bounds for Contextual Bandits with Function Approximation. CoRR Aldo Pacchiano
2024	A*	`conf`	State-free Reinforcement Learning. NeurIPS Mingyu Chen, Aldo Pacchiano, Xuezhou Zhang
2024	J	`jnl`	State-free Reinforcement Learning. CoRR Mingyu Chen, Aldo Pacchiano, Xuezhou Zhang
2023	A*	`conf`	A Unified Model and Dimension for Interactive Estimation. NeurIPS Nataly Brukhim, Miro Dudík, Aldo Pacchiano, Robert E. Schapire
2023	J	`jnl`	A Unified Model and Dimension for Interactive Estimation. CoRR Nataly Brukhim, Miroslav Dudík, Aldo Pacchiano, Robert E. Schapire
2023	B	`conf`	An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. ALT Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
2023	A*	`conf`	Anytime Model Selection in Linear Bandits. NeurIPS Parnian Kassraie, Nicolas Emmenegger, Andreas Krause, Aldo Pacchiano
2023	J	`jnl`	Anytime Model Selection in Linear Bandits. CoRR Parnian Kassraie, Aldo Pacchiano, Nicolas Emmenegger, Andreas Krause
2023	J	`jnl`	Data-Driven Regret Balancing for Online Model Selection in Bandits. CoRR Aldo Pacchiano, Christoph Dann, Claudio Gentile
2023	A	`conf`	Dueling RL: Reinforcement Learning with Trajectory Preferences. AISTATS Aadirupa Saha, Aldo Pacchiano, Jonathan Lee
2023	J	`jnl`	Estimating Optimal Policy Value in General Linear Contextual Bandits. CoRR Jonathan N. Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill
2023	A*	`conf`	Experiment Planning with Function Approximation. NeurIPS Aldo Pacchiano, Jonathan Lee, Emma Brunskill
2023	J	`jnl`	Improving Offline RL by Blending Heuristics. CoRR Sinong Geng, Aldo Pacchiano, Andrey Kolobov, Ching-An Cheng
2023	A*	`conf`	Leveraging Offline Data in Online Reinforcement Learning. ICML Andrew Wagenmaker, Aldo Pacchiano
2023	A*	`conf`	Neural Design for Genetic Perturbation Experiments. ICLR Aldo Pacchiano, Drausin Wulsin, Robert A. Barton, Luis F. Voloch
2023	A*	`conf`	Supervised Pretraining Can Learn In-Context Reinforcement Learning. NeurIPS Jonathan Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill
2023	J	`jnl`	Supervised Pretraining Can Learn In-Context Reinforcement Learning. CoRR Jonathan N. Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill
2023	J	`jnl`	Unbiased Decisions Reduce Regret: Adversarial Domain Adaptation for the Bank Loan Problem. CoRR Elena Gal, Shaun Singh, Aldo Pacchiano, Ben Walker, Terry J. Lyons, Jakob N. Foerster
2022	A*	`conf`	Best of Both Worlds Model Selection. NeurIPS Aldo Pacchiano, Christoph Dann, Claudio Gentile
2022	J	`jnl`	Best of Both Worlds Model Selection. CoRR Aldo Pacchiano, Christoph Dann, Claudio Gentile
2022	J	`jnl`	Joint Representation Training in Sequential Tasks with Shared Structure. CoRR Aldo Pacchiano, Ofir Nachum, Nilesh Tripuraneni, Peter L. Bartlett
2022	A*	`conf`	Learning General World Models in a Handful of Reward-Free Deployments. NeurIPS Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip J. Ball, Oleh Rybkin, Stephen Roberts, Tim Rocktäschel, Edward Grefenstette
2022	J	`jnl`	Learning General World Models in a Handful of Reward-Free Deployments. CoRR Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip J. Ball, Oleh Rybkin, Stephen J. Roberts, Tim Rocktäschel, Edward Grefenstette
2022	J	`jnl`	Leveraging Offline Data in Online Reinforcement Learning. CoRR Andrew Wagenmaker, Aldo Pacchiano
2022	J	`jnl`	Meta Learning MDPs with Linear Transition Models. CoRR Robert Müller, Aldo Pacchiano
2022	A	`conf`	Meta Learning MDPs with linear transition models. AISTATS Robert Müller, Aldo Pacchiano
2022	J	`jnl`	Neural Design for Genetic Perturbation Experiments. CoRR Aldo Pacchiano, Drausin Wulsin, Robert A. Barton, Luis F. Voloch
2022	A*	`conf`	Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. ICML Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan
2022	J	`jnl`	Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback. CoRR Tianyi Lin, Aldo Pacchiano, Yaodong Yu, Michael I. Jordan
2022	A	`conf`	Towards an Understanding of Default Policies in Multitask Policy Optimization. AISTATS Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano
2022	J	`jnl`	Transfer RL via the Undo Maps Formalism. CoRR Abhi Gupta, Ted Moskovitz, David Alvarez-Melis, Aldo Pacchiano
2022	A*	`conf`	Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity. NeurIPS Abhishek Gupta, Aldo Pacchiano, Yuexiang Zhai, Sham M. Kakade, Sergey Levine
2022	J	`jnl`	Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity. CoRR Abhishek Gupta, Aldo Pacchiano, Yuexiang Zhai, Sham M. Kakade, Sergey Levine
2021	J	`jnl`	An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit. CoRR Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
2021	J	`jnl`	Deep Reinforcement Learning with Dynamic Optimism. CoRR Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel
2021	J	`jnl`	Dueling RL: Reinforcement Learning with Trajectory Preferences. CoRR Aldo Pacchiano, Aadirupa Saha, Jonathan Lee
2021	A*	`conf`	Dynamic Balancing for Model Selection in Bandits and RL. ICML Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Claudio Gentile, Aldo Pacchiano, Manish Purohit
2021	J	`jnl`	ES-ENAS: Combining Evolution Strategies with Neural Architecture Search at No Extra Cost for Reinforcement Learning. CoRR Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Daiyi Peng, Deepali Jain, Wenbo Gao, Aldo Pacchiano, Tamás Sarlós, Yuxiang Yang
2021	J	`jnl`	Fairness with Continuous Optimal Transport. CoRR Silvia Chiappa, Aldo Pacchiano
2021	A	`conf`	Learning the Truth From Only One Side of the Story. AISTATS Heinrich Jiang, Qijia Jiang, Aldo Pacchiano
2021	—	`—`	Model Selection for Contextual Bandits and Reinforcement Learning Aldo Pacchiano
2021	A*	`conf`	Near Optimal Policy Optimization via REPS. NeurIPS Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum
2021	J	`jnl`	Near Optimal Policy Optimization via REPS. CoRR Aldo Pacchiano, Jonathan N. Lee, Peter L. Bartlett, Ofir Nachum
2021	A*	`conf`	Neural Pseudo-Label Optimism for the Bank Loan Problem. NeurIPS Aldo Pacchiano, Shaun Singh, Edward Chou, Alexander C. Berg, Jakob N. Foerster
2021	J	`jnl`	Neural Pseudo-Label Optimism for the Bank Loan Problem. CoRR Aldo Pacchiano, Shaun Singh, Edward Chou, Alexander C. Berg, Jakob N. Foerster
2021	A*	`conf`	On the Theory of Reinforcement Learning with Once-per-Episode Feedback. NeurIPS Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
2021	J	`jnl`	On the Theory of Reinforcement Learning with Once-per-Episode Feedback. CoRR Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
2021	A	`conf`	Online Model Selection for Reinforcement Learning with Function Approximation. AISTATS Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill
2021	J	`jnl`	Parallelizing Contextual Linear Bandits. CoRR Jeffrey Chan, Aldo Pacchiano, Nilesh Tripuraneni, Yun S. Song, Peter L. Bartlett, Michael I. Jordan
2021	A*	`conf`	Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection. NeurIPS Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta
2021	J	`jnl`	Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection. CoRR Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta
2021	A*	`conf`	Robustness Guarantees for Mode Estimation with an Application to Bandits. AAAI Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan
2021	A*	`conf`	Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity. ICML Dhruv Malik, Aldo Pacchiano, Vishwak Srinivasan, Yuanzhi Li
2021	J	`jnl`	Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity. CoRR Dhruv Malik, Aldo Pacchiano, Vishwak Srinivasan, Yuanzhi Li
2021	A	`conf`	Stochastic Bandits with Linear Constraints. AISTATS Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang
2021	A*	`conf`	Tactical Optimism and Pessimism for Deep Reinforcement Learning. NeurIPS Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel, Michael I. Jordan
2021	J	`jnl`	Towards an Understanding of Default Policies in Multitask Policy Optimization. CoRR Ted Moskovitz, Michael Arbel, Jack Parker-Holder, Aldo Pacchiano
2021	A	`conf`	Towards tractable optimism in model-based reinforcement learning. UAI Aldo Pacchiano, Philip J. Ball, Jack Parker-Holder, Krzysztof Choromanski, Stephen Roberts
2021	J	`jnl`	Unlocking Pixels for Reinforcement Learning via Implicit Attention. CoRR Krzysztof Choromanski, Deepali Jain, Jack Parker-Holder, Xingyou Song, Valerii Likhosherstov, Anirban Santara, Aldo Pacchiano, Yunhao Tang, Adrian Weller
2020	A*	`conf`	A General Approach to Fairness with Optimal Transport. AAAI Silvia Chiappa, Ray Jiang, Tom Stepleton, Aldo Pacchiano, Heinrich Jiang, John Aslanides
2020	A*	`conf`	Accelerated Message Passing for Entropy-Regularized MAP Inference. ICML Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
2020	J	`jnl`	Accelerated Message Passing for Entropy-Regularized MAP Inference. CoRR Jonathan N. Lee, Aldo Pacchiano, Peter L. Bartlett, Michael I. Jordan
2020	A	`conf`	Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference. AISTATS Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan
2020	A*	`conf`	ES-MAML: Simple Hessian-Free Meta Learning. ICLR Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Yunhao Tang
2020	A*	`conf`	Effective Diversity in Population Based Reinforcement Learning. NeurIPS Jack Parker-Holder, Aldo Pacchiano, Krzysztof Marcin Choromanski, Stephen J. Roberts
2020	J	`jnl`	Effective Diversity in Population-Based Reinforcement Learning. CoRR Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts
2020	J	`jnl`	Learning the Truth From Only One Side of the Story. CoRR Heinrich Jiang, Qijia Jiang, Aldo Pacchiano
2020	A*	`conf`	Learning to Score Behaviors for Guided Policy Optimization. ICML Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, Michael I. Jordan
2020	A*	`conf`	Model Selection in Contextual Stochastic Bandit Problems. NeurIPS Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvári
2020	J	`jnl`	Model Selection in Contextual Stochastic Bandit Problems. CoRR Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvári
2020	A*	`conf`	On Approximate Thompson Sampling with Langevin Algorithms. ICML Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Michael I. Jordan, Peter L. Bartlett
2020	J	`jnl`	On Optimism in Model-Based Reinforcement Learning. CoRR Aldo Pacchiano, Philip J. Ball, Jack Parker-Holder, Krzysztof Choromanski, Stephen Roberts
2020	J	`jnl`	On Thompson Sampling with Langevin Algorithms. CoRR Eric Mazumdar, Aldo Pacchiano, Yi-An Ma, Peter L. Bartlett, Michael I. Jordan
2020	J	`jnl`	Online Model Selection for Reinforcement Learning with Function Approximation. CoRR Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill
2020	A	`conf`	Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes. AISTATS Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
2020	A*	`conf`	Ready Policy One: World Building Through Active Learning. ICML Philip J. Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen J. Roberts
2020	J	`jnl`	Ready Policy One: World Building Through Active Learning. CoRR Philip J. Ball, Jack Parker-Holder, Aldo Pacchiano, Krzysztof Choromanski, Stephen Roberts
2020	J	`jnl`	Regret Balancing for Bandit and RL Model Selection. CoRR Yasin Abbasi-Yadkori, Aldo Pacchiano, My Phan
2020	J	`jnl`	Regret Bound Balancing and Elimination for Model Selection in Bandits and RL. CoRR Aldo Pacchiano, Christoph Dann, Claudio Gentile, Peter L. Bartlett
2020	A*	`conf`	Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian. NeurIPS Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alexander Peysakhovich, Aldo Pacchiano, Jakob N. Foerster
2020	J	`jnl`	Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian. CoRR Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alex Peysakhovich, Aldo Pacchiano, Jakob N. Foerster
2020	J	`jnl`	Robustness Guarantees for Mode Estimation with an Application to Bandits. CoRR Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan
2020	J	`jnl`	Stochastic Bandits with Linear Constraints. CoRR Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang
2020	A*	`conf`	Stochastic Flows and Geometric Optimization on the Orthogonal Group. ICML 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
2020	J	`jnl`	Stochastic Flows and Geometric Optimization on the Orthogonal Group. CoRR 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
2019	J	`jnl`	Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces. CoRR Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
2019	J	`jnl`	Approximate Sherali-Adams Relaxations for MAP Inference via Entropy Regularization. CoRR Jonathan N. Lee, Aldo Pacchiano, Michael I. Jordan
2019	A	`conf`	Computing Stable Solutions in Threshold Network Flow Games With Bounded Treewidth. AAMAS Aldo Pacchiano, Yoram Bachrach
2019	J	`jnl`	ES-MAML: Simple Hessian-Free Meta Learning. CoRR Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, Yunhao Tang
2019	A*	`conf`	From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization. NeurIPS Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Vikas Sindhwani
2019	A	`conf`	KAMA-NNs: Low-dimensional Rotation Based Neural Networks. AISTATS Krzysztof Choromanski, Aldo Pacchiano, Jeffrey Pennington, Yunhao Tang
2019	A*	`conf`	Online learning with kernel losses. ICML Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett
2019	Misc	`conf`	Provably Robust Blackbox Optimization for Reinforcement Learning. CoRL Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Deepali Jain, Yuxiang Yang, Atil Iscen, Jasmine Hsu, Vikas Sindhwani
2019	J	`jnl`	Reinforcement Learning with Chromatic Networks. CoRR Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder, Yunhao Tang, Wenbo Gao, Aldo Pacchiano, Tamás Sarlós, Deepali Jain, Yuxiang Yang
2019	J	`jnl`	Structured Monte Carlo Sampling for Nonisotropic Distributions via Determinantal Point Processes. CoRR Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang
2019	A	`conf`	Wasserstein Fair Classification. UAI Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa
2019	J	`jnl`	Wasserstein Fair Classification. CoRR Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa
2019	J	`jnl`	Wasserstein Reinforcement Learning. CoRR Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Anna Choromanska, Krzysztof Choromanski, Michael I. Jordan
2019	J	`jnl`	When random search is not enough: Sample-Efficient and Noise-Robust Blackbox Optimization of RL Policies. CoRR Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Jasmine Hsu, Atil Iscen, Deepali Jain, Vikas Sindhwani
2018	J	`jnl`	A note on reinforcement learning with Wasserstein distance regularisation, with applications to multipolicy learning. CoRR Mohammed Amin Abdullah, Aldo Pacchiano, Moez Draief
2018	J	`jnl`	Gen-Oja: A Simple and Efficient Algorithm for Streaming Generalized Eigenvector Computation. CoRR Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan
2018	A*	`conf`	Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation. NeurIPS Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael I. Jordan
2018	A*	`conf`	Geometrically Coupled Monte Carlo Sampling. NeurIPS Mark Rowland, Krzysztof Choromanski, François Chalus, Aldo Pacchiano, Tamás Sarlós, Richard E. Turner, Adrian Weller
2018	J	`jnl`	Online learning with kernel losses. CoRR Aldo Pacchiano, Niladri S. Chatterji, Peter L. Bartlett
2017	A	`conf`	Conditions beyond treewidth for tightness of higher-order LP relaxations. AISTATS Mark Rowland, Aldo Pacchiano, Adrian Weller
2015	J	`jnl`	Real time clustering of time series using triangular potentials. CoRR Aldo Pacchiano, Oliver Williams
2012	J	`jnl`	Computational Approaches to Poisson Traces Associated to Finite Subgroups of S Exp. Math. Pavel Etingof, Sherry Gong, Aldo Pacchiano, Qingchun Ren, Travis Schedler