Jeff G. Schneider

According to our database, Jeff G. Schneider authored at least 172 papers between 1993 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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, Norman Papernick, David Guttendorf, Erik E. Kahn, Jessica Kasemer, Jesse Holdaway, Jeff Schneider

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

Beyond Parameter Count: Implicit Bias in Soft Mixture of Experts.

Youngseog Chung, Dhruv Malik, Jeff G. Schneider, Yuanzhi Li, Aarti Singh

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, Roger B. Grosse, Eric P. Xing

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

Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Driving.

Adam Villaflor, Brian Yang, Huangyuan Su, Katerina Fragkiadaki, John M. Dolan, Jeff Schneider

CoRR, 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, Katerina Fragkiadaki

CoRR, 2024

Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization.

Aditya Kapoor, Benjamin Freed, Jeff Schneider, Howie Choset

RLJ, 2024

Unifying Model-Based and Model-Free Reinforcement Learning with Equivalent Policy Sets.

Benjamin Freed, Thomas Wei, Roberto Calandra, Jeff Schneider, Howie Choset

RLJ, 2024

Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Driving.

Adam Villaflor, Brian Yang, Huangyuan Su, Katerina Fragkiadaki, John Dolan, Jeff G. Schneider

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents.

Arundhati Banerjee, Jeff G. Schneider

Proceedings of the IEEE International Conference on Robotics and Automation, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Sampling-based Multi-dimensional Recalibration.

Youngseog Chung, Ian Char, Jeff Schneider

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Reasoning with Latent Diffusion in Offline Reinforcement Learning.

Siddarth Venkatraman, Shivesh Khaitan, Ravi Tej Akella, John Dolan, Jeff Schneider, Glen Berseth

Proceedings of the Twelfth International Conference on Learning Representations, 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

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

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

Reasoning with Latent Diffusion in Offline Reinforcement Learning.

Siddarth Venkatraman, Shivesh Khaitan, Ravi Tej Akella, John M. Dolan, Jeff G. Schneider, Glen Berseth

CoRR, 2023

Kernelized Offline Contextual Dueling Bandits.

Viraj Mehta, Ojash Neopane, Vikramjeet Das, Sen Lin, Jeff Schneider, Willie Neiswanger

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

CoRR, 2023

Enhancing Visual Domain Adaptation with Source Preparation.

Anirudha Ramesh, Anurag Ghosh, Christoph Mertz, Jeff Schneider

CoRR, 2023

PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks.

Ian Char, Jeff Schneider

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the Learning for Dynamics and Control Conference, 2023

Multi-Alpha Soft Actor-Critic: Overcoming Stochastic Biases in Maximum Entropy Reinforcement Learning.

Conor Igoe, Swapnil Pande, Siddarth Venkatraman, Jeff G. Schneider

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

Multi-Agent Active Search using Detection and Location Uncertainty.

Arundhati Banerjee, Ramina Ghods, Jeff G. Schneider

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search.

Nikhil Angad Bakshi, Tejus Gupta, Ramina Ghods, Jeff G. Schneider

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

Learning Temporally AbstractWorld Models without Online Experimentation.

Benjamin Freed, Siddarth Venkatraman, Guillaume Adrien Sartoretti, Jeff Schneider, Howie Choset

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Cost-Awareness in Multi-Agent Active Search.

Arundhati Banerjee, Ramina Ghods, Jeff Schneider

Proceedings of the ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Kraków, Poland, 2023

Stealthy Terrain-Aware Multi-Agent Active Search.

Nikhil Angad Bakshi, Jeff Schneider

Proceedings of the Conference on Robot Learning, 2023

2022

Multi-Agent Active Search: A Reinforcement Learning Approach.

Conor Igoe, Ramina Ghods, Jeff Schneider

IEEE Robotics Autom. Lett., 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

Cost Aware Asynchronous Multi-Agent Active Search.

Arundhati Banerjee, Ramina Ghods, Jeff Schneider

CoRR, 2022

How Useful are Gradients for OOD Detection Really?

Conor Igoe, Youngseog Chung, Ian Char, Jeff Schneider

CoRR, 2022

BATS: Best Action Trajectory Stitching.

Ian Char, Viraj Mehta, Adam Villaflor, John M. Dolan, Jeff Schneider

CoRR, 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, Matt Schnur, Carter Tiernan, Trenton Tabor

CoRR, 2022

SBEVNet: End-to-End Deep Stereo Layout Estimation.

Divam Gupta, Wei Pu, Trenton Tabor, Jeff Schneider

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 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, Willie Neiswanger

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Robust Reinforcement Learning via Genetic Curriculum.

Yeeho Song, Jeff Schneider

Proceedings of the 2022 International Conference on Robotics and Automation, 2022

Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning.

Adam R. Villaflor, Zhe Huang, Swapnil Pande, John M. Dolan, Jeff Schneider

Proceedings of the International Conference on Machine Learning, 2022

An Experimental Design Perspective on Model-Based Reinforcement Learning.

Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification.

Youngseog Chung, Ian Char, Han Guo, Jeff Schneider, Willie Neiswanger

CoRR, 2021

Affordance-based Reinforcement Learning for Urban Driving.

Tanmay Agarwal, Hitesh Arora, Jeff Schneider

CoRR, 2021

Decentralized multi-agent active search for sparse signals.

Ramina Ghods, Arundhati Banerjee, Jeff Schneider

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification.

Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning Urban Driving Policies using Deep Reinforcement Learning.

Tanmay Agarwal, Hitesh Arora, Jeff Schneider

Proceedings of the 24th IEEE International Intelligent Transportation Systems Conference, 2021

Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning^*.

Zhiqian Qiao, Jeff Schneider, John M. Dolan

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

Multi-Agent Active Search using Realistic Depth-Aware Noise Model.

Ramina Ghods, William J. Durkin, Jeff Schneider

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

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

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

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, Barnabás Póczos, Eric P. Xing

J. Mach. Learn. Res., 2020

Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning.

Zhiqian Qiao, Jeff Schneider, John M. Dolan

CoRR, 2020

Asynchronous Multi Agent Active Search.

Ramina Ghods, Arundhati Banerjee, Jeff Schneider

CoRR, 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

CoRR, 2020

Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning.

Zhiqian Qiao, Zachariah Tyree, Priyantha Mudalige, Jeff Schneider, John M. Dolan

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

Human Driver Behavior Prediction based on UrbanFlow^*.

Zhiqian Qiao, Jing Zhao, Jin Zhu, Zachariah Tyree, Priyantha Mudalige, Jeff Schneider, John M. Dolan

Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations.

Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Multi-fidelity Gaussian Process Bandit Optimisation.

Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos

J. Artif. Intell. Res., 2019

Human Driver Behavior Prediction based on UrbanFlow.

Zhiqian Qiao, Jing Zhao, Zachariah Tyree, Priyantha Mudalige, Jeff Schneider, John M. Dolan

CoRR, 2019

ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization.

Willie Neiswanger, Kirthevasan Kandasamy, Barnabás Póczos, Jeff Schneider, Eric P. Xing

CoRR, 2019

Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments.

Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming.

Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos

CoRR, 2018

Neural Architecture Search with Bayesian Optimisation and Optimal Transport.

Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Transformation Autoregressive Networks.

Junier B. Oliva, Avinava Dubey, Manzil Zaheer, Barnabás Póczos, Ruslan Salakhutdinov, Eric P. Xing, Jeff Schneider

Proceedings of the 35th International Conference on Machine Learning, 2018

Parallelised Bayesian Optimisation via Thompson Sampling.

Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Recurrent Estimation of Distributions.

Junier B. Oliva, Kumar Avinava Dubey, Barnabás Póczos, Eric P. Xing, Jeff G. Schneider

CoRR, 2017

Asynchronous Parallel Bayesian Optimisation via Thompson Sampling.

Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff G. Schneider, Barnabás Póczos

CoRR, 2017

Query efficient posterior estimation in scientific experiments via Bayesian active learning.

Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos

Artif. Intell., 2017

Scaling Active Search using Linear Similarity Functions.

Sibi Venkatesan, James Kyle Miller, Jeff Schneider, Artur Dubrawski

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Equivariance Through Parameter-Sharing.

Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos

Proceedings of the 34th International Conference on Machine Learning, 2017

The Statistical Recurrent Unit.

Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider

Proceedings of the 34th International Conference on Machine Learning, 2017

Multi-fidelity Bayesian Optimisation with Continuous Approximations.

Kirthevasan Kandasamy, Gautam Dasarathy, Jeff G. Schneider, Barnabás Póczos

Proceedings of the 34th International Conference on Machine Learning, 2017

Deep Learning with Sets and Point Clouds.

Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos

Proceedings of the 5th International Conference on Learning Representations, 2017

Active Optimization and Self Driving Cars.

Jeff G. Schneider

Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, 2017

Enabling Dark Energy Science with Deep Generative Models of Galaxy Images.

Siamak Ravanbakhsh, François Lanusse, Rachel Mandelbaum, Jeff G. Schneider, Barnabás Póczos

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

Active Search for Sparse Signals with Region Sensing.

Yifei Ma, Roman Garnett, Jeff G. Schneider

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

The Multi-fidelity Multi-armed Bandit.

Kirthevasan Kandasamy, Gautam Dasarathy, Barnabás Póczos, Jeff G. Schneider

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations.

Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems.

Xuezhi Wang, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 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

Proceedings of the 33nd International Conference on Machine Learning, 2016

Stochastic Neural Networks with Monotonic Activation Functions.

Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Bayesian Nonparametric Kernel-Learning.

Junier B. Oliva, Avinava Dubey, Andrew Gordon Wilson, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models.

Chun-Liang Li, Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Linear-Time Learning on Distributions with Approximate Kernel Embeddings.

Danica J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Deep Mean Maps.

Junier B. Oliva, Danica J. Sutherland, Barnabás Póczos, Jeff G. Schneider

CoRR, 2015

Generalization Bounds for Transfer Learning under Model Shift.

Xuezhi Wang, Jeff G. Schneider

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

On the Error of Random Fourier Features.

Danica J. Sutherland, Jeff G. Schneider

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Active Search and Bandits on Graphs using Sigma-Optimality.

Yifei Ma, Tzu-Kuo Huang, Jeff G. Schneider

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Bayesian Active Learning for Posterior Estimation - IJCAI-15 Distinguished Paper.

Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

High Dimensional Bayesian Optimisation and Bandits via Additive Models.

Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos

Proceedings of the 32nd International Conference on Machine Learning, 2015

Finding Galaxies in the Shadows of Quasars with Gaussian Processes.

Roman Garnett, Shirley Ho, Jeff G. Schneider

Proceedings of the 32nd International Conference on Machine Learning, 2015

Statistical Learning in Chip (SLIC).

Ronald D. Blanton, Xin Li, Ken Mai, Diana Marculescu, Radu Marculescu, Jeyanandh Paramesh, Jeff G. Schneider, Donald E. Thomas

Proceedings of the IEEE/ACM International Conference on Computer-Aided Design, 2015

Fast Function to Function Regression.

Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Hy Trac, Shirley Ho, Jeff G. Schneider

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

Active Pointillistic Pattern Search.

Yifei Ma, Danica J. Sutherland, Roman Garnett, Jeff G. Schneider

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Fast Function to Function Regression.

Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Jeff G. Schneider

CoRR, 2014

Learning from Point Sets with Observational Bias.

Liang Xiong, Jeff G. Schneider

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Flexible Transfer Learning under Support and Model Shift.

Xuezhi Wang, Jeff G. Schneider

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

SLIC: Statistical learning in chip.

Ronald D. Blanton, Xin Li, Ken Mai, Diana Marculescu, Radu Marculescu, Jeyanandh Paramesh, Jeff G. Schneider, Donald E. Thomas

Proceedings of the 2014 International Symposium on Integrated Circuits (ISIC), 2014

Systematic Labeling Bias: De-biasing Where Everyone is Wrong.

Guillermo F. Cabrera, Christopher J. Miller, Jeff G. Schneider

Proceedings of the 22nd International Conference on Pattern Recognition, 2014

Active Transfer Learning under Model Shift.

Xuezhi Wang, Tzu-Kuo Huang, Jeff G. Schneider

Proceedings of the 31th International Conference on Machine Learning, 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

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Fast Distribution To Real Regression.

Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Active Area Search via Bayesian Quadrature.

Yifei Ma, Roman Garnett, Jeff G. Schneider

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Σ-Optimality for Active Learning on Gaussian Random Fields.

Yifei Ma, Roman Garnett, Jeff G. Schneider

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition.

Tzu-Kuo Huang, Jeff G. Schneider

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Active search on graphs.

Xuezhi Wang, Roman Garnett, Jeff G. Schneider

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

Active learning and search on low-rank matrices.

Danica J. Sutherland, Barnabás Póczos, Jeff G. Schneider

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

Expensive multiobjective optimization for robotics.

Matthew Tesch, Jeff G. Schneider, Howie Choset

Proceedings of the 2013 IEEE International Conference on Robotics and Automation, 2013

Expensive Function Optimization with Stochastic Binary Outcomes.

Matthew Tesch, Jeff G. Schneider, Howie Choset

Proceedings of the 30th International Conference on Machine Learning, 2013

Distribution to Distribution Regression.

Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider

Proceedings of the 30th International Conference on Machine Learning, 2013

Spectral Learning of Hidden Markov Models from Dynamic and Static Data.

Tzu-Kuo Huang, Jeff G. Schneider

Proceedings of the 30th International Conference on Machine Learning, 2013

Efficient Learning on Point Sets.

Liang Xiong, Barnabás Póczos, Jeff G. Schneider

Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

2012

A Composite Likelihood View for Multi-Label Classification.

Yi Zhang, Jeff G. Schneider

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Nonparametric Estimation of Conditional Information and Divergences.

Barnabás Póczos, Jeff G. Schneider

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Submodularity in Batch Active Learning and Survey Problems on Gaussian Random Fields

Yifei Ma, Roman Garnett, Jeff G. Schneider

CoRR, 2012

Support Distribution Machines

Barnabás Póczos, Liang Xiong, Danica J. Sutherland, Jeff G. Schneider

CoRR, 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

Learning Bi-clustered Vector Autoregressive Models.

Tzu-Kuo Huang, Jeff G. Schneider

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Maximum Margin Output Coding.

Yi Zhang, Jeff G. Schneider

Proceedings of the 29th International Conference on Machine Learning, 2012

Copula-based Kernel Dependency Measures.

Barnabás Póczos, Zoubin Ghahramani, Jeff G. Schneider

Proceedings of the 29th International Conference on Machine Learning, 2012

Bayesian Optimal Active Search and Surveying.

Roman Garnett, Yamuna Krishnamurthy, Xuehan Xiong, Jeff G. Schneider, Richard P. Mann

Proceedings of the 29th International Conference on Machine Learning, 2012

Nonparametric kernel estimators for image classification.

Barnabás Póczos, Liang Xiong, Danica J. Sutherland, Jeff G. Schneider

Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

2011

Multi-Label Output Codes using Canonical Correlation Analysis.

Yi Zhang, Jeff G. Schneider

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Hierarchical Probabilistic Models for Group Anomaly Detection.

Liang Xiong, Barnabás Póczos, Jeff G. Schneider, Andrew J. Connolly, Jake VanderPlas

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

On the Estimation of alpha-Divergences.

Barnabás Póczos, Jeff G. Schneider

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions.

Barnabás Póczos, Liang Xiong, Jeff G. Schneider

Proceedings of the UAI 2011, 2011

Group Anomaly Detection using Flexible Genre Models.

Liang Xiong, Barnabás Póczos, Jeff G. Schneider

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Learning Auto-regressive Models from Sequence and Non-sequence Data.

Tzu-Kuo Huang, Jeff G. Schneider

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Using response surfaces and expected improvement to optimize snake robot gait parameters.

Matthew Tesch, Jeff G. Schneider, Howie Choset

Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011

Adapting control policies for expensive systems to changing environments.

Matthew Tesch, Jeff G. Schneider, Howie Choset

Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011

Direct Robust Matrix Factorizatoin for Anomaly Detection.

Liang Xiong, Xi Chen, Jeff G. Schneider

Proceedings of the 11th IEEE International Conference on Data Mining, 2011

Nonparametric divergence estimators for independent subspace analysis.

Barnabás Póczos, Zoltán Szabó, Jeff G. Schneider

Proceedings of the 19th European Signal Processing Conference, 2011

2010

Learning Nonlinear Dynamic Models from Non-sequenced Data.

Tzu-Kuo Huang, Le Song, Jeff G. Schneider

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Learning Compressible Models.

Yi Zhang, Jeff G. Schneider, Artur Dubrawski

Proceedings of the SIAM International Conference on Data Mining, 2010

Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization.

Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff G. Schneider, Jaime G. Carbonell

Proceedings of the SIAM International Conference on Data Mining, 2010

A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy.

Pinar Donmez, Jaime G. Carbonell, Jeff G. Schneider

Proceedings of the SIAM International Conference on Data Mining, 2010

Learning Multiple Tasks with a Sparse Matrix-Normal Penalty.

Yi Zhang, Jeff G. Schneider

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Projection Penalties: Dimension Reduction without Loss.

Yi Zhang, Jeff G. Schneider

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

2009

Efficiently learning the accuracy of labeling sources for selective sampling.

Pinar Donmez, Jaime G. Carbonell, Jeff G. Schneider

Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28, 2009

Learning linear dynamical systems without sequence information.

Tzu-Kuo Huang, Jeff G. Schneider

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text.

Yi Zhang, Jeff G. Schneider, Artur Dubrawski

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Anomaly pattern detection in categorical datasets.

Kaustav Das, Jeff G. Schneider, Daniel B. Neill

Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008

Actively learning level-sets of composite functions.

Brent Bryan, Jeff G. Schneider

Proceedings of the Machine Learning, 2008

Learning Detectors of Events in Multivariate Time Series.

Josep Roure, Artur Dubrawski, Jeff G. Schneider

Proceedings of the AMIA 2008, 2008

2007

Detecting anomalous records in categorical datasets.

Kaustav Das, Jeff G. Schneider

Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007

Efficiently computing minimax expected-size confidence regions.

Brent Bryan, H. Brendan McMahan, Chad M. Schafer, Jeff G. Schneider

Proceedings of the Machine Learning, 2007

A Study into Detection of Bio-Events in Multiple Streams of Surveillance Data.

Josep Roure, Artur Dubrawski, Jeff G. Schneider

Proceedings of the Intelligence and Security Informatics: Biosurveillance, 2007

2005

Active Learning For Identifying Function Threshold Boundaries.

Brent Bryan, Jeff G. Schneider, Robert Nichol, Christopher J. Miller, Christopher R. Genovese, Larry A. Wasserman

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Learning Opportunity Costs in Multi-Robot Market Based Planners.

Jeff G. Schneider, David Apfelbaum, Drew Bagnell, Reid G. Simmons

Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005

Game Theoretic Control for Robot Teams.

Rosemary Emery-Montemerlo, Geoffrey J. Gordon, Jeff G. Schneider, Sebastian Thrun

Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005

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

Belief state approaches to signaling alarms in surveillance systems.

Kaustav Das, Andrew W. Moore, Jeff G. Schneider

Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004

Approximate Solutions for Partially Observable Stochastic Games with Common Payoffs.

Rosemary Emery-Montemerlo, Geoffrey J. Gordon, Jeff G. Schneider, Sebastian Thrun

Proceedings of the 3rd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2004), 2004

2003

Policy Search by Dynamic Programming.

J. Andrew Bagnell, Sham M. Kakade, Andrew Y. Ng, Jeff G. Schneider

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Covariant Policy Search.

J. Andrew Bagnell, Jeff G. Schneider

Proceedings of the IJCAI-03, 2003

Finding Underlying Connections: A Fast Graph-Based Method for Link Analysis and Collaboration Queries.

Jeremy Kubica, Andrew W. Moore, David Cohn, Jeff G. Schneider

Proceedings of the Machine Learning, 2003

Tractable Group Detection on Large Link Data Sets.

Jeremy Kubica, Andrew W. Moore, Jeff G. Schneider

Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003), 2003

2002

Real-valued All-Dimensions Search: Low-overhead Rapid Searching over Subsets of Attributes.

Andrew W. Moore, Jeff G. Schneider

Proceedings of the UAI '02, 2002

Stochastic Link and Group Detection.

Jeremy Kubica, Andrew W. Moore, Jeff G. Schneider, Yiming Yang

Proceedings of the Eighteenth National Conference on Artificial Intelligence and Fourteenth Conference on Innovative Applications of Artificial Intelligence, July 28, 2002

2001

Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures.

Yanxi Liu, Frank Dellaert, William E. Rothfus, Andrew W. Moore, Jeff G. Schneider, Takeo Kanade

Proceedings of the Medical Image Computing and Computer-Assisted Intervention, 2001

Autonomous Helicopter Control using Reinforcement Learning Policy Search Methods.

J. Andrew Bagnell, Jeff G. Schneider

Proceedings of the 2001 IEEE International Conference on Robotics and Automation, 2001

2000

Reinforcement Learning for Cooperating and Communicating Reactive Agents in Electrical Power Grids.

Martin A. Riedmiller, Andrew W. Moore, Jeff G. Schneider

Proceedings of the Balancing Reactivity and Social Deliberation in Multi-Agent Systems, 2000

1999

3-D Deformable Registration of Medical Images Using a Statistical Atlas.

Mei Chen, Takeo Kanade, Dean Pomerleau, Jeff G. Schneider

Proceedings of the Medical Image Computing and Computer-Assisted Intervention, 1999

Distributed Value Functions.

Jeff G. Schneider, Weng-Keen Wong, Andrew W. Moore, Martin A. Riedmiller

Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27, 1999

1998

Value Function Based Production Scheduling.

Jeff G. Schneider, Justin A. Boyan, Andrew W. Moore

Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), 1998

Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions.

Andrew W. Moore, Jeff G. Schneider, Justin A. Boyan, Mary S. Lee

Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), 1998

1997

Efficient Locally Weighted Polynomial Regression Predictions.

Andrew W. Moore, Jeff G. Schneider, Kan Deng

Proceedings of the Fourteenth International Conference on Machine Learning (ICML 1997), 1997

Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators.

Artur Dubrawski, Jeff Schneider

Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, 1997

1996

Exploiting Model Uncertainty Estimates for Safe Dynamic Control Learning.

Jeff G. Schneider

Proceedings of the Advances in Neural Information Processing Systems 9, 1996

1995

Memory-based Stochastic Optimization.

Andrew W. Moore, Jeff G. Schneider

Proceedings of the Advances in Neural Information Processing Systems 8, 1995

Cooperative coaching in robot learning.

Jeff G. Schneider, Christopher M. Brown

Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 1995

1994

Efficient search for robot skill learning: simulation and reality.

Jeff G. Schneider, Roger F. Gans

Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 1994

High Dimension Action Spaces in Robot Skill Learning.

Jeff G. Schneider

Proceedings of the 12th National Conference on Artificial Intelligence, Seattle, WA, USA, July 31, 1994

1993

Robot Skill Learning, Basic Functions, and Control Regimes.

Jeff G. Schneider, Christopher M. Brown

Proceedings of the 1993 IEEE International Conference on Robotics and Automation, 1993