Robot Skill Learning, Basic Functions, and Control Regimes. - Jeff G. Schneider; Christopher M. Brown (ICRA (1), 1993)

High Dimension Action Spaces in Robot Skill Learning. - Jeff G. Schneider (AAAI, 1994)

Efficient search for robot skill learning: simulation and reality. - Jeff G. Schneider; Roger F. Gans (IROS, 1994)

Cooperative coaching in robot learning. - Jeff G. Schneider; Christopher M. Brown (IROS (3), 1995)

Memory-based Stochastic Optimization. - Andrew W. Moore 001; Jeff G. Schneider (NIPS, 1995)

Exploiting Model Uncertainty Estimates for Safe Dynamic Control Learning. - Jeff G. Schneider (NIPS, 1996)

Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators. - Artur Dubrawski; Jeff Schneider 001 (AISTATS, 1997)

Efficient Locally Weighted Polynomial Regression Predictions. - Andrew W. Moore 001; Jeff G. Schneider; Kan Deng (ICML, 1997)

Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions. - Andrew W. Moore 001; Jeff G. Schneider; Justin A. Boyan; Mary S. Lee (ICML, 1998)

Value Function Based Production Scheduling. - Jeff G. Schneider; Justin A. Boyan; Andrew W. Moore 001 (ICML, 1998)

Distributed Value Functions. - Jeff G. Schneider; Weng-Keen Wong; Andrew W. Moore 001; Martin A. Riedmiller (ICML, 1999)

3-D Deformable Registration of Medical Images Using a Statistical Atlas. - Mei Chen; Takeo Kanade; Dean Pomerleau; Jeff G. Schneider (MICCAI, 1999)

Reinforcement Learning for Cooperating and Communicating Reactive Agents in Electrical Power Grids. - Martin A. Riedmiller; Andrew W. Moore 001; Jeff G. Schneider (Balancing Reactivity and Social Deliberation in Multi-Agent Systems, 2000)

Autonomous Helicopter Control using Reinforcement Learning Policy Search Methods. - J. Andrew Bagnell; Jeff G. Schneider (ICRA, 2001)

Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures. - Yanxi Liu 001; Frank Dellaert; William E. Rothfus; Andrew W. Moore 001; Jeff G. Schneider; Takeo Kanade (MICCAI, 2001)

Stochastic Link and Group Detection. - Jeremy Kubica; Andrew W. Moore 001; Jeff G. Schneider; Yiming Yang (AAAI/IAAI, 2002)

Real-valued All-Dimensions Search: Low-overhead Rapid Searching over Subsets of Attributes. - Andrew W. Moore 001; Jeff G. Schneider (UAI, 2002)

Tractable Group Detection on Large Link Data Sets. - Jeremy Kubica; Andrew W. Moore 001; Jeff G. Schneider (ICDM, 2003)

Finding Underlying Connections: A Fast Graph-Based Method for Link Analysis and Collaboration Queries. - Jeremy Kubica; Andrew W. Moore 001; David Cohn; Jeff G. Schneider (ICML, 2003)

Covariant Policy Search. - J. Andrew Bagnell; Jeff G. Schneider (IJCAI, 2003)

Policy Search by Dynamic Programming. - J. Andrew Bagnell; Sham M. Kakade; Andrew Y. Ng; Jeff G. Schneider (NIPS, 2003)

Approximate Solutions for Partially Observable Stochastic Games with Common Payoffs. - Rosemary Emery-Montemerlo; Geoffrey J. Gordon; Jeff G. Schneider; Sebastian Thrun (AAMAS, 2004)

Belief state approaches to signaling alarms in surveillance systems. - Kaustav Das; Andrew W. Moore 001; Jeff G. Schneider (KDD, 2004)

Automatic Construction of Active Appearance Models as an Image Coding Problem. - Simon Baker; Iain A. Matthews; Jeff G. Schneider (IEEE Trans. Pattern Anal. Mach. Intell., 2004)

Game Theoretic Control for Robot Teams. - Rosemary Emery-Montemerlo; Geoffrey J. Gordon; Jeff G. Schneider; Sebastian Thrun (ICRA, 2005)

Learning Opportunity Costs in Multi-Robot Market Based Planners. - Jeff G. Schneider; David Apfelbaum; Drew Bagnell; Reid G. Simmons (ICRA, 2005)

Active Learning For Identifying Function Threshold Boundaries. - Brent Bryan; Jeff G. Schneider; Robert Nichol; Christopher J. Miller; Christopher R. Genovese; Larry A. Wasserman (NIPS, 2005)

A Study into Detection of Bio-Events in Multiple Streams of Surveillance Data. - Josep Roure; Artur Dubrawski; Jeff G. Schneider (BioSurveillance, 2007)

Efficiently computing minimax expected-size confidence regions. - Brent Bryan; H. Brendan McMahan; Chad M. Schafer; Jeff G. Schneider (ICML, 2007)

Detecting anomalous records in categorical datasets. - Kaustav Das; Jeff G. Schneider (KDD, 2007)

Learning Detectors of Events in Multivariate Time Series. - Josep Roure; Artur Dubrawski; Jeff G. Schneider (AMIA, 2008)

Actively learning level-sets of composite functions. - Brent Bryan; Jeff G. Schneider (ICML, 2008)

Anomaly pattern detection in categorical datasets. - Kaustav Das; Jeff G. Schneider; Daniel B. Neill (KDD, 2008)

Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text. - Yi Zhang 010; Jeff G. Schneider; Artur Dubrawski (NIPS, 2008)

Learning linear dynamical systems without sequence information. - Tzu-Kuo Huang; Jeff G. Schneider (ICML, 2009)

Efficiently learning the accuracy of labeling sources for selective sampling. - Pinar Donmez; Jaime G. Carbonell; Jeff G. Schneider (KDD, 2009)

Projection Penalties: Dimension Reduction without Loss. - Yi Zhang 010; Jeff G. Schneider (ICML, 2010)

Learning Multiple Tasks with a Sparse Matrix-Normal Penalty. - Yi Zhang 010; Jeff G. Schneider (NIPS, 2010)

A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy. - Pinar Donmez; Jaime G. Carbonell; Jeff G. Schneider (SDM, 2010)

Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization. - Liang Xiong; Xi Chen 010; Tzu-Kuo Huang; Jeff G. Schneider; Jaime G. Carbonell (SDM, 2010)

Learning Compressible Models. - Yi Zhang 010; Jeff G. Schneider; Artur Dubrawski (SDM, 2010)

Learning Nonlinear Dynamic Models from Non-sequenced Data. - Tzu-Kuo Huang; Le Song; Jeff G. Schneider (AISTATS, 2010)

Nonparametric divergence estimators for independent subspace analysis. - Barnabás Póczos; Zoltán Szabó 001; Jeff G. Schneider (EUSIPCO, 2011)

Direct Robust Matrix Factorizatoin for Anomaly Detection. - Liang Xiong; Xi Chen 010; Jeff G. Schneider (ICDM, 2011)

Adapting control policies for expensive systems to changing environments. - Matthew Tesch; Jeff G. Schneider; Howie Choset (IROS, 2011)

Using response surfaces and expected improvement to optimize snake robot gait parameters. - Matthew Tesch; Jeff G. Schneider; Howie Choset (IROS, 2011)

Learning Auto-regressive Models from Sequence and Non-sequence Data. - Tzu-Kuo Huang; Jeff G. Schneider (NIPS, 2011)

Group Anomaly Detection using Flexible Genre Models. - Liang Xiong; Barnabás Póczos; Jeff G. Schneider (NIPS, 2011)

Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions. - Barnabás Póczos; Liang Xiong; Jeff G. Schneider (UAI, 2011)

On the Estimation of alpha-Divergences. - Barnabás Póczos; Jeff G. Schneider (AISTATS, 2011)

Hierarchical Probabilistic Models for Group Anomaly Detection. - Liang Xiong; Barnabás Póczos; Jeff G. Schneider; Andrew J. Connolly; Jake VanderPlas (AISTATS, 2011)

Multi-Label Output Codes using Canonical Correlation Analysis. - Yi Zhang 010; Jeff G. Schneider (AISTATS, 2011)

Nonparametric kernel estimators for image classification. - Barnabás Póczos; Liang Xiong; Danica J. Sutherland; Jeff G. Schneider (CVPR, 2012)

Bayesian Optimal Active Search and Surveying. - Roman Garnett; Yamuna Krishnamurthy; Xuehan Xiong; Jeff G. Schneider; Richard P. Mann (ICML, 2012)

Copula-based Kernel Dependency Measures. - Barnabás Póczos; Zoubin Ghahramani; Jeff G. Schneider (ICML, 2012)

Maximum Margin Output Coding. - Yi Zhang 010; Jeff G. Schneider (ICML, 2012)

Learning Bi-clustered Vector Autoregressive Models. - Tzu-Kuo Huang; Jeff G. Schneider (ECML/PKDD (2), 2012)

Protein subcellular location pattern classification in cellular images using latent discriminative models. - Jieyue Li; Liang Xiong; Jeff G. Schneider; Robert F. Murphy (Bioinform., 2012)

Support Distribution Machines - Barnabás Póczos; Liang Xiong; Danica J. Sutherland; Jeff G. Schneider (CoRR, 2012)

Submodularity in Batch Active Learning and Survey Problems on Gaussian Random Fields - Yifei Ma; Roman Garnett; Jeff G. Schneider (CoRR, 2012)

Nonparametric Estimation of Conditional Information and Divergences. - Barnabás Póczos; Jeff G. Schneider (AISTATS, 2012)

A Composite Likelihood View for Multi-Label Classification. - Yi Zhang 010; Jeff G. Schneider (AISTATS, 2012)

Efficient Learning on Point Sets. - Liang Xiong; Barnabás Póczos; Jeff G. Schneider (ICDM, 2013)

Spectral Learning of Hidden Markov Models from Dynamic and Static Data. - Tzu-Kuo Huang; Jeff G. Schneider (ICML (3), 2013)

Distribution to Distribution Regression. - Junier B. Oliva; Barnabás Póczos; Jeff G. Schneider (ICML (3), 2013)

Expensive Function Optimization with Stochastic Binary Outcomes. - Matthew Tesch; Jeff G. Schneider; Howie Choset (ICML (3), 2013)

Expensive multiobjective optimization for robotics. - Matthew Tesch; Jeff G. Schneider; Howie Choset (ICRA, 2013)

Active learning and search on low-rank matrices. - Danica J. Sutherland; Barnabás Póczos; Jeff G. Schneider (KDD, 2013)

Active search on graphs. - Xuezhi Wang 002; Roman Garnett; Jeff G. Schneider (KDD, 2013)

Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition. - Tzu-Kuo Huang; Jeff G. Schneider (NIPS, 2013)

Σ-Optimality for Active Learning on Gaussian Random Fields. - Yifei Ma; Roman Garnett; Jeff G. Schneider (NIPS, 2013)

Active Area Search via Bayesian Quadrature. - Yifei Ma; Roman Garnett; Jeff G. Schneider (AISTATS, 2014)

Fast Distribution To Real Regression. - Junier B. Oliva; Willie Neiswanger; Barnabás Póczos; Jeff G. Schneider; Eric P. Xing (AISTATS, 2014)

FuSSO: Functional Shrinkage and Selection Operator. - Junier B. Oliva; Barnabás Póczos; Timothy D. Verstynen; Aarti Singh; Jeff G. Schneider; Fang-Cheng Yeh; Wen-Yih Isaac Tseng (AISTATS, 2014)

Active Transfer Learning under Model Shift. - Xuezhi Wang 002; Tzu-Kuo Huang; Jeff G. Schneider (ICML, 2014)

Systematic Labeling Bias: De-biasing Where Everyone is Wrong. - Guillermo F. Cabrera; Christopher J. Miller; Jeff G. Schneider (ICPR, 2014)

SLIC: Statistical learning in chip. - Ronald D. Blanton; Xin Li 001; Ken Mai; Diana Marculescu; Radu Marculescu; Jeyanandh Paramesh; Jeff G. Schneider; Donald E. Thomas (ISIC, 2014)

Flexible Transfer Learning under Support and Model Shift. - Xuezhi Wang 002; Jeff G. Schneider (NIPS, 2014)

Learning from Point Sets with Observational Bias. - Liang Xiong; Jeff G. Schneider (UAI, 2014)

Fast Function to Function Regression. - Junier B. Oliva; Willie Neiswanger; Barnabás Póczos; Eric P. Xing; Jeff G. Schneider (CoRR, 2014)

Active Pointillistic Pattern Search. - Yifei Ma; Danica J. Sutherland; Roman Garnett; Jeff G. Schneider (AISTATS, 2015)

Fast Function to Function Regression. - Junier B. Oliva; Willie Neiswanger; Barnabás Póczos; Eric P. Xing; Hy Trac; Shirley Ho; Jeff G. Schneider (AISTATS, 2015)

Statistical Learning in Chip (SLIC). - Ronald D. Blanton; Xin Li 001; Ken Mai; Diana Marculescu; Radu Marculescu; Jeyanandh Paramesh; Jeff G. Schneider; Donald E. Thomas (ICCAD, 2015)

Finding Galaxies in the Shadows of Quasars with Gaussian Processes. - Roman Garnett; Shirley Ho; Jeff G. Schneider (ICML, 2015)

High Dimensional Bayesian Optimisation and Bandits via Additive Models. - Kirthevasan Kandasamy; Jeff G. Schneider; Barnabás Póczos (ICML, 2015)

Bayesian Active Learning for Posterior Estimation - IJCAI-15 Distinguished Paper. - Kirthevasan Kandasamy; Jeff G. Schneider; Barnabás Póczos (IJCAI, 2015)

Active Search and Bandits on Graphs using Sigma-Optimality. - Yifei Ma; Tzu-Kuo Huang; Jeff G. Schneider (UAI, 2015)

On the Error of Random Fourier Features. - Danica J. Sutherland; Jeff G. Schneider (UAI, 2015)

Generalization Bounds for Transfer Learning under Model Shift. - Xuezhi Wang 002; Jeff G. Schneider (UAI, 2015)

Deep Mean Maps. - Junier B. Oliva; Danica J. Sutherland; Barnabás Póczos; Jeff G. Schneider (CoRR, 2015)

Linear-Time Learning on Distributions with Approximate Kernel Embeddings. - Danica J. Sutherland; Junier B. Oliva; Barnabás Póczos; Jeff G. Schneider (AAAI, 2016)

High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models. - Chun-Liang Li; Kirthevasan Kandasamy; Barnabás Póczos; Jeff G. Schneider (AISTATS, 2016)

Bayesian Nonparametric Kernel-Learning. - Junier B. Oliva; Avinava Dubey; Andrew Gordon Wilson; Barnabás Póczos; Jeff G. Schneider; Eric P. Xing (AISTATS, 2016)

Stochastic Neural Networks with Monotonic Activation Functions. - Siamak Ravanbakhsh; Barnabás Póczos; Jeff G. Schneider; Dale Schuurmans; Russell Greiner (AISTATS, 2016)

Estimating Cosmological Parameters from the Dark Matter Distribution. - Siamak Ravanbakhsh; Junier B. Oliva; Sebastian Fromenteau; Layne Price; Shirley Ho; Jeff G. Schneider; Barnabás Póczos (ICML, 2016)

Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems. - Xuezhi Wang 002; Junier B. Oliva; Jeff G. Schneider; Barnabás Póczos (IJCAI, 2016)

Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations. - Kirthevasan Kandasamy; Gautam Dasarathy; Junier B. Oliva; Jeff G. Schneider; Barnabás Póczos (NIPS, 2016)

The Multi-fidelity Multi-armed Bandit. - Kirthevasan Kandasamy; Gautam Dasarathy; Barnabás Póczos; Jeff G. Schneider (NIPS, 2016)

Active Search for Sparse Signals with Region Sensing. - Yifei Ma; Roman Garnett; Jeff G. Schneider (AAAI, 2017)

Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. - Siamak Ravanbakhsh; François Lanusse; Rachel Mandelbaum; Jeff G. Schneider; Barnabás Póczos (AAAI, 2017)

Active Optimization and Self Driving Cars. - Jeff G. Schneider (AAMAS, 2017)

Deep Learning with Sets and Point Clouds. - Siamak Ravanbakhsh; Jeff G. Schneider; Barnabás Póczos (ICLR (Workshop), 2017)

Multi-fidelity Bayesian Optimisation with Continuous Approximations. - Kirthevasan Kandasamy; Gautam Dasarathy; Jeff G. Schneider; Barnabás Póczos (ICML, 2017)

The Statistical Recurrent Unit. - Junier B. Oliva; Barnabás Póczos; Jeff G. Schneider (ICML, 2017)

Equivariance Through Parameter-Sharing. - Siamak Ravanbakhsh; Jeff G. Schneider; Barnabás Póczos (ICML, 2017)

Scaling Active Search using Linear Similarity Functions. - Sibi Venkatesan; James Kyle Miller; Jeff Schneider 001; Artur Dubrawski (IJCAI, 2017)

Query efficient posterior estimation in scientific experiments via Bayesian active learning. - Kirthevasan Kandasamy; Jeff G. Schneider; Barnabás Póczos (Artif. Intell., 2017)

Asynchronous Parallel Bayesian Optimisation via Thompson Sampling. - Kirthevasan Kandasamy; Akshay Krishnamurthy; Jeff G. Schneider; Barnabás Póczos (CoRR, 2017)

Recurrent Estimation of Distributions. - Junier B. Oliva; Kumar Avinava Dubey; Barnabás Póczos; Eric P. Xing; Jeff G. Schneider (CoRR, 2017)

Parallelised Bayesian Optimisation via Thompson Sampling. - Kirthevasan Kandasamy; Akshay Krishnamurthy; Jeff Schneider 001; Barnabás Póczos (AISTATS, 2018)

Transformation Autoregressive Networks. - Junier B. Oliva; Avinava Dubey; Manzil Zaheer; Barnabás Póczos; Ruslan Salakhutdinov; Eric P. Xing; Jeff Schneider 001 (ICML, 2018)

Neural Architecture Search with Bayesian Optimisation and Optimal Transport. - Kirthevasan Kandasamy; Willie Neiswanger; Jeff Schneider 001; Barnabás Póczos; Eric P. Xing (NeurIPS, 2018)

Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming. - Kirthevasan Kandasamy; Willie Neiswanger; Reed Zhang; Akshay Krishnamurthy; Jeff Schneider 001; Barnabás Póczos (CoRR, 2018)

Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments. - Kirthevasan Kandasamy; Willie Neiswanger; Reed Zhang; Akshay Krishnamurthy; Jeff Schneider 001; Barnabás Póczos (ICML, 2019)

ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization. - Willie Neiswanger; Kirthevasan Kandasamy; Barnabás Póczos; Jeff Schneider 001; Eric P. Xing (CoRR, 2019)

Human Driver Behavior Prediction based on UrbanFlow. - Zhiqian Qiao; Jing Zhao; Zachariah Tyree; Priyantha Mudalige; Jeff Schneider 001; John M. Dolan (CoRR, 2019)

Multi-fidelity Gaussian Process Bandit Optimisation. - Kirthevasan Kandasamy; Gautam Dasarathy; Junier B. Oliva; Jeff G. Schneider; Barnabás Póczos (J. Artif. Intell. Res., 2019)

ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. - Ksenia Korovina; Sailun Xu; Kirthevasan Kandasamy; Willie Neiswanger; Barnabás Póczos; Jeff Schneider 001; Eric P. Xing (AISTATS, 2020)

Human Driver Behavior Prediction based on UrbanFlow*. - Zhiqian Qiao; Jing Zhao; Jin Zhu; Zachariah Tyree; Priyantha Mudalige; Jeff Schneider 001; John M. Dolan (ICRA, 2020)

Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning. - Zhiqian Qiao; Zachariah Tyree; Priyantha Mudalige; Jeff Schneider 001; John M. Dolan (IROS, 2020)

Offline Contextual Bayesian Optimization for Nuclear Fusion. - Youngseog Chung; Ian Char; Willie Neiswanger; Kirthevasan Kandasamy; Andrew Oakleigh Nelson; Mark D. Boyer; Egemen Kolemen; Jeff Schneider 001 (CoRR, 2020)

Asynchronous Multi Agent Active Search. - Ramina Ghods; Arundhati Banerjee; Jeff Schneider 001 (CoRR, 2020)

Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning. - Zhiqian Qiao; Jeff Schneider 001; John M. Dolan (CoRR, 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 001; Barnabás Póczos; Eric P. Xing (J. Mach. Learn. Res., 2020)

Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction. - Viraj Mehta; Ian Char; Willie Neiswanger; Youngseog Chung; Andrew Oakleigh Nelson; Mark D. Boyer; Egemen Kolemen; Jeff G. Schneider (CDC, 2021)

Multi-Agent Active Search using Realistic Depth-Aware Noise Model. - Ramina Ghods; William J. Durkin; Jeff Schneider 001 (ICRA, 2021)

Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning*. - Zhiqian Qiao; Jeff Schneider 001; John M. Dolan (ICRA, 2021)

Learning Urban Driving Policies using Deep Reinforcement Learning. - Tanmay Agarwal; Hitesh Arora; Jeff Schneider 001 (ITSC, 2021)

Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. - Youngseog Chung; Willie Neiswanger; Ian Char; Jeff Schneider 001 (NeurIPS, 2021)

Decentralized multi-agent active search for sparse signals. - Ramina Ghods; Arundhati Banerjee; Jeff Schneider 001 (UAI, 2021)

Affordance-based Reinforcement Learning for Urban Driving. - Tanmay Agarwal; Hitesh Arora; Jeff Schneider 001 (CoRR, 2021)

Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification. - Youngseog Chung; Ian Char; Han Guo; Jeff Schneider 001; Willie Neiswanger (CoRR, 2021)

An Experimental Design Perspective on Model-Based Reinforcement Learning. - Viraj Mehta; Biswajit Paria; Jeff Schneider 001; Stefano Ermon; Willie Neiswanger (ICLR, 2022)

Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning. - Adam R. Villaflor; Zhe Huang; Swapnil Pande; John M. Dolan; Jeff Schneider 001 (ICML, 2022)

Robust Reinforcement Learning via Genetic Curriculum. - Yeeho Song; Jeff Schneider 001 (ICRA, 2022)

Exploration via Planning for Information about the Optimal Trajectory. - Viraj Mehta; Ian Char; Joseph Abbate; Rory Conlin; Mark D. Boyer; Stefano Ermon; Jeff Schneider 001; Willie Neiswanger (NeurIPS, 2022)

SBEVNet: End-to-End Deep Stereo Layout Estimation. - Divam Gupta; Wei Pu; Trenton Tabor; Jeff Schneider 001 (WACV, 2022)

UGV-UAV Object Geolocation in Unstructured Environments. - 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 001; et al. (CoRR, 2022)

BATS: Best Action Trajectory Stitching. - Ian Char; Viraj Mehta; Adam Villaflor; John M. Dolan; Jeff Schneider 001 (CoRR, 2022)

How Useful are Gradients for OOD Detection Really? - Conor Igoe; Youngseog Chung; Ian Char; Jeff Schneider 001 (CoRR, 2022)

Cost Aware Asynchronous Multi-Agent Active Search. - Arundhati Banerjee; Ramina Ghods; Jeff Schneider 001 (CoRR, 2022)

Learning Cooperative Multi-Agent Policies With Partial Reward Decoupling. - Benjamin Freed; Aditya Kapoor; Ian Abraham; Jeff G. Schneider; Howie Choset (IEEE Robotics Autom. Lett., 2022)

Multi-Agent Active Search: A Reinforcement Learning Approach. - Conor Igoe; Ramina Ghods; Jeff Schneider 001 (IEEE Robotics Autom. Lett., 2022)

Stealthy Terrain-Aware Multi-Agent Active Search. - Nikhil Angad Bakshi; Jeff Schneider 001 (CoRL, 2023)

Cost-Awareness in Multi-Agent Active Search. - Arundhati Banerjee; Ramina Ghods; Jeff Schneider 001 (ECAI, 2023)

Near-optimal Policy Identification in Active Reinforcement Learning. - Xiang Li; Viraj Mehta; Johannes Kirschner; Ian Char; Willie Neiswanger; Jeff Schneider 001; Andreas Krause 001; Ilija Bogunovic (ICLR, 2023)

Learning Temporally AbstractWorld Models without Online Experimentation. - Benjamin Freed; Siddarth Venkatraman; Guillaume Adrien Sartoretti; Jeff Schneider 001; Howie Choset (ICML, 2023)

GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search. - Nikhil Angad Bakshi; Tejus Gupta; Ramina Ghods; Jeff G. Schneider (ICRA, 2023)

Multi-Agent Active Search using Detection and Location Uncertainty. - Arundhati Banerjee; Ramina Ghods; Jeff G. Schneider (ICRA, 2023)

Multi-Alpha Soft Actor-Critic: Overcoming Stochastic Biases in Maximum Entropy Reinforcement Learning. - Conor Igoe; Swapnil Pande; Siddarth Venkatraman; Jeff G. Schneider (ICRA, 2023)

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

PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks. - Ian Char; Jeff Schneider 001 (NeurIPS, 2023)

Enhancing Visual Domain Adaptation with Source Preparation. - Anirudha Ramesh; Anurag Ghosh; Christoph Mertz; Jeff Schneider 001 (CoRR, 2023)

Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading. - Vikram Duvvur; Aashay Mehta; Edward Sun; Bo Wu; Ken Yew Chan; Jeff Schneider 001 (CoRR, 2023)

Kernelized Offline Contextual Dueling Bandits. - Viraj Mehta; Ojash Neopane; Vikramjeet Das; Sen Lin; Jeff Schneider 001; Willie Neiswanger (CoRR, 2023)

Sample Efficient Reinforcement Learning from Human Feedback via Active Exploration. - Viraj Mehta; Vikramjeet Das; Ojash Neopane; Yijia Dai; Ilija Bogunovic; Jeff G. Schneider; Willie Neiswanger (CoRR, 2023)

Genetic Algorithm for Curriculum Design in Multi-Agent Reinforcement Learning. - Yeeho Song; Jeff G. Schneider (CoRL, 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 G. Schneider; Katerina Fragkiadaki (CVPR, 2024)

Reasoning with Latent Diffusion in Offline Reinforcement Learning. - Siddarth Venkatraman; Shivesh Khaitan; Ravi Tej Akella; John M. Dolan; Jeff Schneider 001; Glen Berseth (ICLR, 2024)

Sampling-based Multi-dimensional Recalibration. - Youngseog Chung; Ian Char; Jeff Schneider 001 (ICML, 2024)

Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data. - Fahim Tajwar; Anikait Singh; Archit Sharma; Rafael Rafailov; Jeff Schneider 001; Tengyang Xie; Stefano Ermon; Chelsea Finn; Aviral Kumar (ICML, 2024)

Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents. - Arundhati Banerjee; Jeff G. Schneider (ICRA, 2024)

Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Driving. - Adam Villaflor; Brian Yang; Huangyuan Su; Katerina Fragkiadaki; John M. Dolan; Jeff G. Schneider (ICRA, 2024)

Unifying Model-Based and Model-Free Reinforcement Learning with Equivalent Policy Sets. - Benjamin Freed; Thomas Wei; Roberto Calandra; Jeff Schneider 001; Howie Choset (RLJ, 2024)

Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization. - Aditya Kapoor; Benjamin Freed; Jeff Schneider 001; Howie Choset (RLJ, 2024)

Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following. - Brian Yang; Huangyuan Su; Nikolaos Gkanatsios; Tsung-Wei Ke; Ayush Jain; Jeff Schneider 001; Katerina Fragkiadaki (CoRR, 2024)

Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks. - Ian Char; Youngseog Chung; Joseph Abbate; Egemen Kolemen; Jeff G. Schneider (CoRR, 2024)

What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions. - Sang Keun Choe; Hwijeen Ahn; Juhan Bae; Kewen Zhao; Minsoo Kang; Youngseog Chung; Adithya Pratapa; Willie Neiswanger; Emma Strubell; Teruko Mitamura; Jeff G. Schneider; Eduard H. Hovy; et al. (CoRR, 2024)

Measure Preserving Flows for Ergodic Search in Convoluted Environments. - Albert Xu; Bhaskar Vundurthy; Geordan Gutow; Ian Abraham; Jeff G. Schneider; Howie Choset (CoRR, 2024)

Decentralized Uncertainty-Aware Active Search with a Team of Aerial Robots. - 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; et al. (CoRR, 2024)

Beyond Parameter Count: Implicit Bias in Soft Mixture of Experts. - Youngseog Chung; Dhruv Malik; Jeff Schneider 001; Yuanzhi Li; Aarti Singh (Trans. Mach. Learn. Res., 2025)

State Combinatorial Generalization In Decision Making With Conditional Diffusion Models. - Xintong Duan; Yutong He; Fahim Tajwar; Wentse Chen; Ruslan Salakhutdinov; Jeff G. Schneider (CoRR, 2025)

Training a Generally Curious Agent. - Fahim Tajwar; Yiding Jiang; Abitha Thankaraj; Sumaita Sadia Rahman; J. Zico Kolter; Jeff Schneider 001; Ruslan Salakhutdinov (CoRR, 2025)

Can Large Reasoning Models Self-Train? - Sheikh Shafayat; Fahim Tajwar; Ruslan Salakhutdinov; Jeff G. Schneider; Andrea Zanette (CoRR, 2025)

Accelerating Diffusion Models in Offline RL via Reward-Aware Consistency Trajectory Distillation. - Xintong Duan; Yutong He; Fahim Tajwar; Ruslan Salakhutdinov; J. Zico Kolter; Jeff G. Schneider (CoRR, 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 G. Schneider (CoRR, 2025)

Accelerated Online Reinforcement Learning using Auxiliary Start State Distributions. - Aman Mehra; Alexandre Capone; Jeff G. Schneider (CoRR, 2025)