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ORCIDJeff G. Schneider
Jeff Schneider 0001
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- affiliation: Carnegie Mellon University, The Robotics Institute
Other persons with the same name
- Jeff Schneider — disambiguation page
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2020 – today
- 2025
[i71]Xintong Duan, Yutong He, Fahim Tajwar, Wentse Chen, Ruslan Salakhutdinov, Jeff G. Schneider:
State Combinatorial Generalization In Decision Making With Conditional Diffusion Models. CoRR abs/2501.13241 (2025)- [i70]Fahim Tajwar, Yiding Jiang, Abitha Thankaraj, Sumaita Sadia Rahman, J. Zico Kolter, Jeff Schneider, Ruslan Salakhutdinov:
Training a Generally Curious Agent. CoRR abs/2502.17543 (2025) - 2024
- [j9]Benjamin Freed, Thomas Wei, Roberto Calandra, Jeff Schneider, Howie Choset:
Unifying Model-Based and Model-Free Reinforcement Learning with Equivalent Policy Sets. RLJ 1: 283-301 (2024) - [j8]Aditya Kapoor, Benjamin Freed, Jeff Schneider, Howie Choset:
Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization. RLJ 1: 380-399 (2024) - [c135]Yeeho Song, Jeff G. Schneider:
Genetic Algorithm for Curriculum Design in Multi-Agent Reinforcement Learning. CoRL 2024: 5351-5372 - [c134]Brian Yang, Huangyuan Su, Nikolaos Gkanatsios, Tsung-Wei Ke
, Ayush Jain, Jeff G. Schneider, Katerina Fragkiadaki:
Diffusion-ES: Gradient-Free Planning with Diffusion for Autonomous and Instruction-Guided Driving. CVPR 2024: 15342-15353 - [c133]Siddarth Venkatraman, Shivesh Khaitan, Ravi Tej Akella, John M. Dolan, Jeff Schneider, Glen Berseth:
Reasoning with Latent Diffusion in Offline Reinforcement Learning. ICLR 2024 - [c132]Youngseog Chung, Ian Char, Jeff Schneider:
Sampling-based Multi-dimensional Recalibration. ICML 2024 - [c131]Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar:
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data. ICML 2024 - [c130]Arundhati Banerjee, Jeff G. Schneider:
Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents. ICRA 2024: 7229-7235 - [c129]Adam Villaflor, Brian Yang, Huangyuan Su, Katerina Fragkiadaki, John M. Dolan, Jeff G. Schneider:
Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Driving. ICRA 2024: 14785-14791 - [i69]Arundhati Banerjee, Jeff Schneider:
Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents. CoRR abs/2401.03154 (2024) - [i68]Brian Yang, Huangyuan Su, Nikolaos Gkanatsios, Tsung-Wei Ke, Ayush Jain, Jeff Schneider, Katerina Fragkiadaki:
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following. CoRR abs/2402.06559 (2024) - [i67]Adam Villaflor, Brian Yang, Huangyuan Su, Katerina Fragkiadaki, John M. Dolan, Jeff Schneider:
Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Driving. CoRR abs/2403.07232 (2024) - [i66]Ian Char, Youngseog Chung, Joseph Abbate, Egemen Kolemen, Jeff G. Schneider:
Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks. CoRR abs/2404.12416 (2024) - [i65]Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar:
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data. CoRR abs/2404.14367 (2024) - [i64]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:
What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions. CoRR abs/2405.13954 (2024) - [i63]Aditya Kapoor, Benjamin Freed, Howie Choset, Jeff G. Schneider:
Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization. CoRR abs/2408.04295 (2024) - [i62]Youngseog Chung, Dhruv Malik, Jeff G. Schneider, Yuanzhi Li, Aarti Singh:
Beyond Parameter Count: Implicit Bias in Soft Mixture of Experts. CoRR abs/2409.00879 (2024) - [i61]Albert Xu, Bhaskar Vundurthy, Geordan Gutow, Ian Abraham, Jeff G. Schneider, Howie Choset:
Measure Preserving Flows for Ergodic Search in Convoluted Environments. CoRR abs/2409.09164 (2024) - [i60]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:
Decentralized Uncertainty-Aware Active Search with a Team of Aerial Robots. CoRR abs/2410.08507 (2024) - 2023
- [c128]Nikhil Angad Bakshi, Jeff Schneider:
Stealthy Terrain-Aware Multi-Agent Active Search. CoRL 2023: 1782-1796 - [c127]Arundhati Banerjee, Ramina Ghods, Jeff Schneider:
Cost-Awareness in Multi-Agent Active Search. ECAI 2023: 182-189 - [c126]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 - [c125]Benjamin Freed, Siddarth Venkatraman, Guillaume Adrien Sartoretti, Jeff Schneider, Howie Choset:
Learning Temporally AbstractWorld Models without Online Experimentation. ICML 2023: 10338-10356 - [c124]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 - [c123]Arundhati Banerjee, Ramina Ghods, Jeff G. Schneider:
Multi-Agent Active Search using Detection and Location Uncertainty. ICRA 2023: 7720-7727 - [c122]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 - [c121]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 - [c120]Ian Char, Jeff Schneider:
PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks. NeurIPS 2023 - [i59]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) - [i58]Anirudha Ramesh, Anurag Ghosh, Christoph Mertz, Jeff Schneider:
Enhancing Visual Domain Adaptation with Source Preparation. CoRR abs/2306.10142 (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
- [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) - [c119]Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger:
An Experimental Design Perspective on Model-Based Reinforcement Learning. ICLR 2022 - [c118]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 - [c117]Yeeho Song, Jeff Schneider:
Robust Reinforcement Learning via Genetic Curriculum. ICRA 2022: 5560-5566 - [c116]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 - [c115]Divam Gupta, Wei Pu, Trenton Tabor, Jeff Schneider:
SBEVNet: End-to-End Deep Stereo Layout Estimation. WACV 2022: 667-676 - [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
- [c114]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 - [c113]Zhiqian Qiao, Jeff Schneider, John M. Dolan:
Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning*. ICRA 2021: 2667-2673 - [c112]Ramina Ghods, William J. Durkin, Jeff Schneider:
Multi-Agent Active Search using Realistic Depth-Aware Noise Model. ICRA 2021: 9101-9108 - [c111]Tanmay Agarwal, Hitesh Arora, Jeff Schneider:
Learning Urban Driving Policies using Deep Reinforcement Learning. ITSC 2021: 607-614 - [c110]Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider:
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. NeurIPS 2021: 10971-10984 - [c109]Ramina Ghods, Arundhati Banerjee, Jeff Schneider:
Decentralized multi-agent active search for sparse signals. UAI 2021: 696-706 - [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
- [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) - [c108]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 - [c107]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 - [c106]Zhiqian Qiao, Zachariah Tyree, Priyantha Mudalige, Jeff Schneider, John M. Dolan:
Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning. IROS 2020: 6084-6089 - [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)
2010 – 2019
- 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) - [c105]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 - [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
- [c104]Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos:
Parallelised Bayesian Optimisation via Thompson Sampling. AISTATS 2018: 133-142 - [c103]Junier B. Oliva, Avinava Dubey, Manzil Zaheer, Barnabás Póczos, Ruslan Salakhutdinov, Eric P. Xing, Jeff Schneider:
Transformation Autoregressive Networks. ICML 2018: 3895-3904 - [c102]Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Neural Architecture Search with Bayesian Optimisation and Optimal Transport. NeurIPS 2018: 2020-2029 - [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
- [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) - [c101]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 - [c100]Yifei Ma, Roman Garnett, Jeff G. Schneider:
Active Search for Sparse Signals with Region Sensing. AAAI 2017: 2315-2321 - [c99]Jeff G. Schneider:
Active Optimization and Self Driving Cars. AAMAS 2017: 3 - [c98]Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos:
Deep Learning with Sets and Point Clouds. ICLR (Workshop) 2017 - [c97]Kirthevasan Kandasamy, Gautam Dasarathy, Jeff G. Schneider, Barnabás Póczos:
Multi-fidelity Bayesian Optimisation with Continuous Approximations. ICML 2017: 1799-1808 - [c96]Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider:
The Statistical Recurrent Unit. ICML 2017: 2671-2680 - [c95]Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos:
Equivariance Through Parameter-Sharing. ICML 2017: 2892-2901 - [c94]Sibi Venkatesan, James Kyle Miller, Jeff Schneider, Artur Dubrawski:
Scaling Active Search using Linear Similarity Functions. IJCAI 2017: 2878-2884 - [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
- [c93]Danica J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider:
Linear-Time Learning on Distributions with Approximate Kernel Embeddings. AAAI 2016: 2073-2079 - [c92]Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner:
Stochastic Neural Networks with Monotonic Activation Functions. AISTATS 2016: 809-818 - [c91]Chun-Liang Li, Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider:
High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models. AISTATS 2016: 884-892 - [c90]Junier B. Oliva, Avinava Dubey, Andrew Gordon Wilson, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing:
Bayesian Nonparametric Kernel-Learning. AISTATS 2016: 1078-1086 - [c89]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 - [c88]Xuezhi Wang, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos:
Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems. IJCAI 2016: 2146-2152 - [c87]Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos:
Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations. NIPS 2016: 992-1000 - [c86]Kirthevasan Kandasamy, Gautam Dasarathy, Barnabás Póczos, Jeff G. Schneider:
The Multi-fidelity Multi-armed Bandit. NIPS 2016: 1777-1785 - [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
- [c85]Yifei Ma, Danica J. Sutherland, Roman Garnett, Jeff G. Schneider:
Active Pointillistic Pattern Search. AISTATS 2015 - [c84]Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Hy Trac, Shirley Ho, Jeff G. Schneider:
Fast Function to Function Regression. AISTATS 2015 - [c83]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 - [c82]Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos:
High Dimensional Bayesian Optimisation and Bandits via Additive Models. ICML 2015: 295-304 - [c81]Roman Garnett, Shirley Ho, Jeff G. Schneider:
Finding Galaxies in the Shadows of Quasars with Gaussian Processes. ICML 2015: 1025-1033 - [c80]Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos:
Bayesian Active Learning for Posterior Estimation - IJCAI-15 Distinguished Paper. IJCAI 2015: 3605-3611 - [c79]Yifei Ma, Tzu-Kuo Huang, Jeff G. Schneider:
Active Search and Bandits on Graphs using Sigma-Optimality. UAI 2015: 542-551 - [c78]Danica J. Sutherland, Jeff G. Schneider:
On the Error of Random Fourier Features. UAI 2015: 862-871 - [c77]Xuezhi Wang, Jeff G. Schneider:
Generalization Bounds for Transfer Learning under Model Shift. UAI 2015: 922-931 - [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
- [c76]Yifei Ma, Roman Garnett, Jeff G. Schneider:
Active Area Search via Bayesian Quadrature. AISTATS 2014: 595-603 - [c75]Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing:
Fast Distribution To Real Regression. AISTATS 2014: 706-714 - [c74]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 - [c73]Xuezhi Wang, Tzu-Kuo Huang, Jeff G. Schneider:
Active Transfer Learning under Model Shift. ICML 2014: 1305-1313 - [c72]Guillermo F. Cabrera, Christopher J. Miller, Jeff G. Schneider:
Systematic Labeling Bias: De-biasing Where Everyone is Wrong. ICPR 2014: 4417-4422 - [c71]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 - [c70]Xuezhi Wang, Jeff G. Schneider:
Flexible Transfer Learning under Support and Model Shift. NIPS 2014: 1898-1906 - [c69]Liang Xiong, Jeff G. Schneider:
Learning from Point Sets with Observational Bias. UAI 2014: 898-906 - [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
- [c68]Liang Xiong, Barnabás Póczos, Jeff G. Schneider:
Efficient Learning on Point Sets. ICDM 2013: 847-856 - [c67]Tzu-Kuo Huang, Jeff G. Schneider:
Spectral Learning of Hidden Markov Models from Dynamic and Static Data. ICML (3) 2013: 630-638 - [c66]Junier B. Oliva, Barnabás Póczos, Jeff G. Schneider:
Distribution to Distribution Regression. ICML (3) 2013: 1049-1057 - [c65]Matthew Tesch, Jeff G. Schneider, Howie Choset:
Expensive Function Optimization with Stochastic Binary Outcomes. ICML (3) 2013: 1283-1291 - [c64]Matthew Tesch, Jeff G. Schneider, Howie Choset:
Expensive multiobjective optimization for robotics. ICRA 2013: 973-980 - [c63]Danica J. Sutherland, Barnabás Póczos, Jeff G. Schneider:
Active learning and search on low-rank matrices. KDD 2013: 212-220 - [c62]Xuezhi Wang, Roman Garnett, Jeff G. Schneider:
Active search on graphs. KDD 2013: 731-738 - [c61]Tzu-Kuo Huang, Jeff G. Schneider:
Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition. NIPS 2013: 333-341 - [c60]Yifei Ma, Roman Garnett, Jeff G. Schneider:
Σ-Optimality for Active Learning on Gaussian Random Fields. NIPS 2013: 2751-2759 - [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
- [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) - [c59]Barnabás Póczos, Liang Xiong, Danica J. Sutherland, Jeff G. Schneider:
Nonparametric kernel estimators for image classification. CVPR 2012: 2989-2996 - [c58]Roman Garnett, Yamuna Krishnamurthy, Xuehan Xiong, Jeff G. Schneider, Richard P. Mann:
Bayesian Optimal Active Search and Surveying. ICML 2012 - [c57]Barnabás Póczos, Zoubin Ghahramani, Jeff G. Schneider:
Copula-based Kernel Dependency Measures. ICML 2012 - [c56]Yi Zhang, Jeff G. Schneider:
Maximum Margin Output Coding. ICML 2012 - [c55]Tzu-Kuo Huang, Jeff G. Schneider:
Learning Bi-clustered Vector Autoregressive Models. ECML/PKDD (2) 2012: 741-756 - [c54]Barnabás Póczos, Jeff G. Schneider:
Nonparametric Estimation of Conditional Information and Divergences. AISTATS 2012: 914-923 - [c53]Yi Zhang, Jeff G. Schneider:
A Composite Likelihood View for Multi-Label Classification. AISTATS 2012: 1407-1415 - [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) - 2011
- [c52]Barnabás Póczos, Zoltán Szabó, Jeff G. Schneider:
Nonparametric divergence estimators for independent subspace analysis. EUSIPCO 2011: 1718-1722 - [c51]Liang Xiong, Xi Chen, Jeff G. Schneider:
Direct Robust Matrix Factorizatoin for Anomaly Detection. ICDM 2011: 844-853 - [c50]Matthew Tesch, Jeff G. Schneider, Howie Choset:
Adapting control policies for expensive systems to changing environments. IROS 2011: 357-364 - [c49]Matthew Tesch, Jeff G. Schneider, Howie Choset:
Using response surfaces and expected improvement to optimize snake robot gait parameters. IROS 2011: 1069-1074 - [c48]Liang Xiong, Barnabás Póczos, Jeff G. Schneider:
Group Anomaly Detection using Flexible Genre Models. NIPS 2011: 1071-1079 - [c47]Tzu-Kuo Huang, Jeff G. Schneider:
Learning Auto-regressive Models from Sequence and Non-sequence Data. NIPS 2011: 1548-1556 - [c46]Barnabás Póczos, Liang Xiong, Jeff G. Schneider:
Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions. UAI 2011: 599-608 - [c45]Barnabás Póczos, Jeff G. Schneider:
On the Estimation of alpha-Divergences. AISTATS 2011: 609-617 - [c44]Liang Xiong, Barnabás Póczos, Jeff G. Schneider, Andrew J. Connolly, Jake VanderPlas:
Hierarchical Probabilistic Models for Group Anomaly Detection. AISTATS 2011: 789-797 - [c43]Yi Zhang, Jeff G. Schneider:
Multi-Label Output Codes using Canonical Correlation Analysis. AISTATS 2011: 873-882 - 2010
- [c42]Yi Zhang, Jeff G. Schneider:
Projection Penalties: Dimension Reduction without Loss. ICML 2010: 1223-1230 - [c41]Yi Zhang, Jeff G. Schneider:
Learning Multiple Tasks with a Sparse Matrix-Normal Penalty. NIPS 2010: 2550-2558 - [c40]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 - [c39]Pinar Donmez, Jaime G. Carbonell, Jeff G. Schneider:
A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy. SDM 2010: 826-837 - [c38]Yi Zhang, Jeff G. Schneider, Artur Dubrawski:
Learning Compressible Models. SDM 2010: 872-881 - [c37]Tzu-Kuo Huang, Le Song, Jeff G. Schneider:
Learning Nonlinear Dynamic Models from Non-sequenced Data. AISTATS 2010: 350-357
2000 – 2009
- 2009
- [c36]Tzu-Kuo Huang, Jeff G. Schneider:
Learning linear dynamical systems without sequence information. ICML 2009: 425-432 - [c35]Pinar Donmez, Jaime G. Carbonell, Jeff G. Schneider:
Efficiently learning the accuracy of labeling sources for selective sampling. KDD 2009: 259-268 - 2008
- [c34]Josep Roure, Artur Dubrawski, Jeff G. Schneider:
Learning Detectors of Events in Multivariate Time Series. AMIA 2008 - [c33]Brent Bryan, Jeff G. Schneider:
Actively learning level-sets of composite functions. ICML 2008: 80-87 - [c32]Kaustav Das, Jeff G. Schneider, Daniel B. Neill:
Anomaly pattern detection in categorical datasets. KDD 2008: 169-176 - [c31]Yi Zhang, Jeff G. Schneider, Artur Dubrawski:
Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text. NIPS 2008: 1945-1952 - 2007
- [c30]Josep Roure, Artur Dubrawski, Jeff G. Schneider:
A Study into Detection of Bio-Events in Multiple Streams of Surveillance Data. BioSurveillance 2007: 124-133 - [c29]Brent Bryan, H. Brendan McMahan, Chad M. Schafer, Jeff G. Schneider:
Efficiently computing minimax expected-size confidence regions. ICML 2007: 97-104 - [c28]Kaustav Das, Jeff G. Schneider:
Detecting anomalous records in categorical datasets. KDD 2007: 220-229 - 2005
- [c27]Jeff G. Schneider, David Apfelbaum, Drew Bagnell, Reid G. Simmons:
Learning Opportunity Costs in Multi-Robot Market Based Planners. ICRA 2005: 1151-1156 - [c26]Rosemary Emery-Montemerlo, Geoffrey J. Gordon, Jeff G. Schneider, Sebastian Thrun:
Game Theoretic Control for Robot Teams. ICRA 2005: 1163-1169 - [c25]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
- [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) - [c24]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 - [c23]Kaustav Das, Andrew W. Moore, Jeff G. Schneider:
Belief state approaches to signaling alarms in surveillance systems. KDD 2004: 539-544 - 2003
- [c22]Jeremy Kubica, Andrew W. Moore, Jeff G. Schneider:
Tractable Group Detection on Large Link Data Sets. ICDM 2003: 573-576 - [c21]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 - [c20]J. Andrew Bagnell, Jeff G. Schneider:
Covariant Policy Search. IJCAI 2003: 1019-1024 - [c19]J. Andrew Bagnell, Sham M. Kakade, Andrew Y. Ng, Jeff G. Schneider:
Policy Search by Dynamic Programming. NIPS 2003: 831-838 - 2002
- [c18]Jeremy Kubica, Andrew W. Moore, Jeff G. Schneider, Yiming Yang:
Stochastic Link and Group Detection. AAAI/IAAI 2002: 798-806 - [c17]Andrew W. Moore, Jeff G. Schneider:
Real-valued All-Dimensions Search: Low-overhead Rapid Searching over Subsets of Attributes. UAI 2002: 360-369 - 2001
- [c16]J. Andrew Bagnell, Jeff G. Schneider:
Autonomous Helicopter Control using Reinforcement Learning Policy Search Methods. ICRA 2001: 1615-1620 - [c15]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
- [c14]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 - [c13]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-
1990 – 1999
- 1999
- [c12]Jeff G. Schneider, Weng-Keen Wong, Andrew W. Moore, Martin A. Riedmiller:
Distributed Value Functions. ICML 1999: 371-378 - [c11]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
- [c10]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 - [c9]Jeff G. Schneider, Justin A. Boyan, Andrew W. Moore:
Value Function Based Production Scheduling. ICML 1998: 522-530 - 1997
- [c8]Artur Dubrawski, Jeff Schneider:
Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators. AISTATS 1997: 165-172 - [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
Coauthor Index
[j5] [c108] [j4] [c105] [i31] [i30] [i29] [c104] [c103] [c102] [i26] [i25] [j3] [c101] [c98] [c97] [c96] [c95] [i24] [i23] [i21] [i20] [i19] [c93] [c92] [c91] [c90] [c89] [c88] [c87] [c86] [i18] [i17] [i16] [i15] [i14] [c84] [c82] [c80] [i12] [i10] [i9] [c75] [c74] [i8] [c68] [c66] [c63] [i6] [i5] [c59] [c57] [c54] [i4] [i3] [i2] [c52] [c48] [c46] [c45] [c44]
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