21 citations · 79 across the 17 of their papers we have counts for
17 papers
Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations
Joey Hong, Sergey Levine, Anca Dragan
Large language models (LLMs) have emerged as powerful and general solutions to many natural language tasks. However, many of the most important applications of language generation…
Offline RL with Observation Histories: Analyzing and Improving Sample Complexity
Joey Hong, Anca Dragan, Sergey Levine
Offline reinforcement learning (RL) can in principle synthesize more optimal behavior from a dataset consisting only of suboptimal trials. One way that this can happen is by "stitc…
ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis
Kensen Shi, Joey Hong, Yinlin Deng +3
When writing programs, people have the ability to tackle a new complex task by decomposing it into smaller and more familiar subtasks. While it is difficult to measure whether neur…
Learning to Influence Human Behavior with Offline Reinforcement Learning
Joey Hong, Sergey Levine, Anca Dragan
When interacting with people, AI agents do not just influence the state of the world -- they also influence the actions people take in response to the agent, and even their underly…
Multi-Task Off-Policy Learning from Bandit Feedback
Joey Hong, Branislav Kveton, Sumeet Katariya +2
Many practical applications, such as recommender systems and learning to rank, involve solving multiple similar tasks. One example is learning of recommendation policies for users…
On the Sensitivity of Reward Inference to Misspecified Human Models
Joey Hong, Kush Bhatia, Anca Dragan
Inferring reward functions from human behavior is at the center of value alignment - aligning AI objectives with what we, humans, actually want. But doing so relies on models of ho…
Confidence-Conditioned Value Functions for Offline Reinforcement Learning
Joey Hong, Aviral Kumar, Sergey Levine
Offline reinforcement learning (RL) promises the ability to learn effective policies solely using existing, static datasets, without any costly online interaction. To do so, offlin…
When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?
Aviral Kumar, Joey Hong, Anikait Singh +1
Offline reinforcement learning (RL) algorithms can acquire effective policies by utilizing previously collected experience, without any online interaction. It is widely understood…
Compositional Generalization and Decomposition in Neural Program Synthesis
Kensen Shi, Joey Hong, Manzil Zaheer +2
When writing programs, people have the ability to tackle a new complex task by decomposing it into smaller and more familiar subtasks. While it is difficult to measure whether neur…
Deep Hierarchy in Bandits
Joey Hong, Branislav Kveton, Sumeet Katariya +2
Mean rewards of actions are often correlated. The form of these correlations may be complex and unknown a priori, such as the preferences of a user for recommended products and the…
Hierarchical Bayesian Bandits
Joey Hong, Branislav Kveton, Manzil Zaheer +1
Meta-, multi-task, and federated learning can be all viewed as solving similar tasks, drawn from a distribution that reflects task similarities. We provide a unified view of all th…
Thompson Sampling with a Mixture Prior
Joey Hong, Branislav Kveton, Manzil Zaheer +2
We study Thompson sampling (TS) in online decision making, where the uncertain environment is sampled from a mixture distribution. This is relevant in multi-task learning, where a…
Non-Stationary Latent Bandits
Joey Hong, Branislav Kveton, Manzil Zaheer +4
Users of recommender systems often behave in a non-stationary fashion, due to their evolving preferences and tastes over time. In this work, we propose a practical approach for fas…
Latent Programmer: Discrete Latent Codes for Program Synthesis
Joey Hong, David Dohan, Rishabh Singh +2
In many sequence learning tasks, such as program synthesis and document summarization, a key problem is searching over a large space of possible output sequences. We propose to lea…
Latent Bandits Revisited
Joey Hong, Branislav Kveton, Manzil Zaheer +3
A latent bandit problem is one in which the learning agent knows the arm reward distributions conditioned on an unknown discrete latent state. The primary goal of the agent is to i…
Non-Stationary Off-Policy Optimization
Joey Hong, Branislav Kveton, Manzil Zaheer +2
Off-policy learning is a framework for evaluating and optimizing policies without deploying them, from data collected by another policy. Real-world environments are typically non-s…
Rules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactions
Joey Hong, Benjamin Sapp, James Philbin
We focus on the problem of predicting future states of entities in complex, real-world driving scenarios. Previous research has used low-level signals to predict short time horizon…