D. Wingate

发表

Automated Variational Inference in Probabilistic Programming pdf

David Wingate, Theophane Weber, T. Weber, 2013, ArXiv.

The Infinite Latent Events Model

Joshua B. Tenenbaum, Noah D. Goodman, Daniel M. Roy, 2009, UAI.

Exponential Family Predictive Representations of State

Satinder P. Singh, David Wingate, Satinder Singh, 2007, NIPS.

A Bayesian Sampling Approach to Exploration in Reinforcement Learning pdf

Lihong Li, Michael L. Littman, David Wingate, 2009, UAI.

Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems

Satinder P. Singh, David Wingate, Satinder Singh, 2006, ICML.

Nonparametric Bayesian Policy Priors for Reinforcement Learning

Joshua B. Tenenbaum, Nicholas Roy, Finale Doshi-Velez, 2010, NIPS.

Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation

Noah D. Goodman, David Wingate, Andreas Stuhlmüller, 2011, AISTATS.

A Physics-Based Model Prior for Object-Oriented MDPs

David Wingate, Charles Lee Isbell, Jonathan Scholz, 2014, ICML.

Compositional Policy Priors

Timothy J. O'Donnell, Carlos Diuk, J. Tenenbaum, 2013 .

Bayesian Policy Search with Policy Priors

Leslie Pack Kaelbling, Joshua B. Tenenbaum, Noah D. Goodman, 2011, IJCAI.

A Bayesian Approach to Finding Compact Representations for Reinforcement Learning

N. Roy, J. How, Stefanie Tellex, 2012 .

PAC-MDP Reinforcement Learning with Bayesian Priors

Michael L. Littman, David Wingate, Ali Nouri, 2009 .

Machine-learned multi-system surrogate models for materials prediction

Conrad W. Rosenbrock, Tim Mueller, Matthias Rupp, 2018, npj Computational Materials.

A New Approach for Conditioning Process-Based Geologic Models to Well Data

D. Wingate, J. Kane, Z. Sylvester, 2016, Mathematical Geosciences.

BYU-EVE: Mixed Initiative Dialog via Structured Knowledge Graph Traversal and Conversational Scaffolding

Andrew N. Carr, D. Wingate, Nancy Fulda, 2018 .

The Infinite Latent Events Model Citation

Noah D. Goodman, Daniel M. Roy, J. Tenenbaum, 2009 .

Predictive Linear-Gaussian Models of Stochastic Dynamical Systems

Matthew R. Rudary, Satinder Singh, D. Wingate, 2005, UAI.

Cache Performance of Priority Metrics for MDP Solvers

D. Wingate, K. Seppi, 2022 .

Designing intelligent robots : reintegrating AI : papers from the AAAI Spring Symposium

Byron Boots, George Konidaris, Benjamin Rosman, 2012 .

Smart Queue , SkipList , PairHeap , Stack Reinforcement Learning Publication ListE . g . : 1 4 ( of a discrete var ) Smart Skip List S t Serial Skip List 32 Interface Lock cancoun

D. Wingate, A. Agarwal, 2011 .

General machine-learning surrogate models for materials prediction

Conrad W. Rosenbrock, M. Rupp, D. Wingate, 2018, 1809.09203.

Nested Reasoning About Autonomous Agents Using Probabilistic Programs

Jan-Willem van de Meent, D. Wingate, I. Seaman, 2018, 1812.01569.

Out of One, Many: Using Language Models to Simulate Human Samples

D. Wingate, Nancy Fulda, Lisa P. Argyle, 2022, Political Analysis.

Wasserstein Neural Processes

Andrew N. Carr, D. Wingate, Jared Nielson, 2019, ArXiv.

Leveraging Large Language Models for Multiple Choice Question Answering

D. Wingate, Joshua Robinson, C. Rytting, 2022, ArXiv.

Leveraging Large Language Models for Multiple Choice Question Answering

D. Wingate, Joshua Robinson, C. Rytting, 2022, ArXiv.

Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models

M. Shoeybi, D. Wingate, Taylor Sorensen, 2022, EMNLP.

An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels

D. Wingate, Nancy Fulda, Lisa P. Argyle, 2022, ACL.

Prioritization Methods for Accelerating MDP Solvers

Kevin D. Seppi, David Wingate, D. Wingate, 2005, J. Mach. Learn. Res..

P3VI: a partitioned, prioritized, parallel value iterator

Kevin D. Seppi, David Wingate, D. Wingate, 2004, ICML.

Threat, Explore, Barter, Puzzle: A Semantically-Informed Algorithm for Extracting Interaction Modes

David Wingate, Nancy Fulda, Daniel Ricks, 2018, AAAI Workshops.

What Can You Do with a Rock? Affordance Extraction via Word Embeddings

David Wingate, Nancy Fulda, Daniel Ricks, 2017, IJCAI.

Nonstandard Interpretations of Probabilistic Programs for Efficient Inference

Noah D. Goodman, Jeffrey Mark Siskind, David Wingate, 2011, NIPS.

A probabilistic view of protein stability, conformational specificity, and design

Dennis Della Corte, Jacob A. Stern, Bradley C. Bundy, 2022, bioRxiv.

Smartlocks: lock acquisition scheduling for self-aware synchronization

Anant Agarwal, Marco D. Santambrogio, David Wingate, 2010, ICAC '10.

Predictively Defined Representations of State

David Wingate, D. Wingate, 2012, Reinforcement Learning.

Leveraging the Inductive Bias of Large Language Models for Abstract Textual Reasoning pdf

David Wingate, Christopher Michael Rytting, D. Wingate, 2021, NeurIPS.

Model-Based Control of Soft Actuators Using Learned Non-linear Discrete-Time Models

David Wingate, Marc D. Killpack, Phillip Hyatt, 2019, Front. Robot. AI.

Leveraging Large Language Models for Multiple Choice Question Answering

D. Wingate, Joshua Robinson, C. Rytting, 2022, ICLR.

Religious exemplars’ experience of indebtedness to God: employing innovative machine learning to explore a novel construct

D. Wingate, Sam A. Hardy, Justin J. Hendricks, 2023, The Journal of Positive Psychology.

Towards Neural Programming Interfaces pdf

David Wingate, Nancy Fulda, Nathaniel Robinson, 2020, NeurIPS.

Prioritized Multiplicative Schwarz Procedures for Solutions to General Linear Systems

D. Wingate, Q. Snell, Nathaniel Powell, 2022 .

Towards Coding Social Science Datasets with Language Models

D. Wingate, Nancy Fulda, Lisa P. Argyle, 2023, 2306.02177.

AI Chat Assistants can Improve Conversations about Divisive Topics

D. Wingate, C. Bail, Lisa P. Argyle, 2023, ArXiv.

Learning nonlinear dynamic models of soft robots for model predictive control with neural networks

David Wingate, Marc D. Killpack, Charles M. Best, 2018, 2018 IEEE International Conference on Soft Robotics (RoboSoft).

Infinite Dynamic Bayesian Networks

Joshua B. Tenenbaum, Nicholas Roy, Finale Doshi-Velez, 2011, ICML.

Graph Neural Processes: Towards Bayesian Graph Neural Networks pdf

David Wingate, Andrew N. Carr, Andrew N. Carr, 2019, ArXiv.

Probabilistic programs for inferring the goals of autonomous agents pdf

Vikash K. Mansinghka, David Wingate, Alexey Radul, 2017, ArXiv.

Predictive Linear-Gaussian Models of Stochastic Dynamical Systems

Satinder P. Singh, David Wingate, Matthew R. Rudary, 2005, UAI.

Using First Principles for Deep Learning and Model-Based Control of Soft Robots

David Wingate, Marc D. Killpack, Curtis C. Johnson, 2021, Frontiers in Robotics and AI.

Embedding Grammars pdf

David Wingate, William Myers, Nancy Fulda, 2018, ArXiv.

ProSPr: Democratized Implementation of Alphafold Protein Distance Prediction Network

David Wingate, Dennis Della Corte, Wendy M. Billings, 2019, bioRxiv.

Harvesting Common-sense Navigational Knowledge for Robotics from Uncurated Text Corpora

David Wingate, Nancy Fulda, Zachary Brown, 2017, CoRL.

Informing Action Primitives Through Free-Form Text

D. Wingate, Nancy Fulda, D. Ricks, 2018 .

Robust Source Localization and Enhancement With a Probabilistic Steered Response Power Model

Paris Smaragdis, Noah D. Stein, David Wingate, 2016, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

An expanding reference library for Peer-to-Peer content

Mina Guirguis, David Wingate, Jason Valdez, 2011, 2011 eCrime Researchers Summit.

On discovery and learning of models with predictive representations of state for agents with continuous actions and observations

Satinder P. Singh, David Wingate, Satinder Singh, 2007, AAMAS '07.