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06/03/2023
Towards Coding Social Science Datasets with Language Models
Researchers often rely on humans to code (label, annotate, etc.) large s...
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02/14/2023
AI Chat Assistants can Improve Conversations about Divisive Topics
A rapidly increasing amount of human conversation occurs online. But div...
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10/22/2022
Leveraging Large Language Models for Multiple Choice Question Answering
While large language models (LLMs) like GPT-3 have achieved impressive r...
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10/06/2022
Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models
We explore the idea of compressing the prompts used to condition languag...
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09/14/2022
Out of One, Many: Using Language Models to Simulate Human Samples
We propose and explore the possibility that language models can be studi...
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03/21/2022
An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels
Pre-trained language models derive substantial linguistic and factual kn...
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10/05/2021
Leveraging the Inductive Bias of Large Language Models for Abstract Textual Reasoning
Large natural language models (such as GPT-3 or T5) demonstrate impressi...
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12/10/2020
Towards Neural Programming Interfaces
It is notoriously difficult to control the behavior of artificial neural...
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01/03/2020
Human-robot co-manipulation of extended objects: Data-driven models and control from analysis of human-human dyads
Human teams are able to easily perform collaborative manipulation tasks....
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10/03/2019
Using Logical Specifications of Objectives in Multi-Objective Reinforcement Learning
In the multi-objective reinforcement learning (MORL) paradigm, the relat...
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10/01/2019
Wasserstein Neural Processes
Neural Processes (NPs) are a class of models that learn a mapping from a...
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03/01/2019
Video Extrapolation with an Invertible Linear Embedding
We predict future video frames from complex dynamic scenes, using an inv...
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02/26/2019
Graph Neural Processes: Towards Bayesian Graph Neural Networks
We introduce Graph Neural Processes (GNP), inspired by the recent work i...
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12/04/2018
Modeling Theory of Mind for Autonomous Agents with Probabilistic Programs
As autonomous agents become more ubiquitous, they will eventually have t...
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08/14/2018
Embedding Grammars
Classic grammars and regular expressions can be used for a variety of pu...
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04/17/2017
Probabilistic programs for inferring the goals of autonomous agents
Intelligent systems sometimes need to infer the probable goals of people...
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03/09/2017
What can you do with a rock? Affordance extraction via word embeddings
Autonomous agents must often detect affordances: the set of behaviors en...
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01/07/2013
Automated Variational Inference in Probabilistic Programming
We present a new algorithm for approximate inference in probabilistic pr...
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07/04/2012
Predictive Linear-Gaussian Models of Stochastic Dynamical Systems
Models of dynamical systems based on predictive state representations (P...
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05/09/2012