Jeff Schneider

73 papers A* 20A 7Misc 1Journal 43Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Beyond Parameter Count: Implicit Bias in Soft Mixture of Experts. Trans. Mach. Learn. Res. Youngseog Chung, Dhruv Malik, Jeff Schneider, Yuanzhi Li, Aarti Singh
2025	A*	`conf`	Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks. ICML Rohit Sonker, Alexandre Capone, Andrew Rothstein, Hiro Josep Farre Kaga, Egemen Kolemen, Jeff Schneider
2025	A*	`conf`	Training a Generally Curious Agent. ICML Fahim Tajwar, Yiding Jiang, Abitha Thankaraj, Sumaita Sadia Rahman, J. Zico Kolter, Jeff Schneider, Russ Salakhutdinov
2025	J	`jnl`	Training a Generally Curious Agent. CoRR Fahim Tajwar, Yiding Jiang, Abitha Thankaraj, Sumaita Sadia Rahman, J. Zico Kolter, Jeff Schneider, Ruslan Salakhutdinov
2024	J	`jnl`	Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization. RLJ Aditya Kapoor, Benjamin Freed, Jeff Schneider, Howie Choset
2024	—	`conf`	Beyond Parameter Count: Implicit Bias in Soft Mixture of Experts. ENLSP Youngseog Chung, Dhruv Malik, Jeff Schneider, Yuanzhi Li, Aarti Singh
2024	J	`jnl`	Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents. CoRR Arundhati Banerjee, Jeff Schneider
2024	J	`jnl`	Decentralized Uncertainty-Aware Active Search with a Team of Aerial Robots. CoRR Wennie Tabib, John Stecklein, Caleb McDowell, Kshitij Goel, Felix Jonathan, Abhishek Rathod, Meghan Kokoski, Edsel Burkholder, Brian Wallace, Luis Ernesto Navarro-Serment, Nikhil Angad Bakshi, Tejus Gupta, Norman Papernick, David Guttendorf, Erik E. Kahn, Jessica Kasemer, Jesse Holdaway, Jeff Schneider
2024	J	`jnl`	Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following. CoRR Brian Yang, Huangyuan Su, Nikolaos Gkanatsios, Tsung-Wei Ke, Ayush Jain, Jeff Schneider, Katerina Fragkiadaki
2024	A*	`conf`	Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data. ICML Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar
2024	J	`jnl`	Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data. CoRR Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar
2024	A*	`conf`	Reasoning with Latent Diffusion in Offline Reinforcement Learning. ICLR Siddarth Venkatraman, Shivesh Khaitan, Ravi Tej Akella, John M. Dolan, Jeff Schneider, Glen Berseth
2024	A*	`conf`	Sampling-based Multi-dimensional Recalibration. ICML Youngseog Chung, Ian Char, Jeff Schneider
2024	J	`jnl`	Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Driving. CoRR Adam Villaflor, Brian Yang, Huangyuan Su, Katerina Fragkiadaki, John M. Dolan, Jeff Schneider
2024	J	`jnl`	Unifying Model-Based and Model-Free Reinforcement Learning with Equivalent Policy Sets. RLJ Benjamin Freed, Thomas Wei, Roberto Calandra, Jeff Schneider, Howie Choset
2023	A	`conf`	Cost-Awareness in Multi-Agent Active Search. ECAI Arundhati Banerjee, Ramina Ghods, Jeff Schneider
2023	J	`jnl`	Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading. CoRR Vikram Duvvur, Aashay Mehta, Edward Sun, Bo Wu, Ken Yew Chan, Jeff Schneider
2023	J	`jnl`	Enhancing Visual Domain Adaptation with Source Preparation. CoRR Anirudha Ramesh, Anurag Ghosh, Christoph Mertz, Jeff Schneider
2023	J	`jnl`	GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search. CoRR Nikhil Angad Bakshi, Tejus Gupta, Ramina Ghods, Jeff Schneider
2023	J	`jnl`	Kernelized Offline Contextual Dueling Bandits. CoRR Viraj Mehta, Ojash Neopane, Vikramjeet Das, Sen Lin, Jeff Schneider, Willie Neiswanger
2023	A*	`conf`	Learning Temporally AbstractWorld Models without Online Experimentation. ICML Benjamin Freed, Siddarth Venkatraman, Guillaume Adrien Sartoretti, Jeff Schneider, Howie Choset
2023	A*	`conf`	Near-optimal Policy Identification in Active Reinforcement Learning. ICLR Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic
2023	A*	`conf`	PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks. NeurIPS Ian Char, Jeff Schneider
2023	Misc	`conf`	Stealthy Terrain-Aware Multi-Agent Active Search. CoRL Nikhil Angad Bakshi, Jeff Schneider
2023	J	`jnl`	Stealthy Terrain-Aware Multi-Agent Active Search. CoRR Nikhil Angad Bakshi, Jeff Schneider
2022	A*	`conf`	Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning. ICML Adam R. Villaflor, Zhe Huang, Swapnil Pande, John M. Dolan, Jeff Schneider
2022	J	`jnl`	Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning. CoRR Adam Villaflor, Zhe Huang, Swapnil Pande, John M. Dolan, Jeff Schneider
2022	A*	`conf`	An Experimental Design Perspective on Model-Based Reinforcement Learning. ICLR Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger
2022	J	`jnl`	BATS: Best Action Trajectory Stitching. CoRR Ian Char, Viraj Mehta, Adam Villaflor, John M. Dolan, Jeff Schneider
2022	J	`jnl`	Cost Aware Asynchronous Multi-Agent Active Search. CoRR Arundhati Banerjee, Ramina Ghods, Jeff Schneider
2022	A*	`conf`	Exploration via Planning for Information about the Optimal Trajectory. NeurIPS Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark D. Boyer, Stefano Ermon, Jeff Schneider, Willie Neiswanger
2022	J	`jnl`	Exploration via Planning for Information about the Optimal Trajectory. CoRR Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark D. Boyer, Stefano Ermon, Jeff Schneider, Willie Neiswanger
2022	J	`jnl`	How Useful are Gradients for OOD Detection Really? CoRR Conor Igoe, Youngseog Chung, Ian Char, Jeff Schneider
2022	J	`jnl`	Multi-Agent Active Search using Detection and Location Uncertainty. CoRR Arundhati Banerjee, Ramina Ghods, Jeff Schneider
2022	J	`jnl`	Multi-Agent Active Search: A Reinforcement Learning Approach. IEEE Robotics Autom. Lett. Conor Igoe, Ramina Ghods, Jeff Schneider
2022	J	`jnl`	Near-optimal Policy Identification in Active Reinforcement Learning. CoRR Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic
2022	A*	`conf`	Robust Reinforcement Learning via Genetic Curriculum. ICRA Yeeho Song, Jeff Schneider
2022	J	`jnl`	Robust Reinforcement Learning via Genetic Curriculum. CoRR Yeeho Song, Jeff Schneider
2022	A	`conf`	SBEVNet: End-to-End Deep Stereo Layout Estimation. WACV Divam Gupta, Wei Pu, Trenton Tabor, Jeff Schneider
2022	J	`jnl`	UGV-UAV Object Geolocation in Unstructured Environments. CoRR David Guttendorf, D. W. Wilson Hamilton, Anne Harris Heckman, Herman Herman, Felix Jonathan, Prasanna Kannappan, Nicholas Mireles, Luis E. Navarro-Serment, Jean Oh, Wei Pu, Rohan Saxena, Jeff Schneider, Matt Schnur, Carter Tiernan, Trenton Tabor
2021	J	`jnl`	Affordance-based Reinforcement Learning for Urban Driving. CoRR Tanmay Agarwal, Hitesh Arora, Jeff Schneider
2021	J	`jnl`	An Experimental Design Perspective on Model-Based Reinforcement Learning. CoRR Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger
2021	A*	`conf`	Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning ICRA Zhiqian Qiao, Jeff Schneider, John M. Dolan
2021	A*	`conf`	Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. NeurIPS Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider
2021	A	`conf`	Decentralized multi-agent active search for sparse signals. UAI Ramina Ghods, Arundhati Banerjee, Jeff Schneider
2021	—	`conf`	Learning Urban Driving Policies using Deep Reinforcement Learning. ITSC Tanmay Agarwal, Hitesh Arora, Jeff Schneider
2021	A*	`conf`	Multi-Agent Active Search using Realistic Depth-Aware Noise Model. ICRA Ramina Ghods, William J. Durkin, Jeff Schneider
2021	J	`jnl`	SBEVNet: End-to-End Deep Stereo Layout Estimation. CoRR Divam Gupta, Wei Pu, Trenton Tabor, Jeff Schneider
2021	J	`jnl`	Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification. CoRR Youngseog Chung, Ian Char, Han Guo, Jeff Schneider, Willie Neiswanger
2020	J	`jnl`	Asynchronous Multi Agent Active Search. CoRR Ramina Ghods, Arundhati Banerjee, Jeff Schneider
2020	J	`jnl`	Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning. CoRR Zhiqian Qiao, Jeff Schneider, John M. Dolan
2020	J	`jnl`	Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. CoRR Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider
2020	A	`conf`	ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. AISTATS Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing
2020	A	`conf`	Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning. IROS Zhiqian Qiao, Zachariah Tyree, Priyantha Mudalige, Jeff Schneider, John M. Dolan
2020	A*	`conf`	Human Driver Behavior Prediction based on UrbanFlow ICRA Zhiqian Qiao, Jing Zhao, Jin Zhu, Zachariah Tyree, Priyantha Mudalige, Jeff Schneider, John M. Dolan
2020	J	`jnl`	Multi-Agent Active Search using Realistic Depth-Aware Noise Model. CoRR Ramina Ghods, William J. Durkin, Jeff Schneider
2020	J	`jnl`	Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction. CoRR Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff Schneider
2020	J	`jnl`	Offline Contextual Bayesian Optimization for Nuclear Fusion. CoRR Youngseog Chung, Ian Char, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff Schneider
2020	J	`jnl`	Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. J. Mach. Learn. Res. Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing
2019	J	`jnl`	ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. CoRR Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing
2019	J	`jnl`	Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning. CoRR Zhiqian Qiao, Zachariah Tyree, Priyantha Mudalige, Jeff Schneider, John M. Dolan
2019	J	`jnl`	Human Driver Behavior Prediction based on UrbanFlow. CoRR Zhiqian Qiao, Jing Zhao, Zachariah Tyree, Priyantha Mudalige, Jeff Schneider, John M. Dolan
2019	A*	`conf`	Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments. ICML Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos
2019	J	`jnl`	ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization. CoRR Willie Neiswanger, Kirthevasan Kandasamy, Barnabás Póczos, Jeff Schneider, Eric P. Xing
2019	J	`jnl`	Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. CoRR Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing
2018	J	`jnl`	Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming. CoRR Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos
2018	A*	`conf`	Neural Architecture Search with Bayesian Optimisation and Optimal Transport. NeurIPS Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing
2018	J	`jnl`	Neural Architecture Search with Bayesian Optimisation and Optimal Transport. CoRR Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing
2018	A	`conf`	Parallelised Bayesian Optimisation via Thompson Sampling. AISTATS Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos
2018	A*	`conf`	Transformation Autoregressive Networks. ICML Junier B. Oliva, Avinava Dubey, Manzil Zaheer, Barnabás Póczos, Ruslan Salakhutdinov, Eric P. Xing, Jeff Schneider
2017	A*	`conf`	Scaling Active Search using Linear Similarity Functions. IJCAI Sibi Venkatesan, James Kyle Miller, Jeff Schneider, Artur Dubrawski
2017	J	`jnl`	Scaling Active Search using Linear Similarity Functions. CoRR Sibi Venkatesan, James Kyle Miller, Jeff Schneider, Artur Dubrawski
1997	A	`conf`	Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators. AISTATS Artur Dubrawski, Jeff Schneider