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ORCIDJeff G. Schneider
Jeff Schneider 0001
Person information
- affiliation: Carnegie Mellon University, The Robotics Institute
Other persons with the same name
- Jeff Schneider — disambiguation page
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Journal Articles
- 2022
[j7]Conor Igoe
, Ramina Ghods, Jeff Schneider:
Multi-Agent Active Search: A Reinforcement Learning Approach. IEEE Robotics Autom. Lett. 7(2): 754-761 (2022)- [j6]Benjamin Freed, Aditya Kapoor, Ian Abraham, Jeff G. Schneider, Howie Choset:
Learning Cooperative Multi-Agent Policies With Partial Reward Decoupling. IEEE Robotics Autom. Lett. 7(2): 890-897 (2022) - 2020
- [j5]Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. J. Mach. Learn. Res. 21: 81:1-81:27 (2020) - 2019
- [j4]Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos:
Multi-fidelity Gaussian Process Bandit Optimisation. J. Artif. Intell. Res. 66: 151-196 (2019) - 2017
- [j3]Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos:
Query efficient posterior estimation in scientific experiments via Bayesian active learning. Artif. Intell. 243: 45-56 (2017) - 2012
- [j2]Jieyue Li, Liang Xiong, Jeff G. Schneider, Robert F. Murphy:
Protein subcellular location pattern classification in cellular images using latent discriminative models. Bioinform. 28(12): 32-39 (2012) - 2004
- [j1]Simon Baker, Iain A. Matthews, Jeff G. Schneider:
Automatic Construction of Active Appearance Models as an Image Coding Problem. IEEE Trans. Pattern Anal. Mach. Intell. 26(10): 1380-1384 (2004)
Conference and Workshop Papers
- 2023
- [c126]Nikhil Angad Bakshi, Jeff Schneider:
Stealthy Terrain-Aware Multi-Agent Active Search. CoRL 2023: 1782-1796 - [c125]Arundhati Banerjee, Ramina Ghods, Jeff Schneider:
Cost-Awareness in Multi-Agent Active Search. ECAI 2023: 182-189 - [c124]Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic:
Near-optimal Policy Identification in Active Reinforcement Learning. ICLR 2023 - [c123]Benjamin Freed, Siddarth Venkatraman, Guillaume Adrien Sartoretti, Jeff Schneider, Howie Choset:
Learning Temporally AbstractWorld Models without Online Experimentation. ICML 2023: 10338-10356 - [c122]Conor Igoe, Swapnil Pande, Siddarth Venkatraman, Jeff G. Schneider:
Multi-Alpha Soft Actor-Critic: Overcoming Stochastic Biases in Maximum Entropy Reinforcement Learning. ICRA 2023: 7162-7168 - [c121]Arundhati Banerjee, Ramina Ghods, Jeff G. Schneider:
Multi-Agent Active Search using Detection and Location Uncertainty. ICRA 2023: 7720-7727 - [c120]Nikhil Angad Bakshi, Tejus Gupta, Ramina Ghods, Jeff G. Schneider:
GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search. ICRA 2023: 7735-7741 - [c119]Ian Char, Joseph Abbate, Laszlo Bardoczi, Mark D. Boyer, Youngseog Chung, Rory Conlin, Keith Erickson, Viraj Mehta, Nathan Richner, Egemen Kolemen, Jeff G. Schneider:
Offline Model-Based Reinforcement Learning for Tokamak Control. L4DC 2023: 1357-1372 - 2022
- [c118]Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger:
An Experimental Design Perspective on Model-Based Reinforcement Learning. ICLR 2022 - [c117]Adam R. Villaflor, Zhe Huang, Swapnil Pande, John M. Dolan, Jeff Schneider:
Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning. ICML 2022: 22270-22283 - [c116]Yeeho Song, Jeff Schneider:
Robust Reinforcement Learning via Genetic Curriculum. ICRA 2022: 5560-5566 - [c115]Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark D. Boyer, Stefano Ermon, Jeff Schneider, Willie Neiswanger:
Exploration via Planning for Information about the Optimal Trajectory. NeurIPS 2022 - [c114]Divam Gupta, Wei Pu, Trenton Tabor, Jeff Schneider:
SBEVNet: End-to-End Deep Stereo Layout Estimation. WACV 2022: 667-676 - 2021
- [c113]Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff G. Schneider:
Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction. CDC 2021: 3735-3742 - [c112]Zhiqian Qiao, Jeff Schneider, John M. Dolan:
Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning*. ICRA 2021: 2667-2673 - [c111]Ramina Ghods, William J. Durkin, Jeff Schneider:
Multi-Agent Active Search using Realistic Depth-Aware Noise Model. ICRA 2021: 9101-9108 - [c110]Tanmay Agarwal, Hitesh Arora, Jeff Schneider:
Learning Urban Driving Policies using Deep Reinforcement Learning. ITSC 2021: 607-614 - [c109]Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider:
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. NeurIPS 2021: 10971-10984 - [c108]Ramina Ghods, Arundhati Banerjee, Jeff Schneider:
Decentralized multi-agent active search for sparse signals. UAI 2021: 696-706 - 2020
- [c107]Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing:
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. AISTATS 2020: 3393-3403 - [c106]Zhiqian Qiao, Jing Zhao, Jin Zhu, Zachariah Tyree, Priyantha Mudalige, Jeff Schneider, John M. Dolan:
Human Driver Behavior Prediction based on UrbanFlow*. ICRA 2020: 10570-10576 - [c105]Zhiqian Qiao, Zachariah Tyree, Priyantha Mudalige, Jeff Schneider, John M. Dolan:
Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning. IROS 2020: 6084-6089 - 2019
- [c104]Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos:
Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments. ICML 2019: 3222-3232 - 2018
- [c103]Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos:
Parallelised Bayesian Optimisation via Thompson Sampling. AISTATS 2018: 133-142 - [c102]Junier B. Oliva, Avinava Dubey, Manzil Zaheer, Barnabás Póczos, Ruslan Salakhutdinov, Eric P. Xing, Jeff Schneider:
Transformation Autoregressive Networks. ICML 2018: 3895-3904 - [c101]Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Neural Architecture Search with Bayesian Optimisation and Optimal Transport. NeurIPS 2018: 2020-2029 - 2017
- [c100]Siamak Ravanbakhsh, François Lanusse, Rachel Mandelbaum, Jeff G. Schneider, Barnabás Póczos:
Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. AAAI 2017: 1488-1494 - [c99]Yifei Ma, Roman Garnett, Jeff G. Schneider:
Active Search for Sparse Signals with Region Sensing. AAAI 2017: 2315-2321 - [c98]Jeff G. Schneider:
Active Optimization and Self Driving Cars. AAMAS 2017: 3 - [c97]Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos:
Deep Learning with Sets and Point Clouds. ICLR (Workshop) 2017 - [c96]Kirthevasan Kandasamy, Gautam Dasarathy, Jeff G. Schneider, Barnabás Póczos:
Multi-fidelity Bayesian Optimisation with Continuous Approximations. ICML 2017: 1799-1808 - [c95]Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider:
The Statistical Recurrent Unit. ICML 2017: 2671-2680 - [c94]Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos:
Equivariance Through Parameter-Sharing. ICML 2017: 2892-2901 - [c93]Sibi Venkatesan, James Kyle Miller, Jeff Schneider, Artur Dubrawski:
Scaling Active Search using Linear Similarity Functions. IJCAI 2017: 2878-2884 - 2016
- [c92]Danica J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider:
Linear-Time Learning on Distributions with Approximate Kernel Embeddings. AAAI 2016: 2073-2079 - [c91]Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner:
Stochastic Neural Networks with Monotonic Activation Functions. AISTATS 2016: 809-818 - [c90]Chun-Liang Li, Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider:
High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models. AISTATS 2016: 884-892 - [c89]Junier B. Oliva, Avinava Dubey, Andrew Gordon Wilson, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing:
Bayesian Nonparametric Kernel-Learning. AISTATS 2016: 1078-1086 - [c88]Siamak Ravanbakhsh, Junier B. Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff G. Schneider, Barnabás Póczos:
Estimating Cosmological Parameters from the Dark Matter Distribution. ICML 2016: 2407-2416 - [c87]Xuezhi Wang, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos:
Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems. IJCAI 2016: 2146-2152 - [c86]Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos:
Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations. NIPS 2016: 992-1000 - [c85]Kirthevasan Kandasamy, Gautam Dasarathy, Barnabás Póczos, Jeff G. Schneider:
The Multi-fidelity Multi-armed Bandit. NIPS 2016: 1777-1785 - 2015
- [c84]Yifei Ma, Danica J. Sutherland, Roman Garnett, Jeff G. Schneider:
Active Pointillistic Pattern Search. AISTATS 2015 - [c83]Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Hy Trac, Shirley Ho, Jeff G. Schneider:
Fast Function to Function Regression. AISTATS 2015 - [c82]Ronald D. Blanton, Xin Li, Ken Mai, Diana Marculescu, Radu Marculescu, Jeyanandh Paramesh, Jeff G. Schneider, Donald E. Thomas:
Statistical Learning in Chip (SLIC). ICCAD 2015: 664-669 - [c81]Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos:
High Dimensional Bayesian Optimisation and Bandits via Additive Models. ICML 2015: 295-304 - [c80]Roman Garnett, Shirley Ho, Jeff G. Schneider:
Finding Galaxies in the Shadows of Quasars with Gaussian Processes. ICML 2015: 1025-1033 - [c79]Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos:
Bayesian Active Learning for Posterior Estimation - IJCAI-15 Distinguished Paper. IJCAI 2015: 3605-3611 - [c78]Yifei Ma, Tzu-Kuo Huang, Jeff G. Schneider:
Active Search and Bandits on Graphs using Sigma-Optimality. UAI 2015: 542-551 - [c77]Danica J. Sutherland, Jeff G. Schneider:
On the Error of Random Fourier Features. UAI 2015: 862-871 - [c76]Xuezhi Wang, Jeff G. Schneider:
Generalization Bounds for Transfer Learning under Model Shift. UAI 2015: 922-931 - 2014
- [c75]Yifei Ma, Roman Garnett, Jeff G. Schneider:
Active Area Search via Bayesian Quadrature. AISTATS 2014: 595-603 - [c74]Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing:
Fast Distribution To Real Regression. AISTATS 2014: 706-714 - [c73]Junier B. Oliva, Barnabás Póczos, Timothy D. Verstynen, Aarti Singh, Jeff G. Schneider, Fang-Cheng Yeh, Wen-Yih Isaac Tseng:
FuSSO: Functional Shrinkage and Selection Operator. AISTATS 2014: 715-723 - [c72]Xuezhi Wang, Tzu-Kuo Huang, Jeff G. Schneider:
Active Transfer Learning under Model Shift. ICML 2014: 1305-1313 - [c71]Guillermo F. Cabrera, Christopher J. Miller, Jeff G. Schneider:
Systematic Labeling Bias: De-biasing Where Everyone is Wrong. ICPR 2014: 4417-4422 - [c70]Ronald D. Blanton, Xin Li, Ken Mai, Diana Marculescu, Radu Marculescu, Jeyanandh Paramesh, Jeff G. Schneider, Donald E. Thomas:
SLIC: Statistical learning in chip. ISIC 2014: 119-123 - [c69]Xuezhi Wang, Jeff G. Schneider:
Flexible Transfer Learning under Support and Model Shift. NIPS 2014: 1898-1906 - [c68]Liang Xiong, Jeff G. Schneider:
Learning from Point Sets with Observational Bias. UAI 2014: 898-906 - 2013
- [c67]Liang Xiong, Barnabás Póczos, Jeff G. Schneider:
Efficient Learning on Point Sets. ICDM 2013: 847-856 - [c66]Tzu-Kuo Huang, Jeff G. Schneider:
Spectral Learning of Hidden Markov Models from Dynamic and Static Data. ICML (3) 2013: 630-638 - [c65]Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider:
Distribution to Distribution Regression. ICML (3) 2013: 1049-1057 - [c64]Matthew Tesch, Jeff G. Schneider, Howie Choset:
Expensive Function Optimization with Stochastic Binary Outcomes. ICML (3) 2013: 1283-1291 - [c63]Matthew Tesch, Jeff G. Schneider, Howie Choset:
Expensive multiobjective optimization for robotics. ICRA 2013: 973-980 - [c62]Danica J. Sutherland, Barnabás Póczos, Jeff G. Schneider:
Active learning and search on low-rank matrices. KDD 2013: 212-220 - [c61]Xuezhi Wang, Roman Garnett, Jeff G. Schneider:
Active search on graphs. KDD 2013: 731-738 - [c60]Tzu-Kuo Huang, Jeff G. Schneider:
Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition. NIPS 2013: 333-341 - [c59]Yifei Ma, Roman Garnett, Jeff G. Schneider:
Σ-Optimality for Active Learning on Gaussian Random Fields. NIPS 2013: 2751-2759 - 2012
- [c58]Barnabás Póczos, Liang Xiong, Danica J. Sutherland, Jeff G. Schneider:
Nonparametric kernel estimators for image classification. CVPR 2012: 2989-2996 - [c57]Roman Garnett, Yamuna Krishnamurthy, Xuehan Xiong, Jeff G. Schneider, Richard P. Mann:
Bayesian Optimal Active Search and Surveying. ICML 2012 - [c56]Barnabás Póczos, Zoubin Ghahramani, Jeff G. Schneider:
Copula-based Kernel Dependency Measures. ICML 2012 - [c55]Yi Zhang, Jeff G. Schneider:
Maximum Margin Output Coding. ICML 2012 - [c54]Tzu-Kuo Huang, Jeff G. Schneider:
Learning Bi-clustered Vector Autoregressive Models. ECML/PKDD (2) 2012: 741-756 - [c53]Barnabás Póczos, Jeff G. Schneider:
Nonparametric Estimation of Conditional Information and Divergences. AISTATS 2012: 914-923 - [c52]Yi Zhang, Jeff G. Schneider:
A Composite Likelihood View for Multi-Label Classification. AISTATS 2012: 1407-1415 - 2011
- [c51]Barnabás Póczos, Zoltán Szabó, Jeff G. Schneider:
Nonparametric divergence estimators for independent subspace analysis. EUSIPCO 2011: 1718-1722 - [c50]Liang Xiong, Xi Chen, Jeff G. Schneider:
Direct Robust Matrix Factorizatoin for Anomaly Detection. ICDM 2011: 844-853 - [c49]Matthew Tesch, Jeff G. Schneider, Howie Choset:
Adapting control policies for expensive systems to changing environments. IROS 2011: 357-364 - [c48]Matthew Tesch, Jeff G. Schneider, Howie Choset:
Using response surfaces and expected improvement to optimize snake robot gait parameters. IROS 2011: 1069-1074 - [c47]Liang Xiong, Barnabás Póczos, Jeff G. Schneider:
Group Anomaly Detection using Flexible Genre Models. NIPS 2011: 1071-1079 - [c46]Tzu-Kuo Huang, Jeff G. Schneider:
Learning Auto-regressive Models from Sequence and Non-sequence Data. NIPS 2011: 1548-1556 - [c45]Barnabás Póczos, Liang Xiong, Jeff G. Schneider:
Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions. UAI 2011: 599-608 - [c44]Barnabás Póczos, Jeff G. Schneider:
On the Estimation of alpha-Divergences. AISTATS 2011: 609-617 - [c43]Liang Xiong, Barnabás Póczos, Jeff G. Schneider, Andrew J. Connolly, Jake VanderPlas:
Hierarchical Probabilistic Models for Group Anomaly Detection. AISTATS 2011: 789-797 - [c42]Yi Zhang, Jeff G. Schneider:
Multi-Label Output Codes using Canonical Correlation Analysis. AISTATS 2011: 873-882 - 2010
- [c41]Yi Zhang, Jeff G. Schneider:
Projection Penalties: Dimension Reduction without Loss. ICML 2010: 1223-1230 - [c40]Yi Zhang, Jeff G. Schneider:
Learning Multiple Tasks with a Sparse Matrix-Normal Penalty. NIPS 2010: 2550-2558 - [c39]Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff G. Schneider, Jaime G. Carbonell:
Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization. SDM 2010: 211-222 - [c38]Pinar Donmez, Jaime G. Carbonell, Jeff G. Schneider:
A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy. SDM 2010: 826-837 - [c37]Yi Zhang, Jeff G. Schneider, Artur Dubrawski:
Learning Compressible Models. SDM 2010: 872-881 - [c36]Tzu-Kuo Huang, Le Song, Jeff G. Schneider:
Learning Nonlinear Dynamic Models from Non-sequenced Data. AISTATS 2010: 350-357 - 2009
- [c35]Tzu-Kuo Huang, Jeff G. Schneider:
Learning linear dynamical systems without sequence information. ICML 2009: 425-432 - [c34]Pinar Donmez, Jaime G. Carbonell, Jeff G. Schneider:
Efficiently learning the accuracy of labeling sources for selective sampling. KDD 2009: 259-268 - 2008
- [c33]Josep Roure, Artur Dubrawski, Jeff G. Schneider:
Learning Detectors of Events in Multivariate Time Series. AMIA 2008 - [c32]Brent Bryan, Jeff G. Schneider:
Actively learning level-sets of composite functions. ICML 2008: 80-87 - [c31]Kaustav Das, Jeff G. Schneider, Daniel B. Neill:
Anomaly pattern detection in categorical datasets. KDD 2008: 169-176 - [c30]Yi Zhang, Jeff G. Schneider, Artur Dubrawski:
Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text. NIPS 2008: 1945-1952 - 2007
- [c29]Josep Roure, Artur Dubrawski, Jeff G. Schneider:
A Study into Detection of Bio-Events in Multiple Streams of Surveillance Data. BioSurveillance 2007: 124-133 - [c28]Brent Bryan, H. Brendan McMahan, Chad M. Schafer, Jeff G. Schneider:
Efficiently computing minimax expected-size confidence regions. ICML 2007: 97-104 - [c27]Kaustav Das, Jeff G. Schneider:
Detecting anomalous records in categorical datasets. KDD 2007: 220-229 - 2005
- [c26]Jeff G. Schneider, David Apfelbaum, Drew Bagnell, Reid G. Simmons:
Learning Opportunity Costs in Multi-Robot Market Based Planners. ICRA 2005: 1151-1156 - [c25]Rosemary Emery-Montemerlo, Geoffrey J. Gordon, Jeff G. Schneider, Sebastian Thrun:
Game Theoretic Control for Robot Teams. ICRA 2005: 1163-1169 - [c24]Brent Bryan, Jeff G. Schneider, Robert Nichol, Christopher J. Miller, Christopher R. Genovese, Larry A. Wasserman:
Active Learning For Identifying Function Threshold Boundaries. NIPS 2005: 163-170 - 2004
- [c23]Rosemary Emery-Montemerlo, Geoffrey J. Gordon, Jeff G. Schneider, Sebastian Thrun:
Approximate Solutions for Partially Observable Stochastic Games with Common Payoffs. AAMAS 2004: 136-143 - [c22]Kaustav Das, Andrew W. Moore, Jeff G. Schneider:
Belief state approaches to signaling alarms in surveillance systems. KDD 2004: 539-544 - 2003
- [c21]Jeremy Kubica, Andrew W. Moore, Jeff G. Schneider:
Tractable Group Detection on Large Link Data Sets. ICDM 2003: 573-576 - [c20]Jeremy Kubica, Andrew W. Moore, David Cohn, Jeff G. Schneider:
Finding Underlying Connections: A Fast Graph-Based Method for Link Analysis and Collaboration Queries. ICML 2003: 392-399 - [c19]J. Andrew Bagnell, Jeff G. Schneider:
Covariant Policy Search. IJCAI 2003: 1019-1024 - [c18]J. Andrew Bagnell, Sham M. Kakade, Andrew Y. Ng, Jeff G. Schneider:
Policy Search by Dynamic Programming. NIPS 2003: 831-838 - 2002
- [c17]Jeremy Kubica, Andrew W. Moore, Jeff G. Schneider, Yiming Yang:
Stochastic Link and Group Detection. AAAI/IAAI 2002: 798-806 - [c16]Andrew W. Moore, Jeff G. Schneider:
Real-valued All-Dimensions Search: Low-overhead Rapid Searching over Subsets of Attributes. UAI 2002: 360-369 - 2001
- [c15]J. Andrew Bagnell, Jeff G. Schneider:
Autonomous Helicopter Control using Reinforcement Learning Policy Search Methods. ICRA 2001: 1615-1620 - [c14]Yanxi Liu, Frank Dellaert, William E. Rothfus, Andrew W. Moore, Jeff G. Schneider, Takeo Kanade:
Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures. MICCAI 2001: 655-665 - 2000
- [c13]Martin A. Riedmiller, Andrew W. Moore, Jeff G. Schneider:
Reinforcement Learning for Cooperating and Communicating Reactive Agents in Electrical Power Grids. Balancing Reactivity and Social Deliberation in Multi-Agent Systems 2000: 137-149 - [c12]Andrew W. Moore, Jeff G. Schneider, Justin A. Boyan, Mary S. Lee:
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions. ICRA 2000: 4096- - 1999
- [c11]Jeff G. Schneider, Weng-Keen Wong, Andrew W. Moore, Martin A. Riedmiller:
Distributed Value Functions. ICML 1999: 371-378 - [c10]Mei Chen, Takeo Kanade, Dean Pomerleau, Jeff G. Schneider:
3-D Deformable Registration of Medical Images Using a Statistical Atlas. MICCAI 1999: 621-630 - 1998
- [c9]Andrew W. Moore, Jeff G. Schneider, Justin A. Boyan, Mary S. Lee:
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions. ICML 1998: 386-394 - [c8]Jeff G. Schneider, Justin A. Boyan, Andrew W. Moore:
Value Function Based Production Scheduling. ICML 1998: 522-530 - 1997
- [c7]Andrew W. Moore, Jeff G. Schneider, Kan Deng:
Efficient Locally Weighted Polynomial Regression Predictions. ICML 1997: 236-244 - 1996
- [c6]Jeff G. Schneider:
Exploiting Model Uncertainty Estimates for Safe Dynamic Control Learning. NIPS 1996: 1047-1053 - 1995
- [c5]Jeff G. Schneider, Christopher M. Brown:
Cooperative coaching in robot learning. IROS (3) 1995: 332-337 - [c4]Andrew W. Moore, Jeff G. Schneider:
Memory-based Stochastic Optimization. NIPS 1995: 1066-1072 - 1994
- [c3]Jeff G. Schneider:
High Dimension Action Spaces in Robot Skill Learning. AAAI 1994: 1272-1278 - [c2]Jeff G. Schneider, Roger F. Gans:
Efficient search for robot skill learning: simulation and reality. IROS 1994: 1256-1263 - 1993
- [c1]Jeff G. Schneider, Christopher M. Brown:
Robot Skill Learning, Basic Functions, and Control Regimes. ICRA (1) 1993: 403-410
Informal and Other Publications
- 2024
- [i59]Arundhati Banerjee, Jeff Schneider:
Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents. CoRR abs/2401.03154 (2024) - 2023
- [i58]Nikhil Angad Bakshi, Tejus Gupta, Ramina Ghods, Jeff Schneider:
GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search. CoRR abs/2304.02075 (2023) - [i57]Ian Char, Jeff G. Schneider:
PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks. CoRR abs/2307.05891 (2023) - [i56]Vikram Duvvur, Aashay Mehta, Edward Sun, Bo Wu, Ken Yew Chan, Jeff Schneider:
Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading. CoRR abs/2307.09377 (2023) - [i55]Viraj Mehta, Ojash Neopane, Vikramjeet Das, Sen Lin, Jeff Schneider, Willie Neiswanger:
Kernelized Offline Contextual Dueling Bandits. CoRR abs/2307.11288 (2023) - [i54]Siddarth Venkatraman, Shivesh Khaitan, Ravi Tej Akella, John M. Dolan, Jeff G. Schneider, Glen Berseth:
Reasoning with Latent Diffusion in Offline Reinforcement Learning. CoRR abs/2309.06599 (2023) - [i53]Nikhil Angad Bakshi, Jeff Schneider:
Stealthy Terrain-Aware Multi-Agent Active Search. CoRR abs/2310.10961 (2023) - [i52]Viraj Mehta, Vikramjeet Das, Ojash Neopane, Yijia Dai, Ilija Bogunovic, Jeff G. Schneider, Willie Neiswanger:
Sample Efficient Reinforcement Learning from Human Feedback via Active Exploration. CoRR abs/2312.00267 (2023) - 2022
- [i51]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:
UGV-UAV Object Geolocation in Unstructured Environments. CoRR abs/2201.05518 (2022) - [i50]Yeeho Song, Jeff Schneider:
Robust Reinforcement Learning via Genetic Curriculum. CoRR abs/2202.08393 (2022) - [i49]Arundhati Banerjee, Ramina Ghods, Jeff Schneider:
Multi-Agent Active Search using Detection and Location Uncertainty. CoRR abs/2203.04524 (2022) - [i48]Ian Char, Viraj Mehta, Adam Villaflor, John M. Dolan, Jeff Schneider:
BATS: Best Action Trajectory Stitching. CoRR abs/2204.12026 (2022) - [i47]Conor Igoe, Youngseog Chung, Ian Char, Jeff Schneider:
How Useful are Gradients for OOD Detection Really? CoRR abs/2205.10439 (2022) - [i46]Adam Villaflor, Zhe Huang, Swapnil Pande, John M. Dolan, Jeff Schneider:
Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning. CoRR abs/2207.10295 (2022) - [i45]Arundhati Banerjee, Ramina Ghods, Jeff Schneider:
Cost Aware Asynchronous Multi-Agent Active Search. CoRR abs/2210.02259 (2022) - [i44]Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark D. Boyer, Stefano Ermon, Jeff Schneider, Willie Neiswanger:
Exploration via Planning for Information about the Optimal Trajectory. CoRR abs/2210.04642 (2022) - [i43]Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic:
Near-optimal Policy Identification in Active Reinforcement Learning. CoRR abs/2212.09510 (2022) - 2021
- [i42]Tanmay Agarwal, Hitesh Arora, Jeff Schneider:
Affordance-based Reinforcement Learning for Urban Driving. CoRR abs/2101.05970 (2021) - [i41]Divam Gupta, Wei Pu, Trenton Tabor, Jeff Schneider:
SBEVNet: End-to-End Deep Stereo Layout Estimation. CoRR abs/2105.11705 (2021) - [i40]Youngseog Chung, Ian Char, Han Guo, Jeff Schneider, Willie Neiswanger:
Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification. CoRR abs/2109.10254 (2021) - [i39]Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger:
An Experimental Design Perspective on Model-Based Reinforcement Learning. CoRR abs/2112.05244 (2021) - [i38]Benjamin Freed, Aditya Kapoor, Ian Abraham, Jeff G. Schneider, Howie Choset:
Learning Cooperative Multi-Agent Policies with Partial Reward Decoupling. CoRR abs/2112.12740 (2021) - 2020
- [i37]Youngseog Chung, Ian Char, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff Schneider:
Offline Contextual Bayesian Optimization for Nuclear Fusion. CoRR abs/2001.01793 (2020) - [i36]Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff Schneider:
Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction. CoRR abs/2006.12682 (2020) - [i35]Ramina Ghods, Arundhati Banerjee, Jeff Schneider:
Asynchronous Multi Agent Active Search. CoRR abs/2006.14718 (2020) - [i34]Zhiqian Qiao, Jeff Schneider, John M. Dolan:
Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning. CoRR abs/2011.04697 (2020) - [i33]Ramina Ghods, William J. Durkin, Jeff Schneider:
Multi-Agent Active Search using Realistic Depth-Aware Noise Model. CoRR abs/2011.04825 (2020) - [i32]Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider:
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. CoRR abs/2011.09588 (2020) - 2019
- [i31]Willie Neiswanger, Kirthevasan Kandasamy, Barnabás Póczos, Jeff Schneider, Eric P. Xing:
ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization. CoRR abs/1901.11515 (2019) - [i30]Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. CoRR abs/1903.06694 (2019) - [i29]Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing:
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. CoRR abs/1908.01425 (2019) - [i28]Zhiqian Qiao, Zachariah Tyree, Priyantha Mudalige, Jeff Schneider, John M. Dolan:
Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning. CoRR abs/1911.03799 (2019) - [i27]Zhiqian Qiao, Jing Zhao, Zachariah Tyree, Priyantha Mudalige, Jeff Schneider, John M. Dolan:
Human Driver Behavior Prediction based on UrbanFlow. CoRR abs/1911.03801 (2019) - 2018
- [i26]Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Neural Architecture Search with Bayesian Optimisation and Optimal Transport. CoRR abs/1802.07191 (2018) - [i25]Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos:
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming. CoRR abs/1805.09964 (2018) - 2017
- [i24]Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos:
Equivariance Through Parameter-Sharing. CoRR abs/1702.08389 (2017) - [i23]Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider:
The Statistical Recurrent Unit. CoRR abs/1703.00381 (2017) - [i22]Sibi Venkatesan, James Kyle Miller, Jeff Schneider, Artur Dubrawski:
Scaling Active Search using Linear Similarity Functions. CoRR abs/1705.00334 (2017) - [i21]Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff G. Schneider, Barnabás Póczos:
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling. CoRR abs/1705.09236 (2017) - [i20]Junier B. Oliva, Kumar Avinava Dubey, Barnabás Póczos, Eric P. Xing, Jeff G. Schneider:
Recurrent Estimation of Distributions. CoRR abs/1705.10750 (2017) - [i19]Siamak Ravanbakhsh, Junier B. Oliva, Sebastian Fromenteau, Layne C. Price, Shirley Ho, Jeff G. Schneider, Barnabás Póczos:
Estimating Cosmological Parameters from the Dark Matter Distribution. CoRR abs/1711.02033 (2017) - 2016
- [i18]Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner:
Stochastic Neural Networks with Monotonic Activation Functions. CoRR abs/1601.00034 (2016) - [i17]Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos:
Multi-fidelity Gaussian Process Bandit Optimisation. CoRR abs/1603.06288 (2016) - [i16]Siamak Ravanbakhsh, François Lanusse, Rachel Mandelbaum, Jeff G. Schneider, Barnabás Póczos:
Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. CoRR abs/1609.05796 (2016) - [i15]Kirthevasan Kandasamy, Gautam Dasarathy, Jeff G. Schneider, Barnabás Póczos:
The Multi-fidelity Multi-armed Bandit. CoRR abs/1610.09726 (2016) - [i14]Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos:
Deep Learning with Sets and Point Clouds. CoRR abs/1611.04500 (2016) - [i13]Yifei Ma, Roman Garnett, Jeff G. Schneider:
Active Search for Sparse Signals with Region Sensing. CoRR abs/1612.00583 (2016) - 2015
- [i12]Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos:
High Dimensional Bayesian Optimisation and Bandits via Additive Models. CoRR abs/1503.01673 (2015) - [i11]Danica J. Sutherland, Jeff G. Schneider:
On the Error of Random Fourier Features. CoRR abs/1506.02785 (2015) - [i10]Danica J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider:
Linear-time Learning on Distributions with Approximate Kernel Embeddings. CoRR abs/1509.07553 (2015) - [i9]Junier B. Oliva, Danica J. Sutherland, Barnabás Póczos, Jeff G. Schneider:
Deep Mean Maps. CoRR abs/1511.04150 (2015) - 2014
- [i8]Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Jeff G. Schneider:
Fast Function to Function Regression. CoRR abs/1410.7414 (2014) - 2013
- [i7]Andrew W. Moore, Jeff G. Schneider:
Real-valued All-Dimensions search: Low-overhead rapid searching over subsets of attributes. CoRR abs/1301.0589 (2013) - [i6]Junier B. Oliva, Barnabás Póczos, Timothy D. Verstynen, Aarti Singh, Jeff G. Schneider, Fang-Cheng Yeh, Wen-Yih Isaac Tseng:
FuSSO: Functional Shrinkage and Selection Operator. CoRR abs/1311.2234 (2013) - [i5]Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing:
Fast Distribution To Real Regression. CoRR abs/1311.2236 (2013) - 2012
- [i4]Barnabás Póczos, Liang Xiong, Danica J. Sutherland, Jeff G. Schneider:
Support Distribution Machines. CoRR abs/1202.0302 (2012) - [i3]Barnabás Póczos, Liang Xiong, Jeff G. Schneider:
Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions. CoRR abs/1202.3758 (2012) - [i2]Barnabás Póczos, Zoubin Ghahramani, Jeff G. Schneider:
Copula-based Kernel Dependency Measures. CoRR abs/1206.4682 (2012) - [i1]Yifei Ma, Roman Garnett, Jeff G. Schneider:
Submodularity in Batch Active Learning and Survey Problems on Gaussian Random Fields. CoRR abs/1209.3694 (2012)
Coauthor Index
[j5] [j4] [j3] [c107] [c104] [c103] [c102] [c101] [c100] [c97] [c96] [c95] [c94] [c92] [c91] [c90] [c89] [c88] [c87] [c86] [c85] [c83] [c81] [c79] [c74] [c73] [c67] [c65] [c62] [c58] [c56] [c53] [c51] [c47] [c45] [c44] [c43] [i31] [i30] [i29] [i26] [i25] [i24] [i23] [i21] [i20] [i19] [i18] [i17] [i16] [i15] [i14] [i12] [i10] [i9] [i8] [i6] [i5] [i4] [i3] [i2]
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