Jeff Schneider

Publication Activity (10 Years)

Years Active: 1997-2024
Publications (10 Years): 31

Top Topics

Reinforcement Learning

Top Venues

Publications

Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data. CoRR (2024)
Arundhati Banerjee, Jeff Schneider
Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents. CoRR (2024)
Siddarth Venkatraman, Shivesh Khaitan, Ravi Tej Akella, John Dolan, Jeff Schneider, Glen Berseth
Reasoning with Latent Diffusion in Offline Reinforcement Learning. ICLR (2024)
Adam Villaflor, Brian Yang, Huangyuan Su, Katerina Fragkiadaki, John M. Dolan, Jeff Schneider
Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Driving. CoRR (2024)
Viraj Mehta, Ojash Neopane, Vikramjeet Das, Sen Lin, Jeff Schneider, Willie Neiswanger
Kernelized Offline Contextual Dueling Bandits. CoRR (2023)
Arundhati Banerjee, Ramina Ghods, Jeff Schneider
Cost-Awareness in Multi-Agent Active Search. ECAI (2023)
Nikhil Angad Bakshi, Tejus Gupta, Ramina Ghods, Jeff Schneider
GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search. CoRR (2023)
Nikhil Angad Bakshi, Jeff Schneider
Stealthy Terrain-Aware Multi-Agent Active Search. CoRL (2023)
Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic
Near-optimal Policy Identification in Active Reinforcement Learning. ICLR (2023)
Benjamin Freed, Siddarth Venkatraman, Guillaume Adrien Sartoretti, Jeff Schneider, Howie Choset
Learning Temporally AbstractWorld Models without Online Experimentation. ICML (2023)
Vikram Duvvur, Aashay Mehta, Edward Sun, Bo Wu, Ken Yew Chan, Jeff Schneider
Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading. CoRR (2023)
Nikhil Angad Bakshi, Jeff Schneider
Stealthy Terrain-Aware Multi-Agent Active Search. CoRR (2023)
Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark D. Boyer, Stefano Ermon, Jeff Schneider, Willie Neiswanger
Exploration via Planning for Information about the Optimal Trajectory. NeurIPS (2022)
Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic
Near-optimal Policy Identification in Active Reinforcement Learning. CoRR (2022)
Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. NeurIPS (2021)
Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger
An Experimental Design Perspective on Model-Based Reinforcement Learning. CoRR (2021)
Youngseog Chung, Ian Char, Han Guo, Jeff Schneider, Willie Neiswanger
Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification. CoRR (2021)
Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. AISTATS (2020)
Youngseog Chung, Ian Char, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff Schneider
Offline Contextual Bayesian Optimization for Nuclear Fusion. CoRR (2020)
Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff Schneider
Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction. CoRR (2020)
Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. CoRR (2020)
Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. J. Mach. Learn. Res. 21 (2020)
Willie Neiswanger, Kirthevasan Kandasamy, Barnabás Póczos, Jeff Schneider, Eric P. Xing
ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization. CoRR (2019)
Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. CoRR (2019)
Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. CoRR (2019)
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos
Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments. ICML (2019)
Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing
Neural Architecture Search with Bayesian Optimisation and Optimal Transport. NeurIPS (2018)
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming. CoRR (2018)
Junier B. Oliva, Avinava Dubey, Manzil Zaheer, Barnabás Póczos, Ruslan Salakhutdinov, Eric P. Xing, Jeff Schneider
Transformation Autoregressive Networks. ICML (2018)
Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing
Neural Architecture Search with Bayesian Optimisation and Optimal Transport. CoRR (2018)
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos
Parallelised Bayesian Optimisation via Thompson Sampling. AISTATS (2018)
Artur Dubrawski, Jeff Schneider
Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators. AISTATS (1997)