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07/12/2023
Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation
Policies often fail due to distribution shift – changes in the state and...
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05/18/2023
Towards Collaborative Plan Acquisition through Theory of Mind Modeling in Situated Dialogue
Collaborative tasks often begin with partial task knowledge and incomple...
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02/03/2023
Aligning Robot and Human Representations
To act in the world, robots rely on a representation of salient task asp...
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10/31/2022
Artificial Intelligence and Life in 2030: The One Hundred Year Study on Artificial Intelligence
In September 2016, Stanford's "One Hundred Year Study on Artificial Inte...
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10/27/2022
Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report
In September 2021, the "One Hundred Year Study on Artificial Intelligenc...
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06/30/2022
Towards Human-Agent Communication via the Information Bottleneck Principle
Emergent communication research often focuses on optimizing task-specifi...
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05/27/2022
Prototype Based Classification from Hierarchy to Fairness
Artificial neural nets can represent and classify many types of data but...
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05/18/2022
The Solvability of Interpretability Evaluation Metrics
Feature attribution methods are popular for explaining neural network pr...
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04/30/2022
ExSum: From Local Explanations to Model Understanding
Interpretability methods are developed to understand the working mechani...
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04/20/2022
When Does Syntax Mediate Neural Language Model Performance? Evidence from Dropout Probes
Recent causal probing literature reveals when language models and syntac...
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01/26/2022
Probe-Based Interventions for Modifying Agent Behavior
Neural nets are powerful function approximators, but the behavior of a g...
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01/19/2022
The Enforcers: Consistent Sparse-Discrete Methods for Constraining Informative Emergent Communication
Communication enables agents to cooperate to achieve their goals. Learni...
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10/14/2021
The Irrationality of Neural Rationale Models
Neural rationale models are popular for interpretable predictions of NLP...
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10/08/2021
Explaining Reward Functions to Humans for Better Human-Robot Collaboration
Explainable AI techniques that describe agent reward functions can enhan...
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08/04/2021
Emergent Discrete Communication in Semantic Spaces
Neural agents trained in reinforcement learning settings can learn to co...
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03/28/2021
A Bayesian Approach to Identifying Representational Errors
Trained AI systems and expert decision makers can make errors that are o...
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12/25/2020
RoCUS: Robot Controller Understanding via Sampling
As robots are deployed in complex situations, engineers and end users mu...
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01/16/2020
Adversarially Guided Self-Play for Adopting Social Conventions
Robotic agents must adopt existing social conventions in order to be eff...
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06/07/2019
Planning With Uncertain Specifications (PUnS)
Reward engineering is crucial to high performance in reinforcement learn...
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05/23/2018
Discovering Blind Spots in Reinforcement Learning
Agents trained in simulation may make errors in the real world due to mi...
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05/11/2018
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
Coordinating agents to complete a set of tasks with intercoupled tempora...
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03/03/2015
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
We present the Bayesian Case Model (BCM), a general framework for Bayesi...
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05/24/2014
Efficient Model Learning for Human-Robot Collaborative Tasks
We present a framework for learning human user models from joint-action ...
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06/05/2013
