563 citations · 861 across the 7 of their papers we have counts for
7 papers
A State-of-the-Art SQL Reasoning Model using RLVR
Alnur Ali, Ashutosh Baheti, Jonathan Chang +13
Developing custom reasoning models via Reinforcement Learning (RL) that can incorporate organization-specific knowledge has great potential to address problems faced by enterprise…
Multitask Prompted Training Enables Zero-Shot Task Generalization
Victor Sanh, Albert Webson, Colin Raffel +38
Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a…
Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage
Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas +2
This paper studies offline Imitation Learning (IL) where an agent learns to imitate an expert demonstrator without additional online environment interactions. Instead, the learner…
MobILE: Model-Based Imitation Learning From Observation Alone
Rahul Kidambi, Jonathan Chang, Wen Sun
This paper studies Imitation Learning from Observations alone (ILFO) where the learner is presented with expert demonstrations that consist only of states visited by an expert (wit…
Learning Deep Parameterized Skills from Demonstration for Re-targetable Visuomotor Control
Jonathan Chang, Nishanth Kumar, Sean Hastings +6
Robots need to learn skills that can not only generalize across similar problems but also be directed to a specific goal. Previous methods either train a new skill for every differ…
Joint Inference of Multiple Label Types in Large Networks
Deepayan Chakrabarti, Stanislav Funiak, Jonathan Chang +1
We tackle the problem of inferring node labels in a partially labeled graph where each node in the graph has multiple label types and each label type has a large number of possible…
Hierarchical relational models for document networks
Jonathan Chang, David M. Blei
We develop the relational topic model (RTM), a hierarchical model of both network structure and node attributes. We focus on document networks, where the attributes of each documen…