J. Schneider

发表

Autonomous helicopter control using reinforcement learning policy search methods

Jeff G. Schneider, J. Andrew Bagnell, J. Bagnell, 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

Active search on graphs

Roman Garnett, Jeff G. Schneider, Xuezhi Wang, 2013, KDD.

Covariant policy search

Jeff G. Schneider, J. Andrew Bagnell, J. Bagnell, 2003, IJCAI 2003.

Autonomous Helicopter Control using Reinforcement Learning

J. Bagnell, J. Schneider, 2001 .

Learning decisions: robustness, uncertainty, and approximation

A. Moore, J. Bagnell, J. Schneider, 2004 .

Efficient Locally Weighted Polynomial Regression Predictions

Andrew W. Moore, Jeff G. Schneider, Kan Deng, 1997, ICML.

Policy search in kernel Hilbert space

J. Bagnell, J. Schneider, 2003 .

Exploiting Model Uncertainty Estimates for Safe Dynamic Control Learning

Jeff G. Schneider, J. Schneider, 1996, NIPS.

Policy Search by Dynamic Programming

Jeff G. Schneider, Sham M. Kakade, Andrew Y. Ng, 2003, NIPS.

Distributed Value Functions

Andrew W. Moore, Weng-Keen Wong, Jeff G. Schneider, 1999, ICML.

STATUS OF THE MACHINE LEARNING EFFORTS AT THE INTERNATIONAL DATA CENTRE OF THE CTBTO

Jeff G. Schneider, Stuart Russell, Nimar S. Arora, 2010 .

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

Howie Choset, Jeff G. Schneider, Matthew Tesch, 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Deep Learning with Sets and Point Clouds pdf

Barnabás Póczos, Jeff G. Schneider, Siamak Ravanbakhsh, 2016, ICLR.

DYNAMICAL MASS MEASUREMENTS OF CONTAMINATED GALAXY CLUSTERS USING MACHINE LEARNING

Danica J. Sutherland, J. Schneider, B. Póczos, 2015, 1509.05409.

Value Function Based Production Scheduling

Andrew W. Moore, Jeff G. Schneider, Justin A. Boyan, 1998, ICML.

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

Kirthevasan Kandasamy, Barnabás Póczos, Eric P. Xing, 2019, AISTATS.

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

Andrew W. Moore, Jeff G. Schneider, Martin A. Riedmiller, 2000, Balancing Reactivity and Social Deliberation in Multi-Agent Systems.

Memory-based Stochastic Optimization

Andrew W. Moore, Jeff G. Schneider, A. Moore, 1995, NIPS.

Flexible Transfer Learning under Support and Model Shift

Jeff G. Schneider, Xuezhi Wang, J. Schneider, 2014, NIPS.

Q2: memory-based active learning for optimizing noisy continuous functions

Andrew W. Moore, Jeff G. Schneider, Justin A. Boyan, 1998, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

Efficiently learning the accuracy of labeling sources for selective sampling

Jaime G. Carbonell, Jeff G. Schneider, Pinar Donmez, 2009, KDD.

Hierarchical Probabilistic Models for Group Anomaly Detection

Barnabás Póczos, Jeff G. Schneider, Liang Xiong, 2011, AISTATS.

Linear-Time Learning on Distributions with Approximate Kernel Embeddings pdf

Barnabás Póczos, Jeff G. Schneider, Junier B. Oliva, 2015, AAAI.

A MACHINE LEARNING APPROACH FOR DYNAMICAL MASS MEASUREMENTS OF GALAXY CLUSTERS

Danica J. Sutherland, J. Schneider, B. Póczos, 2014, 1410.0686.

Bayesian Nonparametric Kernel-Learning

Barnabás Póczos, Andrew Gordon Wilson, Eric P. Xing, 2015, AISTATS.

Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification pdf

Willie Neiswanger, Jeff Schneider, Ian Char, 2020, NeurIPS.

Fast Algorithms and Efficient Statistics: N-Point Correlation Functions

L. Wasserman, A. Moore, J. Schneider, 2000, astro-ph/0012333.

FuSSO: Functional Shrinkage and Selection Operator pdf

Barnabás Póczos, Jeff G. Schneider, Aarti Singh, 2013, AISTATS.

Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures

Takeo Kanade, Andrew W. Moore, Yanxi Liu, 2001, MICCAI.

Deep Kinematic Models for Physically Realistic Prediction of Vehicle Trajectories pdf

David Bradley, Henggang Cui, Fang-Chieh Chou, 2019, ArXiv.

Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets

Henggang Cui, Fang-Chieh Chou, Nemanja Djuric, 2019, 2020 IEEE Intelligent Vehicles Symposium (IV).

Bayesian Optimal Active Search and Surveying

Roman Garnett, Jeff G. Schneider, Richard P. Mann, 2012, ICML.

Bayesian Optimal Active Search on Graphs

J. Schneider, R. Garnett, Yamuna Krishnamurthy, 2011 .

Copula-based Kernel Dependency Measures pdf

Barnabás Póczos, Zoubin Ghahramani, Jeff G. Schneider, 2012, ICML.

Multi-fidelity Gaussian Process Bandit Optimisation

Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider, 2016, J. Artif. Intell. Res..

Solving Uncertain Markov Decision Processes

Andrew Y. Ng, J. Andrew Bagnell, Jeff Schneider, 2001 .

Active Learning For Identifying Function Threshold Boundaries

Larry A. Wasserman, Christopher R. Genovese, Jeff G. Schneider, 2005, NIPS.

Nonparametric kernel estimators for image classification

Barnabás Póczos, Jeff G. Schneider, Liang Xiong, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

Σ-Optimality for Active Learning on Gaussian Random Fields

Roman Garnett, Jeff G. Schneider, Yifei Ma, 2013, NIPS.

Approximate solutions for partially observable stochastic games with common payoffs

Jeff G. Schneider, Geoffrey J. Gordon, Sebastian Thrun, 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

Learning Multiple Tasks with a Sparse Matrix-Normal Penalty

Jeff G. Schneider, Yi Zhang, J. Schneider, 2010, NIPS.

Expensive multiobjective optimization for robotics

Howie Choset, Jeff G. Schneider, Matthew Tesch, 2013, 2013 IEEE International Conference on Robotics and Automation.

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

Takeo Kanade, Mei Chen, Jeff G. Schneider, 1999, MICCAI.

Distribution to Distribution Regression

Barnabás Póczos, Jeff G. Schneider, Junier B. Oliva, 2013, ICML.

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

Jaime G. Carbonell, Jeff G. Schneider, Pinar Donmez, 2010, SDM.

Automatic construction of active appearance models as an image coding problem

Jeff G. Schneider, Simon Baker, Iain A. Matthews, 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Robot skill learning, basis functions, and control regimes

Jeff G. Schneider, Christopher M. Brown, J. Schneider, 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

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

Jeff G. Schneider, Tzu-Kuo Huang, J. Schneider, 2011, NIPS.

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

Jeff G. Schneider, Josep Roure Alcobé, Artur Dubrawski, 2007, BioSurveillance.

Active Search for Sparse Signals with Region Sensing

Roman Garnett, Jeff G. Schneider, Yifei Ma, 2016, AAAI.

Anomaly pattern detection in categorical datasets

Jeff G. Schneider, Daniel B. Neill, Kaustav Das, 2008, KDD.

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

Jeff G. Schneider, Tzu-Kuo Huang, J. Schneider, 2013, NIPS.

On the Error of Random Fourier Features

Jeff G. Schneider, Dougal J. Sutherland, Danica J. Sutherland, 2015, UAI.

Efficient search for robot skill learning: simulation and reality

Jeff G. Schneider, Roger F. Gans, J. Schneider, 1994, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94).

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

Jeff G. Schneider, Yi Zhang, Artur Dubrawski, 2008, NIPS.

The Statistical Recurrent Unit pdf

Barnabás Póczos, Jeff G. Schneider, Junier B. Oliva, 2017, ICML.

Active Pointillistic Pattern Search

Roman Garnett, Jeff G. Schneider, Yifei Ma, 2015, AISTATS.

On the Estimation of alpha-Divergences

Barnabás Póczos, Jeff G. Schneider, J. Schneider, 2011, AISTATS.

Efficiently computing minimax expected-size confidence regions

Jeff G. Schneider, Chad M. Schafer, H. Brendan McMahan, 2007, ICML '07.

Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving

Henggang Cui, Fang-Chieh Chou, Nemanja Djuric, 2018, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

High Dimensional Bayesian Optimisation and Bandits via Additive Models

Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider, 2015, ICML.

Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations

Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider, 2016, NIPS.

Nonparametric divergence estimators for independent subspace analysis

Barnabás Póczos, Jeff G. Schneider, Zoltán Szabó, 2011, 2011 19th European Signal Processing Conference.

Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization

Xi Chen, Jaime G. Carbonell, Jeff G. Schneider, 2010, SDM.

Fast Distribution To Real Regression

Barnabás Póczos, Eric P. Xing, Jeff G. Schneider, 2013, AISTATS.

Actively learning level-sets of composite functions

Jeff G. Schneider, Brent Bryan, J. Schneider, 2008, ICML '08.

Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions

Barnabás Póczos, Jeff G. Schneider, Liang Xiong, 2011, UAI.

Analysis of Prioritizing Malware

J. Schneider, Zhenhao Tang, 2014 .

Direct Robust Matrix Factorizatoin for Anomaly Detection

Xi Chen, Jeff G. Schneider, Liang Xiong, 2011, 2011 IEEE 11th International Conference on Data Mining.

Group Anomaly Detection using Flexible Genre Models

Barnabás Póczos, Jeff G. Schneider, Liang Xiong, 2011, NIPS.

Game Theoretic Control for Robot Teams

Jeff G. Schneider, Geoffrey J. Gordon, Sebastian Thrun, 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

Game-Theoretic Communication Policies for Partially Observable Markov Games

S. Thrun, J. Schneider, G. Gordon, 2022 .

Gaussian Process Optimisation with Multi-fidelity Evaluations

Junier B. Oliva, J. Schneider, Kirthevasan Kandasamy, 2017 .

Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictions

Henggang Cui, Fang-Chieh Chou, Nemanja Djuric, 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).

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

J. Schneider, Ramina Ghods, W. Durkin, 2020, 2021 IEEE International Conference on Robotics and Automation (ICRA).

Controlling the False-Discovery Rate in Astrophysical Data Analysis

Christopher J. Miller, Caltech, L. Wasserman, 2001, astro-ph/0107034.

Cost-Aware Bayesian Optimization via Information Directed Sampling

J. Schneider, B. Póczos, W. Neiswanger, 2020 .

Learning to Drive using Waypoints

J. Schneider, H. Arora, Tanmay Agarwal, 2019 .

M L ] 3 0 Ja n 20 18 Bayesian Nonparametric Kernel-Learning

Junier B. Oliva, A. G. Wilson, E. Xing, 2018 .

Expensive Multiobjective Optimization and Validation with a Robotics Application

J. Schneider, H. Choset, M. Tesch, 2012 .

Active learning and search on low-rank matrices

Barnabás Póczos, Jeff G. Schneider, Dougal J. Sutherland, 2013, KDD.

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

Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider, 2015, International Joint Conference on Artificial Intelligence.

Anomaly Detection for Astronomical Data

Barnabás Póczos, Andrew J. Connolly, Jeff Schneider, 2010 .

CLASSIFICATION OF STELLAR SPECTRA WITH LOCAL LINEAR EMBEDDING

Andrew J. Connolly, Jeff Schneider, Scott F. Daniel, 2011 .

Computational AstroStatistics: Fast and Efficient Tools for Analysing Huge Astronomical Data Sources

A. J. Connolly, A. M. Hopkins, G. T. Richards, 2001 .

The Effect of Large-Scale Structure on the SDSS Galaxy Three-Point Correlation Function

A. J. Connolly, A. Szalay, A. Pope, 2006, astro-ph/0602548.

Robust Nonparametric Copula Based Dependence Estimators

Csaba Szepesvari, D. Pál, J. Schneider, 2011 .

Systematic Labeling Bias in Galaxy Morphologies

Jeff Schneider, Christopher J. Miller, Guillermo Cabrera-Vives, 2018, The Astronomical Journal.

Published at ICLR 2020 Integration of Deep Neural Models and Differential Equations Workshop NEURAL DYNAMICAL SYSTEMS

J. Schneider, W. Neiswanger, M. Boyer, 2020 .

Machine Learning Department School of Computer Science 2012 Support Distribution Machines

Danica J. Sutherland, J. Schneider, B. Póczos, 2015 .

CDS&E: Collaborative Research: Machine Learning for Automated Discovery and Control in Turbulent Plasma

J. Schneider, B. Póczos, H. Trac, 2015 .

Time-Evolving Collaborative Filtering

Xi Chen, Jaime G. Carbonell, Liang Xiong, 2010 .

On the Estimation of α-Divergences

J. Schneider, B. Póczos, 2011 .

DETERMINING FREQUENTIST CONFIDENCE LIMITS USING A DIRECTED PARAMETER SPACE SEARCH

J. Schneider, A. Connolly, S. Daniel, 2012, 1205.2708.

Query efficient posterior estimation in scientific experiments via Bayesian active learning

J. Schneider, Kirthevasan Kandasamy, B. Póczos, 2017, Artif. Intell..

Conditional Distance Variance and Correlation

Barnabás Póczos, Jeff Schneider, J. Schneider, 2012 .

Controlling the False-Discovery Rate in Astrophysical Data Analysis

L. Wasserman, A. Moore, J. Schneider, 2001, astro-ph/0107034.

An Efficient Parameter Space Search as an Alternative to Markov Chain Monte Carlo

J. Schneider, A. Connolly, S. Daniel, 2012 .

Fast Algorithms and Efficient Statistics: Density Estimation in Large Astronomical Datasets

L. Wasserman, A. Moore, J. Schneider, 2000, astro-ph/0008187.

Cost Aware Asynchronous Multi-Agent Active Search

J. Schneider, Ramina Ghods, Arundhati Banerjee, 2022, ArXiv.

Exploration via Planning for Information about the Optimal Trajectory

J. Schneider, S. Ermon, W. Neiswanger, 2022, NeurIPS.

Computational AstroStatistics: Fast Algorithms and Efficient Statistics for Density Estimation in Large Astronomical Datasets

L. Wasserman, A. Moore, J. Schneider, 2000, astro-ph/0007404.

Computational AstroStatistics: Fast and Efficient Tools for Analysing Huge Astronomical Data Sources

L. Wasserman, A. Moore, J. Schneider, 2001, astro-ph/0110230.

Estimation of Correlations in Large Samples

Andrew W. Moore, Alexander S. Szalay, Simon Prunet, 2001 .

Fast Algorithms and Efficient Statistics: N-Point Correlation Functions

L. Wasserman, A. Moore, J. Schneider, 2000, astro-ph/0012333.

Statistical computations with AstroGrid and the Grid

L. Wasserman, A. Moore, J. Schneider, 2005, astro-ph/0511437.

Near-optimal Policy Identification in Active Reinforcement Learning

Xiang Li, Willie Neiswanger, J. Schneider, 2022, ICLR.

Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning

J. Schneider, J. Dolan, Swapnil Pande, 2022, ICML.

Tools and Algorithms for Querying and Mining Large Graphs

Philip S. Yu, J. Schneider, C. Faloutsos, 2008 .

How Useful are Gradients for OOD Detection Really?

J. Schneider, Youngseog Chung, I. Char, 2022, ArXiv.

Stochastic Neural Networks with Monotonic Activation Functions

Dale Schuurmans, Barnabás Póczos, Jeff G. Schneider, 2016, AISTATS.

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

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, 2018, ArXiv.

ENVIRONMENTALLY ADAPTIVE CONTROL POLICIES FOR EXPENSIVE SYSTEMS

Howie Choset, Jeff Schneider, Matthew Tesch, 2012 .

Learning with Limited Supervision by Input and Output Coding

Yi Zhang, Jeff Schneider, J. Schneider, 2012 .

Learning Compressible Models

Jeff G. Schneider, Yi Zhang, Artur Dubrawski, 2010, SDM.

Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction

J. Schneider, W. Neiswanger, M. Boyer, 2020, 2021 60th IEEE Conference on Decision and Control (CDC).

Robot Skill Learning Through Intelligent Experimentation

Jeff Schneider, J. Schneider, 1995 .

Active Transfer Learning under Model Shift

Jeff G. Schneider, Xuezhi Wang, Tzu-Kuo Huang, 2014, ICML.

ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language

Eric Xing, Kirthevasan Kandasamy, Willie Neiswanger, 2019 .

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

Kirthevasan Kandasamy, Barnabás Póczos, Eric P. Xing, 2019, ArXiv.

Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly

Kirthevasan Kandasamy, Barnabás Póczos, Eric P. Xing, 2019, J. Mach. Learn. Res..

Learning Bi-clustered Vector Autoregressive Models

Jeff G. Schneider, Tzu-Kuo Huang, J. Schneider, 2012, ECML/PKDD.

Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems

Barnabás Póczos, Jeff G. Schneider, Xuezhi Wang, 2016, IJCAI.

Neural Architecture Search with Bayesian Optimisation and Optimal Transport pdf

Kirthevasan Kandasamy, Barnabás Póczos, Eric P. Xing, 2018, NeurIPS.

Adapting control policies for expensive systems to changing environments

Howie Choset, Jeff G. Schneider, Matthew Tesch, 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Learning from Point Sets with Observational Bias

Jeff G. Schneider, Liang Xiong, J. Schneider, 2014, UAI.

Nonparametric Estimation of Conditional Information and Divergences

Barnabás Póczos, Jeff G. Schneider, J. Schneider, 2012, AISTATS.

Support Distribution Machines pdf

Barnabás Póczos, Jeff G. Schneider, Liang Xiong, 2012, ArXiv.

Kernels on Sample Sets via Nonparametric Divergence Estimates

Liang Xiong, Jeff Schneider, Dougal J. Sutherland, 2012, 1202.0302.

Nonparametric Divergence Estimation and its Applications to Machine Learning

Barnabás Póczos, Jeff Schneider, Liang Xiong, 2011 .

Fast Function to Function Regression pdf

Barnabás Póczos, Eric P. Xing, Jeff G. Schneider, 2014, AISTATS.

Affordance-based Reinforcement Learning for Urban Driving pdf

Jeff Schneider, Hitesh Arora, Tanmay Agarwal, 2021, ArXiv.

Predicting Events Surrounding the Egyptian Revolution of 2011 Using Learning Algorithms on Micro Blog Data

Margeret Hall, Jeff Schneider, Benedikt Boecking, 2014 .

Equivariance Through Parameter-Sharing pdf

Barnabás Póczos, Jeff G. Schneider, Siamak Ravanbakhsh, 2017, ICML.

Learning Dynamic Models from Non-sequenced Data

Tzu-Kuo Huang, Je Schneider, J. Schneider, 2010 .

Learning Nonlinear Dynamic Models from Non-sequenced Data

Le Song, Jeff G. Schneider, Tzu-Kuo Huang, 2010, AISTATS.

Actively learning specific function properties with applications to statistical inference

Jeff Schneider, Brent Bryan, J. Schneider, 2007 .

Belief state approaches to signaling alarms in surveillance systems

Andrew W. Moore, Jeff G. Schneider, Kaustav Das, 2004, KDD.

Semantic-based Biomedical Image Indexing and Retrieval

Takeo Kanade, Yanxi Liu, Frank Dellaert, 2003 .

Parallelised Bayesian Optimisation via Thompson Sampling

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, 2018, AISTATS.

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

Barnabás Póczos, Jeff G. Schneider, Siamak Ravanbakhsh, 2016, AAAI.

Multi-fidelity Bayesian Optimisation with Continuous Approximations

Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider, 2017, ICML.

Systematic Labeling Bias: De-biasing Where Everyone is Wrong

Jeff G. Schneider, Christopher J. Miller, Guillermo F. Cabrera, 2014, 2014 22nd International Conference on Pattern Recognition.

An Experimental Design Perspective on Model-Based Reinforcement Learning

J. Schneider, S. Ermon, W. Neiswanger, 2021, ICLR.

Computational catalyst discovery: Active classification through myopic multiscale sampling.

Zachary W. Ulissi, Eric P. Xing, J. Schneider, 2021, The Journal of chemical physics.

Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, 2019, ICML.

Offline Contextual Bayesian Optimization

Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, 2019, NeurIPS.

Active Area Search via Bayesian Quadrature

Roman Garnett, Jeff G. Schneider, Yifei Ma, 2014, AISTATS.

A FIRST LOOK AT CREATING MOCK CATALOGS WITH MACHINE LEARNING TECHNIQUES

J. Schneider, B. Póczos, S. Ho, 2013, 1303.1055.

Human Driver Behavior Prediction based on UrbanFlow*

Jing Zhao, John M. Dolan, Jeff Schneider, 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).

Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving

J. Schneider, Henggang Cui, Nemanja Djuric, 2018, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks

Henggang Cui, Nemanja Djuric, Tzu-Kuo Huang, 2018, 2019 International Conference on Robotics and Automation (ICRA).

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

Willie Neiswanger, Jeff Schneider, Ian Char, 2021, ArXiv.

Motion Prediction of Traffic Actors for Autonomous Driving using Deep Convolutional Networks pdf

Henggang Cui, Nemanja Djuric, Tsung-Han Lin, 2018, ArXiv.

Stochastic link and group detection

Yiming Yang, Andrew W. Moore, Jeff G. Schneider, 2002, AAAI/IAAI.

Estimating Cosmological Parameters from the Dark Matter Distribution pdf

Barnabás Póczos, Jeff G. Schneider, Siamak Ravanbakhsh, 2016, ICML.

Detecting patterns of anomalies

Jeff Schneider, Kaustav Das, J. Schneider, 2009 .

High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models

Chun-Liang Li, Kirthevasan Kandasamy, Barnabás Póczos, 2016, AISTATS.

Robust Reinforcement Learning via Genetic Curriculum

J. Schneider, Yeeho Song, 2022, 2022 International Conference on Robotics and Automation (ICRA).

A Composite Likelihood View for Multi-Label Classification

Jeff G. Schneider, Yi Zhang, J. Schneider, 2012, AISTATS.

Asynchronous Parallel Bayesian Optimisation via Thompson Sampling pdf

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, 2017, ArXiv.

Asynchronous Multi Agent Active Search pdf

Arundhati Banerjee, Jeff Schneider, Ramina Ghods, 2020, ArXiv.

Decentralized multi-agent active search for sparse signals

Jeff Schneider, Ramina Ghods, Arundhati Banerjee, 2021, UAI.

Bayesian active learning for posterior estimation

Kirthevasan Kandasamy, Barnabás Póczos, Jeff Schneider, 2015 .

Detecting anomalous records in categorical datasets

Jeff G. Schneider, Kaustav Das, J. Schneider, 2007, KDD '07.

SDSS-RASS: Next generation of cluster - Finding algorithms

L. Wasserman, A. Moore, J. Schneider, 2000, astro-ph/0011557.

Kernelized Offline Contextual Dueling Bandits

J. Schneider, W. Neiswanger, Viraj Mehta, 2023, ArXiv.

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

Wei Pu, Jeff Schneider, Divam Gupta, 2021, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

Transformation Autoregressive Networks

Barnabás Póczos, Eric P. Xing, Ruslan Salakhutdinov, 2018, ICML.

Recurrent Estimation of Distributions pdf

Barnabás Póczos, Eric P. Xing, Jeff G. Schneider, 2017, ArXiv.

Mapping the Cosmological Confidence Ball Surface

L. Wasserman, J. Schneider, C. Genovese, 2007, 0704.2605.

Maximum Margin Output Coding

Jeff G. Schneider, Yi Zhang, J. Schneider, 2012, ICML.

Offline Contextual Bayesian Optimization for Nuclear Fusion pdf

Kirthevasan Kandasamy, Andrew, Willie Neiswanger, 2020, ArXiv.

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

J. Schneider, I. Char, 2023, arXiv.org.

Event Prediction With Learning Algorithms-A Study of Events Surrounding the Egyptian Revolution of 2011 on the Basis of Micro Blog Data

Margeret Hall, Jeff Schneider, Benedikt Boecking, 2015 .

Learning Temporally AbstractWorld Models without Online Experimentation

J. Schneider, H. Choset, Guillaume Sartoretti, 2023, ICML.

Learning Specific Detectors of Adverse Events in Multivariate Time Series

J. Schneider, A. Dubrawski, J. Roure, 2007 .

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

Martin A. Riedmiller, A. Moore, J. Schneider, 2000, Balancing Reactivity and Social Deliberation in Multi-Agent Systems.

Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning pdf

John M. Dolan, Jeff Schneider, Priyantha Mudalige, 2019, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Enhancing Visual Domain Adaptation with Source Preparation

J. Schneider, C. Mertz, Anurag Ghosh, 2023, arXiv.org.

Detecting damped Ly α absorbers with Gaussian processes

Roman Garnett, Jeff Schneider, Shirley Ho, 2016, 1605.04460.

Tractable group detection on large link data sets

Andrew W. Moore, Jeff G. Schneider, Jeremy Kubica, 2003, Third IEEE International Conference on Data Mining.

The Multi-fidelity Multi-armed Bandit

Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider, 2016, NIPS.

Active Search and Bandits on Graphs using Sigma-Optimality

Jeff G. Schneider, Yifei Ma, Tzu-Kuo Huang, 2015, UAI.

Multi-Agent Active Search using Detection and Location Uncertainty

J. Schneider, Ramina Ghods, Arundhati Banerjee, 2022, 2023 IEEE International Conference on Robotics and Automation (ICRA).

Interactive Visualization for Debugging RL pdf

Jeff Schneider, Benjamin Eysenbach, Shuby Deshpande, 2020, ArXiv.

Vizarel: A System to Help Better Understand RL Agents pdf

Jeff Schneider, Shuby Deshpande, J. Schneider, 2020, ArXiv.

A statistical approach to 3d object detection applied to faces and cars

Takeo Kanade, Henry Schneiderman, Teck Khim Ng, 2000 .

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

Andrew W. Moore, Jeff G. Schneider, Jeremy Kubica, 2003, ICML.

Machine Learning methods for interatomic potentials: application to boron carbide

J. Schneider, M. Widom, Q. Gao, 2015, 1512.09110.

Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving

J. Schneider, Henggang Cui, Nemanja Djuric, 2018, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

Nonparametric inference in astrophysics

L. Wasserman, A. Moore, J. Schneider, 2001 .

Searching for Complex Patterns Using Disjunctive Anomaly Detection

Artur Dubrawski, Maheshkumar Sabhnani, Jeff Schneider, 2013, Online Journal of Public Health Informatics.

Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading

Edward W. Sun, J. Schneider, Bo Wu, 2023, ArXiv.

When is Deep Learning the Best Approach to Knowledge Tracing

Tom Mitchell, Ken Koedinger, Jeff Schneider, 2020 .

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

J. Schneider, I. Char, 2023, NeurIPS.

Learning Opportunity Costs in Multi-Robot Market Based Planners

Reid G. Simmons, Jeff G. Schneider, J. Andrew Bagnell, 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

Classification of Stellar Spectra with LLE

Andrew J. Connolly, Jeff Schneider, Scott F. Daniel, 2011, 1110.4646.