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2020 – today
- 2024
[j5]Erich A. Mielke, Eric C. Townsend, David Wingate, John L. Salmon, Marc D. Killpack:
Human-robot planar co-manipulation of extended objects: data-driven models and control from human-human dyads. Frontiers Neurorobotics 18 (2024)- 2023
- [c35]Joshua Robinson, David Wingate:
Leveraging Large Language Models for Multiple Choice Question Answering. ICLR 2023 - [i22]Lisa P. Argyle, Ethan C. Busby, Joshua Gubler, Chris Bail, Thomas Howe, Christopher Michael Rytting, David Wingate:
AI Chat Assistants can Improve Conversations about Divisive Topics. CoRR abs/2302.07268 (2023) - [i21]Christopher Michael Rytting, Taylor Sorensen, Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, Joshua Gubler, David Wingate:
Towards Coding Social Science Datasets with Language Models. CoRR abs/2306.02177 (2023) - 2022
- [c34]Taylor Sorensen, Joshua Robinson, Christopher Michael Rytting, Alexander Glenn Shaw, Kyle Jeffrey Rogers, Alexia Pauline Delorey, Mahmoud Khalil, Nancy Fulda, David Wingate:
An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels. ACL (1) 2022: 819-862 - [c33]David Wingate, Mohammad Shoeybi, Taylor Sorensen:
Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models. EMNLP (Findings) 2022: 5621-5634 - [i20]Taylor Sorensen, Joshua Robinson, Christopher Michael Rytting, Alexander Glenn Shaw, Kyle Jeffrey Rogers, Alexia Pauline Delorey, Mahmoud Khalil, Nancy Fulda, David Wingate:
An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels. CoRR abs/2203.11364 (2022) - [i19]Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, Joshua Gubler, Christopher Michael Rytting, David Wingate:
Out of One, Many: Using Language Models to Simulate Human Samples. CoRR abs/2209.06899 (2022) - [i18]David Wingate, Mohammad Shoeybi, Taylor Sorensen:
Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models. CoRR abs/2210.03162 (2022) - [i17]Joshua Robinson, Christopher Michael Rytting, David Wingate:
Leveraging Large Language Models for Multiple Choice Question Answering. CoRR abs/2210.12353 (2022) - 2021
- [j4]Curtis C. Johnson
, Tyler Quackenbush, Taylor Sorensen, David Wingate, Marc D. Killpack:
Using First Principles for Deep Learning and Model-Based Control of Soft Robots. Frontiers Robotics AI 8: 654398 (2021) - [c32]Christopher Michael Rytting, David Wingate:
Leveraging the Inductive Bias of Large Language Models for Abstract Textual Reasoning. NeurIPS 2021: 17111-17122 - [i16]Christopher Michael Rytting, David Wingate:
Leveraging the Inductive Bias of Large Language Models for Abstract Textual Reasoning. CoRR abs/2110.02370 (2021) - 2020
- [c31]Zachary Brown, Nathaniel R. Robinson, David Wingate, Nancy Fulda:
Towards Neural Programming Interfaces. NeurIPS 2020 - [i15]Erich A. Mielke, Eric C. Townsend, David Wingate, Marc D. Killpack:
Human-robot co-manipulation of extended objects: Data-driven models and control from analysis of human-human dyads. CoRR abs/2001.00991 (2020) - [i14]Zachary C. Brown, Nathaniel R. Robinson, David Wingate, Nancy Fulda:
Towards Neural Programming Interfaces. CoRR abs/2012.05983 (2020)
2010 – 2019
- 2019
- [j3]Phillip Hyatt, David Wingate, Marc D. Killpack:
Model-Based Control of Soft Actuators Using Learned Non-linear Discrete-Time Models. Frontiers Robotics AI 6: 22 (2019) - [i13]Andrew N. Carr, David Wingate:
Graph Neural Processes: Towards Bayesian Graph Neural Networks. CoRR abs/1902.10042 (2019) - [i12]Robert Pottorff, Jared Nielsen, David Wingate:
Video Extrapolation with an Invertible Linear Embedding. CoRR abs/1903.00133 (2019) - [i11]Andrew N. Carr, Jared Nielson, David Wingate:
Wasserstein Neural Processes. CoRR abs/1910.00668 (2019) - [i10]Kolby Nottingham, Anand Balakrishnan, Jyotirmoy V. Deshmukh, Connor Christopherson, David Wingate:
Using Logical Specifications of Objectives in Multi-Objective Reinforcement Learning. CoRR abs/1910.01723 (2019) - 2018
- [c30]Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate:
Threat, Explore, Barter, Puzzle: A Semantically-Informed Algorithm for Extracting Interaction Modes. AAAI Workshops 2018: 552-556 - [c29]Morgan T. Gillespie, Charles M. Best, Eric C. Townsend, David Wingate, Marc D. Killpack:
Learning nonlinear dynamic models of soft robots for model predictive control with neural networks. RoboSoft 2018: 39-45 - [i9]David Wingate, William Myers, Nancy Fulda, Tyler Etchart:
Embedding Grammars. CoRR abs/1808.04891 (2018) - [i8]Iris Rubi Seaman, Jan-Willem van de Meent, David Wingate:
Modeling Theory of Mind for Autonomous Agents with Probabilistic Programs. CoRR abs/1812.01569 (2018) - 2017
- [c28]Nancy Fulda, Nathan Tibbetts, Zachary Brown, David Wingate:
Harvesting Common-sense Navigational Knowledge for Robotics from Uncurated Text Corpora. CoRL 2017: 525-534 - [c27]Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate:
What Can You Do with a Rock? Affordance Extraction via Word Embeddings. IJCAI 2017: 1039-1045 - [c26]Gary J. Ellingson, David Wingate, Timothy W. McLain:
Deep visual gravity vector detection for unmanned aircraft attitude estimation. IROS 2017: 5557-5563 - [i7]Nancy Fulda, Daniel Ricks, Ben Murdoch, David Wingate:
What can you do with a rock? Affordance extraction via word embeddings. CoRR abs/1703.03429 (2017) - [i6]Marco F. Cusumano-Towner, Alexey Radul, David Wingate, Vikash K. Mansinghka:
Probabilistic programs for inferring the goals of autonomous agents. CoRR abs/1704.04977 (2017) - [i5]Eric C. Townsend, Erich A. Mielke, David Wingate, Marc D. Killpack:
Estimating Human Intent for Physical Human-Robot Co-Manipulation. CoRR abs/1705.10851 (2017) - 2016
- [j2]Johannes Traa, David Wingate, Noah D. Stein, Paris Smaragdis:
Robust Source Localization and Enhancement With a Probabilistic Steered Response Power Model. IEEE ACM Trans. Audio Speech Lang. Process. 24(3): 493-503 (2016) - 2015
- [c25]Johannes Traa, Paris Smaragdis, Noah D. Stein, David Wingate:
Directional NMF for joint source localization and separation. WASPAA 2015: 1-5 - 2014
- [c24]Jonathan Scholz, Martin Levihn, Charles Lee Isbell Jr., David Wingate:
A Physics-Based Model Prior for Object-Oriented MDPs. ICML 2014: 1089-1097 - 2013
- [i4]David Wingate, Theophane Weber:
Automated Variational Inference in Probabilistic Programming. CoRR abs/1301.1299 (2013) - 2012
- [p1]David Wingate:
Predictively Defined Representations of State. Reinforcement Learning 2012: 415-439 - [i3]David Wingate, Noah D. Goodman, Daniel M. Roy, Joshua B. Tenenbaum:
The Infinite Latent Events Model. CoRR abs/1205.2604 (2012) - [i2]John Asmuth, Lihong Li, Michael L. Littman, Ali Nouri, David Wingate:
A Bayesian Sampling Approach to Exploration in Reinforcement Learning. CoRR abs/1205.2664 (2012) - [i1]Matthew R. Rudary, Satinder Singh, David Wingate:
Predictive Linear-Gaussian Models of Stochastic Dynamical Systems. CoRR abs/1207.1416 (2012) - 2011
- [c23]Jason Valdez, Mina Guirguis, David Wingate, Rory Rinkevich:
An expanding reference library for Peer-to-Peer content. eCrime Researchers Summit 2011: 1-8 - [c22]Jonathan Eastep, David Wingate, Anant Agarwal:
Smart data structures: an online machine learning approach to multicore data structures. ICAC 2011: 11-20 - [c21]Finale Doshi, David Wingate, Joshua B. Tenenbaum, Nicholas Roy:
Infinite Dynamic Bayesian Networks. ICML 2011: 913-920 - [c20]David Wingate, Noah D. Goodman, Daniel M. Roy, Leslie Pack Kaelbling, Joshua B. Tenenbaum:
Bayesian Policy Search with Policy Priors. IJCAI 2011: 1565-1570 - [c19]David Wingate, Noah D. Goodman, Andreas Stuhlmüller, Jeffrey Mark Siskind:
Nonstandard Interpretations of Probabilistic Programs for Efficient Inference. NIPS 2011: 1152-1160 - [c18]David Wingate, Andreas Stuhlmüller, Noah D. Goodman:
Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation. AISTATS 2011: 770-778 - 2010
- [c17]Jonathan Eastep, David Wingate, Marco D. Santambrogio, Anant Agarwal:
Smartlocks: lock acquisition scheduling for self-aware synchronization. ICAC 2010: 215-224 - [c16]Finale Doshi-Velez, David Wingate, Nicholas Roy, Joshua B. Tenenbaum:
Nonparametric Bayesian Policy Priors for Reinforcement Learning. NIPS 2010: 532-540
2000 – 2009
- 2009
- [c15]David Wingate, Carlos Diuk, Lihong Li, Matthew Taylor, Jordan Frank:
Workshop summary: Results of the 2009 reinforcement learning competition. ICML 2009: 6 - [c14]John Asmuth, Lihong Li, Michael L. Littman, Ali Nouri, David Wingate:
A Bayesian Sampling Approach to Exploration in Reinforcement Learning. UAI 2009: 19-26 - [c13]David Wingate, Noah D. Goodman, Daniel M. Roy, Joshua B. Tenenbaum:
The Infinite Latent Events Model. UAI 2009: 607-614 - 2008
- [b1]David Wingate:
Exponential Family Predictive Representations of State. University of Michigan, USA, 2008 - [c12]Michael H. Bowling, Alborz Geramifard, David Wingate:
Sigma point policy iteration. AAMAS (1) 2008: 379-386 - [c11]David Wingate, Satinder Singh:
Efficiently learning linear-linear exponential family predictive representations of state. ICML 2008: 1176-1183 - 2007
- [c10]David Wingate, Satinder Singh:
On discovery and learning of models with predictive representations of state for agents with continuous actions and observations. AAMAS 2007: 187 - [c9]David Wingate, Vishal Soni, Britton Wolfe, Satinder Singh:
Relational Knowledge with Predictive State Representations. IJCAI 2007: 2035-2040 - [c8]David Wingate, Satinder Singh:
Exponential Family Predictive Representations of State. NIPS 2007: 1617-1624 - 2006
- [c7]David Wingate, Satinder Singh:
Mixtures of Predictive Linear Gaussian Models for Nonlinear, Stochastic Dynamical Systems. AAAI 2006: 524-529 - [c6]David Wingate, Satinder Singh:
Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems. ICML 2006: 1017-1024 - 2005
- [j1]David Wingate, Kevin D. Seppi:
Prioritization Methods for Accelerating MDP Solvers. J. Mach. Learn. Res. 6: 851-881 (2005) - [c5]David Wingate, Nathaniel Powell, Quinn Snell, Kevin D. Seppi:
Prioritized Multiplicative Schwarz Procedures for Solving Linear Systems. IPDPS 2005 - [c4]Matthew R. Rudary, Satinder Singh, David Wingate:
Predictive Linear-Gaussian Models of Stochastic Dynamical Systems. UAI 2005: 501-508 - 2004
- [c3]David Wingate, Kevin D. Seppi:
P3VI: a partitioned, prioritized, parallel value iterator. ICML 2004 - [c2]Christopher K. Monson, David Wingate, Kevin D. Seppi, Todd S. Peterson:
Variable resolution discretization in the joint space. ICMLA 2004: 449-455 - 2003
- [c1]David Wingate, Kevin D. Seppi:
Efficient Value Iteration Using Partitioned Models. ICMLA 2003: 53-59
Coauthor Index
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last updated on 2024-09-13 01:38 CEST by the dblp team
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