Jeff Schneider: Publications

• K Kandasamy, A Krishnamurthy, J Schneider, B Poczos, Asynchronous parallel Bayesian optimisation via thompson sampling, AISTATS, 2018.
• K. Kandasamy, G. Dasarathy, J. Schneider, B. Poczos, Multi-fidelity Bayesian Optimisation with Continuous Approximations, International Conference on Machine Learning (ICML), 2017
• S. Ravanbaksh, J. Schneider, B. Poczos, Equivariance Through Parameter-Sharing, International Conference on Machine Learning (ICML), 2017.
• J. Oliva, B. Poczos, J. Schneider,The Statistical Recurrent Unit, International Conference on Machine Learning (ICML), 2017.
• R. Garnett, S. Ho, S. Bird, J. Schneider, Detecting damped Ly alpha absorbers with Gaussian processes, Monthly Notices of the Royal Astronomical Society (MNRAS) 472 (2), 1850-1865, 2017.
• K. Kandasamy, J. Schneider, B. Poczos, Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning, Artificial Intelligence Journal (AIJ) 2017.
• S. Ravanbakhsh, F. Lanusse, R. Mandelbaum, J. Schneider, B. Poczos, Enabling Dark Energy Science with Deep Generative Models of Galaxy Images, AAAI, 2017.
• Y. Ma, R. Garnett, J. Schneider, Active Search for Sparse Signals with Region Sensing, AAAI, 2017.
• M Ntampaka, H Trac, DJ Sutherland, S Fromenteau, B Póczos, J. Schneider, Dynamical mass measurements of contaminated galaxy clusters using machine learning, The Astrophysical Journal 831 (2), 135, 2016.
• K. Kandasamy, G. Dasarathy, J. Oliva, J. Schneider, B. Poczos, Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations, Advances in Neural Information Processing Systems (NIPS), 2016.
• K. Kandasamy, G. Dasarathy, J. Schneider, B. Poczos, The Multi-fidelity Multi-armed Bandit, Advances in Neural Information Processing Systems (NIPS), 2016.
• S. Ravanbakhsh, J. Oliva, S. Fromenteau, L. Price, S. Ho, J. Schneider, B. Póczos, Estimating cosmological parameters from the dark matter distribution, International Conference on Machine Learning (ICML), 2016.
• J. Oliva, A. Dubey, A. Wilson, B. Poczos, J. Schneider, E. Xing, Bayesian Nonparametric Kernel-Learning, International Conference on AI and Statistics (AISTATS), 2016.
• S. Ravanbakhsh, B. Poczos, J.Schneider, D. Schuurmans, R. Greiner, Stochastic Neural Networks with Monotonic Activation Functions, International Conference on Artificial Intelligence and Statistics(AISTATS), 2016.
• C. Li, K. Kandasamy, B. Poczos, J. Schneider, High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models, International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.
• X. Wang, J. Oliva, J. Schneider, B. Póczos, Nonparametric risk and stability analysis for multi-task learning problems, International Joint Conference on Artificial Intelligence (IJCAI), 2016.
• M. Ntampaka, H. Trac, D. Sutherland, N. Battaglia, B. Póczos, J. Schneider, A machine learning approach for dynamical mass measurements of galaxy clusters, The Astrophysical Journal 803 (2), 50, 2015.
• R. Garnett, S. Ho, J. Schneider, Finding galaxies in the shadows of quasars with Gaussian processes, International Conference on Machine Learning (ICML), 2015.
• K. Kandasamy, J. Schneider, B. Poczos, High Dimensional Bayesian Optimisation and Bandits via Additive Models, International Conference on Machine Learning (ICML), 2015.
• D. Sutherland, J. Oliva, B. Poczos, J. Schneider, Linear-Time Learning on Distributions with Approximate Kernel Embeddings, AAAI, 2015.
• J. Oliva, W. Neiswanger, B. Poczos, E. Xing, J. Schneider, Fast Function to Function Regression, International Conference on AI and Statistics (AISTATS), 2015.
• B. Boecking, M. Hall, J. Schneider, Event prediction with learning algorithms - A study of events surrounding the Egyptian revolution of 2011 on the basis of micro blog data, Policy and Internet, 7 (2), 159-184, 2015.
• K. Kandasamy, J. Schneider, B. Poczos, Bayesian Active Learning for Posterior Estimation, (best paper) International Joint Conference on Artificial Intelligence (IJCAI), 2015.
• X. Wang, J. Schneider, Generalization Bounds for Transfer Learning under Model Shift, Uncertainty in Artificial Intelligence (UAI), 2015.
• Y. Ma, T. Huang, J. Schneider, Active Search and Bandits on Graphs using Sigma-Optimality, Uncertainty in Artificial Intelligence (UAI), 2015.
• D. Sutherland and J. Schneider, On the Error of Random Fourier Features, Uncertainty in Artificial Intelligence (UAI), 2015.
• Y. Ma, D. Sutherland, R. Garnett, J. Schneider, Active pointillistic pattern search, Artificial Intelligence and Statistics (AISTATS), 672-680, 2015.
• X. Wang, J. Schneider, Flexible Transfer Learning under Support and Model Shift, Neural Information Processing Systems (NIPS)}, 2014.
• X. Wang, T. Huang, J. Schneider, Active Transfer Learning under Model Shift, International Conference on Machine Learning (ICML), 2014.
• Y. Ma, R. Garnett, J. Schneider, Active Area Search via Bayesian Quadrature, Artificial Intelligence and Statistics (AISTATS), 2014.
• J. Oliva, W. Neiswanger, B. Poczos, J. Schneider, E. XingFast Distribution To Real Regression, Artificial Intelligence and Statistics (AISTATS), 2014.
• J. Oliva, B. Poczos, T. Verstynen, A. Singh, J. SchneiderFuSSO: Functional Shrinkage and Selection Operator, Artificial Intelligence and Statistics (AISTATS), 2014.
• L. Xiong, J. SchneiderLearning from Point Sets with Observational Bias, Uncertainty in Artificial Intelligence (UAI), 2014.
• X. Xu, S. Ho, H. Trac, J. Schneider, B. Poczos, M. NtampakaA first look at creating mock catalogs with machine learning techniques, The Astrophysical Journal, 772, 147, 2014.
• J. Oliva, B. Poczos, J. SchneiderDistribution to Distribution Regression, International Conference on Machine Learning (ICML), 2013.
• T. Huang, J. SchneiderSpectral Learning of Hidden Markov Models from Dynamic and Static Data, International Conference on Machine Learning (ICML), 2013.
• X. Wang, R. Garnett, J. SchneiderActive Search on Graphs, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2013.
• D. Sutherland, B. Poczos, J. SchneiderActive Learning and Search on Low-Rank Matrices, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2013.
• Y. Ma, R. Garnett, J. SchneiderSigma-Optimality in Active Learning on Gaussian Random Fields, Neural Information Processing Systems (NIPS), 2013.
• T. Huang, J. SchneiderLearning Hidden Markov Models from Non-sequence Data via Tensor Decomposition, Neural Information Processing Systems (NIPS), 2013.
• L.Xiong, J. SchneiderEfficient Learining on Point Sets, IEEE International Conference on Data Mining (ICDM), 2013.
• B. Poczos, Z. Ghahramani, J. Schneider, Copula-based Kernel Dependency Measures, International Conference on Machine Learning (ICML), 2012.
• R. Garnett, Y. Krishnamurthy, X. Xiong, J. Schneider, R. Mann, Bayesian Optimal Active Search and Surveying, International Conference on Machine Learning (ICML), 2012.
• Y. Zhang, J. Schneider, Maximum Margin Output Coding, International Conference on Machine Learning (ICML), 2012.
• B. Poczos, J. Schneider, Nonparametric Estimation of Conditional Information and Divergences, AISTATS, 2012.
• Y. Zhang, J. Schneider, A Composite Likelihood View for Multi-Label Classification, AISTATS, 2012.
• B. Poczos, L. Xiong, D. Sutherland, J. Schneider,Nonparametric Kernel Estimators for Image Classification, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
• T. Huang, J. Schneider, Learning Bi-clustered Vector Autoregressive Models, European Conference on Machine Learning (ECML), 2012.
• X. Wang, R. Garnett, J. Schneider, An Impact Criterion for Active Graph Search, NIPS workshop on Bayesian Optimization and Decision Making, 2012.
• T. Huang, J. Schneider, Learning Auto-regressive Models from Sequence and Non-sequence Data, Neural Information Processing Systems (NIPS), 2011.
• L. Xiong, B. Poczos, J. Schneider, Group Anomaly Detection using Flexible Genre Models, Neural Information Processing Systems (NIPS), 2011.
• S. Daniel, A. Connolly, J. Schneider, J. VanderPlas, L. Xiong, Classification of Stellar Spectra with Local Linear Embedding, Astronomical Journal, 142:203, 2011.
• B. Poczos, L. Xiong, J. Schneider, Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions, Uncertainty in Artificial Intelligence (UAI), 2011.
• B. Poczos, J. Schneider, On the Estimation of Alpha-Divergences, Artificial Intelligence and Statistics (AISTATS), 2011.
• L. Xiong, B. Poczos, J. Schneider, A. Connolly, J. VanderPlasHierarchical Probabilistic Models for Group Anomaly Detection, Artificial Intelligence and Statistics (AISTATS), 2011.
• Y. Zhang, J. Schneider, Multi-Label Output Codes using Canonical Correlation Analysis, Artificial Intelligence and Statistics (AISTATS), 2011.
• M. Tesch, J. Schneider, H. ChosetAdapting Control Policies for Expensive Systems to Changing Environments, IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2011.
• M. Tesch, J. Schneider, H. ChosetUsing Response Surfaces and Expected Improvement to Optimize Snake Robot Gait Parameters, IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2011.
• B. Poczos, Z. Szabo, J. Schneider, Nonparametric Divergence Estimators for Independent Subspace Analysis, European Signal Processing Conference (EUSIPCO), 2011.
• Y. Zhang, J. Schneider, Learning Multiple Tasks with a Sparse Matrix-Normal Penalty, Neural Information Processing Systems (NIPS), 2010.
• Y. Zhang, J. Schneider, Projection Penalties: Dimension Reduction without Loss, International Conference on Machine Learning (ICML), 2010.
• T. Huang, L. Song, J. Schneider, Learning Nonlinear Dynamic Models from Non-sequenced Data, Artificial Intelligence and Statistics (AISTATS), 2010.
• L. Xiong, X. Chen, T. Huang, J. Schneider, J. Carbonell, Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization, SIAM Data Mining (SDM), 2010.
• Y. Zhang, J. Schneider, A. Dubrawski, Learning Compressible Models, SIAM Data Mining (SDM), 2010.
• P. Donmez, J. Carbonell, J. Schneider, Efficiently Learning the Accuracy of Labeling Sources for Selective Sampling, SIAM Data Mining (SDM), 2010.
• P. Donmez, J. Carbonell, J. Schneider, A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy, International Conference on Knowledge Discovery and Data Mining (KDD), 2009.
• T. Huang, J. Schneider, Learning Linear Dynamical Systems without Sequence Information, International Conference on Machine Learning (ICML), 2009.
• Y. Zhang, J. Schneider, A. Dubrawski, Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text, Neural Information Processing Systems (NIPS), 2008.
• B. Bryan, J. Schneider, Actively Learning Level-Sets of Composite Functions, International Conference on Machine Learning (ICML), 2008.
• R. Houghten, C. Pinilla, M Giulianotti, J. Appel, C. Dooley, A. Nefzi, J. Ostresh, Y. Yu, G. Maggiora, J. Medina-Franco, D. Brunner, J. Schneider, Strategies for the Use of Mixture-Based Synthetic Combinatorial Libraries: Scaffold Ranking, Direct Testing In Vivo, and Enhanced Deconvolution by Computational Methods, Journal of Combinatorial Chemistry, 10 (1), 3-19, 2008.
• B. Bryan, J. Schneider, C. Miller, R. Nichol, C. Genovese, L. Wasserman, "Mapping the Cosmological Confidence Ball Surface", Astrophysical Journal, 665(1), 25-41, 2007.
• B. Bryan, B. McMahan, C. Schafer, J. Schneider,Efficiently Computing Minimax Expected-Size Confidence Regions, International Conference on Machine Learning (ICML), 2007.
• J. Roure, A. Dubrawski, J. SchneiderA Study into Detection of Bio-Events in Multiple Streams of Surveillance Data, Intelligence and Security Informatics: Biosurveillance, 2007.
• K. Das, J. Schneider,Detecting Anomalous Records in Categorical Datasets, International Conference on Knowledge Discovery and Data Mining (KDD), 2007.
• B. Bryan, L. Wasserman, J. Schneider, R. Nichol, C. Miller, C. Genovese, Active Learning for Identifying Function Threshold Boundaries, Neural Information Processing Systems (NIPS), 2005.
• R. Emery-Montemerlo, G. Gordon, J. Schneider, S. Thrun, Game Theoretic Control for Robot Teams, International Conference on Robotics and Automation, 2005.
• J. Schneider, D. Apfelbaum, D. Bagnell, R. Simmons,Learning Opportunity Costs in Multi-Robot Market Based Planners, International Conference on Robotics and Automation, 2005.
• P. Hsiung, A. Moore, D. Neill, J. Schneider,Alias Detection in Link Data Sets, International Conference on Intelligence Analysis, 2005.
• S. Baker, I. Matthews, J. Schneider,Automatic Construction of Active Appearance Models as an Image Coding Problem, IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 26, no. 10, 2004.
• K. Das, A. Moore, J. Schneider,Belief state approaches to signaling alarms in surveillance systems, ACM International Conference on Knowledge Discovery and Data Mining (KDD), 2004.
• Rosemary Emery-Montemerlo, Geoff Gordon, Jeff Schneider, Sebastian Thrun,Approximate Solutions for Partially Observable Stochastic Games with Common Payoffs, Autonomous Agents and Multi-Agent Systems (AAMAS), 2004.
• Y. Liu, N. Lazar, W. Rothfus, F. Dellaert, A. Moore, J. Schneider, T. Kanade, Semantic based Biomedical Image Indexing and Retrieval, Trends and Advances in Content-Based Image and Video Retrieval, Shapiro, Kriegel, and Veltkamp, ed., 2004.
• Drew Bagnell, Sham Kakade, Andrew Ng, Jeff Schneider,Policy Search by Dynamic Programming, Proceedings of Neural Information Processing Systems (NIPS), 2003.
• J. A. Bagnell, J. Schneider, Covariant Policy Search, International Joint Conference on Artificial Intelligence (IJCAI), 2003.
• Jeremy Kubica, Andrew Moore, Jeff Schneider,Tractable Group Detection on Large Link Data Sets,The Third IEEE International Conference on Data Mining, 2003.
• Jeremy Kubica, Andrew Moore, David Cohn, Jeff Schneider,cGraph: A Fast Graph-Based Method for Link Analysis and Queries,Proceedings of the 2003 IJCAI Text-Mining & Link-Analysis Workshop, 2003.
• Anna Goldenberg, Jeremy Kubica, Paul Komarek, Andrew Moore, Jeff Schneider,A Comparison of Statistical and Machine Learning Algorithms on the Task of Link Completion, KDD Workshop on Link Analysis for Detecting Complex Behavior, 2003.
• J. Kubica, A. Moore, D. Cohn, J. Schneider, Finding Underlying Connections: A Fast Method for Link Analysis and Collaboration Queries, International Conference on Machine Learning (ICML), 2003.
• J. Schneider, A. Moore, Active Learning in Discrete Input Spaces, The 34th Interface Symposium, Montreal, Quebec, Apr 17-20, 2002.
• A. Moore, J. Schneider, Real-valued All-Dimensions search: Low-overhead rapid searching over subsets of attributes, Conference of Uncertainty in Artificial Intelligence (UAI), 2002.
• J. Kubica, A. Moore, J. Schneider, Y. Yang, Stochastic Link and Group Detection, Eighteenth National Conference on Artificial Intelligence (AAAI), 2002.
• C. Miller, C. Genovese, R. Nichol, L. Wasserman, A. Connolly, D. Reichart, A. Hopkins, J. Schneider, A. Moore,Controlling the False Discovery Rate in Astrophysical Data Analysis,Astronomical Journal,122,6,3492-3505,2001.
• Y. Liu, F. Dellaert, W.E. Rothfus, A. Moore, J. Schneider, T. Kanade Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures (0.6MB gzipped) Proceedings of the International Conference of Medical Image Computing and Computer Assisted Intervention (MICCAI 2001), October 14-17, 2001.
• M. Riedmiller, A. Moore, J. Schneider, Reinforcement Learning for Cooperating and Communicating Reactive Agents in Electrical Power Grids in Balancing Reactivity and Social Deliberation in Multi-agent Systems, edited by M. Hannebauer, J. Wendler, E. Pagello, Springer, 2001
• J. Andrew Bagnell, Jeff Schneider Autonomous Helicopter Control using Reinforcement Learning Policy Search Methods International Conference on Robotics and Automation, 2001
• Jeff Schneider, Weng-Keen Wong, Andrew Moore, Martin Riedmiller Distributed Value Functions, International Conference on Machine Learning, 1999
• Mei Chen, Takeo Kanade, Dean Pomerleau, Jeff Schneider, 3-D Deformable Registration of Medical Images Using a Statistical Atlas (1.6MB gzipped), Second International Conference on Medical Image Computing and Computer-Assisted Intervention, 1999
• Jeff Schneider, Justin Boyan, Andrew Moore, Value Function Based Production Scheduling, International Conference on Machine Learning, 1998
• Andrew Moore, Jeff Schneider, Justin Boyan, Mary Lee, Q2: Memory-based active learning for optimizing noisy continuous functions, International Conference on Machine Learning, 1998
• Andrew Moore, Jeff Schneider, Kan Deng, Efficient Locally Weighted Polynomial Regression Predictions, International Conference on Machine Learning, 1997
• Jeff G. Schneider, Exploiting Model Uncertainty Estimates for Safe Dynamic Control Learning, Neural Information Processing Systems 9 (NIPS), 1996
• Jeff G. Schneider, Active Learning on Non-Stationary Functions, Working Notes of the AAAI Fall Symposium on Active Learning, 1995
• Andrew W. Moore and Jeff G. Schneider, Memory-based Stochastic Optimization, Neural Information Processing Systems 8 (NIPS), 1995
• Jeff G. Schneider, Robot Skill Learning Through Intelligent Experimentation, PhD Thesis, University of Rochester, 1995
• Jeff G. Schneider and Christopher M. Brown, Cooperative Coaching in Robot Learning, International Conference on Intelligent Robots and Systems, 1995
• Jeff G. Schneider and Roger F. Gans, Efficient Search for Robot Skill Learning: Simulation and Reality, International Conference on Intelligent Robots and Systems, 1994