Schneider, Jeff

62 publications

TMLR 2025 Beyond Parameter Count: Implicit Bias in Soft Mixture of Experts Youngseog Chung, Dhruv Malik, Jeff Schneider, Yuanzhi Li, Aarti Singh

NeurIPS 2025 Efficient Bayesian Experiment Design with Equivariant Networks Conor Igoe, Tejus Gupta, Jeff Schneider

NeurIPS 2025 Improving Model-Based Reinforcement Learning by Converging to Flatter Minima Shrinivas Ramasubramanian, Benjamin Freed, Alexandre Capone, Jeff Schneider

ICML 2025 Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks Rohit Sonker, Alexandre Capone, Andrew Rothstein, Hiro Josep Farre Kaga, Egemen Kolemen, Jeff Schneider

NeurIPS 2025 Retrospective In-Context Learning for Temporal Credit Assignment with Large Language Models Wentse Chen, Jiayu Chen, Fahim Tajwar, Hao Zhu, Xintong Duan, Ruslan Salakhutdinov, Jeff Schneider

TMLR 2025 State Combinatorial Generalization in Decision Making with Conditional Diffusion Models Xintong Duan, Yutong He, Fahim Tajwar, Wentse Chen, Ruslan Salakhutdinov, Jeff Schneider

ICML 2025 Training a Generally Curious Agent Fahim Tajwar, Yiding Jiang, Abitha Thankaraj, Sumaita Sadia Rahman, J Zico Kolter, Jeff Schneider, Russ Salakhutdinov

ICLRW 2025 Training a Generally Curious Agent Fahim Tajwar, Yiding Jiang, Abitha Thankaraj, Sumaita Sadia Rahman, J Zico Kolter, Jeff Schneider, Ruslan Salakhutdinov

NeurIPS 2025 What Is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions Sang Keun Choe, Hwijeen Ahn, Juhan Bae, Kewen Zhao, Youngseog Chung, Adithya Pratapa, Willie Neiswanger, Emma Strubell, Teruko Mitamura, Jeff Schneider, Eduard Hovy, Roger Baker Grosse, Eric P. Xing

ICMLW 2024 Accelerated Online Reinforcement Learning Using Auxiliary Start State Distributions Aman Mehra, Alexandre Capone, Jeff Schneider

CVPR 2024 Diffusion-ES: Gradient-Free Planning with Diffusion for Autonomous and Instruction-Guided Driving Brian Yang, Huangyuan Su, Nikolaos Gkanatsios, Tsung-Wei Ke, Ayush Jain, Jeff Schneider, Katerina Fragkiadaki

NeurIPSW 2024 Fine-Tuning LLM Agents with Retrospective In-Context Online Learning Wentse Chen, Jiayu Chen, Fahim Tajwar, Hao Zhu, Xintong Duan, Russ Salakhutdinov, Jeff Schneider

CoRL 2024 Genetic Algorithm for Curriculum Design in Multi-Agent Reinforcement Learning Yeeho Song, Jeff Schneider

ICML 2024 Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar

ICLR 2024 Reasoning with Latent Diffusion in Offline Reinforcement Learning Siddarth Venkatraman, Shivesh Khaitan, Ravi Tej Akella, John Dolan, Jeff Schneider, Glen Berseth

ICML 2024 Sampling-Based Multi-Dimensional Recalibration Youngseog Chung, Ian Char, Jeff Schneider

NeurIPSW 2023 Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making Ian Char, Youngseog Chung, Rohan Shah, Willie Neiswanger, Jeff Schneider

NeurIPSW 2023 Decentralized and Asynchronous Multi-Agent Active Search and Tracking When Targets Outnumber Agents Arundhati Banerjee, Jeff Schneider

ICMLW 2023 Distributional Distance Classifiers for Goal-Conditioned Reinforcement Learning Ravi Tej Akella, Benjamin Eysenbach, Jeff Schneider, Ruslan Salakhutdinov

ICMLW 2023 Kernelized Offline Contextual Dueling Bandits Viraj Mehta, Ojash Neopane, Vikramjeet Das, Sen Lin, Jeff Schneider, Willie Neiswanger

ICML 2023 Learning Temporally AbstractWorld Models Without Online Experimentation Benjamin Freed, Siddarth Venkatraman, Guillaume Adrien Sartoretti, Jeff Schneider, Howie Choset

ICLR 2023 Near-Optimal Policy Identification in Active Reinforcement Learning Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic

L4DC 2023 Offline Model-Based Reinforcement Learning for Tokamak Control Ian Char, Joseph Abbate, Laszlo Bardoczi, Mark Boyer, Youngseog Chung, Rory Conlin, Keith Erickson, Viraj Mehta, Nathan Richner, Egemen Kolemen, Jeff Schneider

CoRL 2023 Stealthy Terrain-Aware Multi-Agent Active Search Nikhil Angad Bakshi, Jeff Schneider

NeurIPSW 2023 Towards LLMs as Operational Copilots for Fusion Reactors Viraj Mehta, Joseph Abbate, Allen Wang, Andrew Rothstein, Ian Char, Jeff Schneider, Egemen Kolemen, Cristina Rea, Darren Garnier

ICML 2022 Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning Adam R Villaflor, Zhe Huang, Swapnil Pande, John M Dolan, Jeff Schneider

ICLR 2022 An Experimental Design Perspective on Model-Based Reinforcement Learning Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger

WACV 2022 SBEVNet: End-to-End Deep Stereo Layout Estimation Divam Gupta, Wei Pu, Trenton Tabor, Jeff Schneider

UAI 2021 Decentralized Multi-Agent Active Search for Sparse Signals Ramina Ghods, Arundhati Banerjee, Jeff Schneider

AISTATS 2020 ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabas Poczos, Jeff Schneider, Eric Xing

ICLRW 2020 Neural Dynamical Systems Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D Boyer, Egemen Kolemen, Jeff Schneider

JMLR 2020 Tuning Hyperparameters Without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabas Poczos, Eric P. Xing

WACV 2020 Uncertainty-Aware Short-Term Motion Prediction of Traffic Actors for Autonomous Driving Nemanja Djuric, Vladan Radosavljevic, Henggang Cui, Thi Nguyen, Fang-Chieh Chou, Tsung-Han Lin, Nitin Singh, Jeff Schneider

ICML 2019 Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos

NeurIPS 2019 Offline Contextual Bayesian Optimization Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Oakleigh Nelson, Mark Boyer, Egemen Kolemen, Jeff Schneider

NeurIPS 2018 Neural Architecture Search with Bayesian Optimisation and Optimal Transport Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, Eric P Xing

AISTATS 2018 Parallelised Bayesian Optimisation via Thompson Sampling Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos

ICML 2018 Transformation Autoregressive Networks Junier Oliva, Avinava Dubey, Manzil Zaheer, Barnabas Poczos, Ruslan Salakhutdinov, Eric Xing, Jeff Schneider

ICML 2017 Equivariance Through Parameter-Sharing Siamak Ravanbakhsh, Jeff Schneider, Barnabás Póczos

ICML 2017 Multi-Fidelity Bayesian Optimisation with Continuous Approximations Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider, Barnabás Póczos

IJCAI 2017 Scaling Active Search Using Linear Similarity Functions Sibi Venkatesan, James Kyle Miller, Jeff Schneider, Artur Dubrawski

ICML 2017 The Statistical Recurrent Unit Junier B. Oliva, Barnabás Póczos, Jeff Schneider

ICML 2016 Estimating Cosmological Parameters from the Dark Matter Distribution Siamak Ravanbakhsh, Junier Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff Schneider, Barnabas Poczos

NeurIPS 2016 Gaussian Process Bandit Optimisation with Multi-Fidelity Evaluations Kirthevasan Kandasamy, Gautam Dasarathy, Junier B Oliva, Jeff Schneider, Barnabas Poczos

NeurIPS 2016 The Multi-Fidelity Multi-Armed Bandit Kirthevasan Kandasamy, Gautam Dasarathy, Barnabas Poczos, Jeff Schneider

ICML 2015 Finding Galaxies in the Shadows of Quasars with Gaussian Processes Roman Garnett, Shirley Ho, Jeff Schneider

ICML 2015 High Dimensional Bayesian Optimisation and Bandits via Additive Models Kirthevasan Kandasamy, Jeff Schneider, Barnabas Poczos

ICML 2014 Active Transfer Learning Under Model Shift Xuezhi Wang, Tzu-Kuo Huang, Jeff Schneider

NeurIPS 2014 Flexible Transfer Learning Under Support and Model Shift Xuezhi Wang, Jeff Schneider

ICML 2013 Distribution to Distribution Regression Junier Oliva, Barnabas Poczos, Jeff Schneider

ICML 2013 Expensive Function Optimization with Stochastic Binary Outcomes Matthew Tesch, Jeff Schneider, Howie Choset

NeurIPS 2013 Learning Hidden Markov Models from Non-Sequence Data via Tensor Decomposition Tzu-Kuo Huang, Jeff Schneider

ICML 2013 Spectral Learning of Hidden Markov Models from Dynamic and Static Data Tzu-Kuo Huang, Jeff Schneider

NeurIPS 2013 Σ-Optimality for Active Learning on Gaussian Random Fields Yifei Ma, Roman Garnett, Jeff Schneider

AISTATS 2012 A Composite Likelihood View for Multi-Label Classification Yi Zhang, Jeff Schneider

AISTATS 2012 Nonparametric Estimation of Conditional Information and Divergences Barnabas Poczos, Jeff Schneider

AISTATS 2011 Hierarchical Probabilistic Models for Group Anomaly Detection Liang Xiong, Barnabás Póczos, Jeff Schneider, Andrew Connolly, Jake VanderPlas

AISTATS 2011 Multi-Label Output Codes Using Canonical Correlation Analysis Yi Zhang, Jeff Schneider

AISTATS 2011 On the Estimation of $\alpha$-Divergences Barnabas Poczos, Jeff Schneider

AISTATS 2010 Learning Nonlinear Dynamic Models from Non-Sequenced Data Tzu–Kuo Huang, Le Song, Jeff Schneider

NeurIPS 2005 Active Learning for Identifying Function Threshold Boundaries Brent Bryan, Robert C. Nichol, Christopher R Genovese, Jeff Schneider, Christopher J. Miller, Larry Wasserman

AISTATS 1997 Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators Artur Dubrawski, Jeff Schneider