10 citations · 29 across the 8 of their papers we have counts for
8 papers
BOAD: Discovering Hierarchical Software Engineering Agents via Bandit Optimization
Iris Xu, Guangtao Zeng, Zexue He +5
Large language models (LLMs) have shown strong reasoning and coding capabilities, yet they struggle to generalize to real-world software engineering (SWE) problems that are long-ho…
Latent Causal Probing: A Formal Perspective on Probing with Causal Models of Data
Charles Jin, Martin Rinard
As language models (LMs) deliver increasing performance on a range of NLP tasks, probing classifiers have become an indispensable technique in the effort to better understand their…
Emergent Representations of Program Semantics in Language Models Trained on Programs
Charles Jin, Martin Rinard
We present evidence that language models (LMs) of code can learn to represent the formal semantics of programs, despite being trained only to perform next-token prediction. Specifi…
Decentralized Inference via Capability Type Structures in Cooperative Multi-Agent Systems
Charles Jin, Zhang-Wei Hong, Farid Arthaud +2
This work studies the problem of ad hoc teamwork in teams composed of agents with differing computational capabilities. We consider cooperative multi-player games in which each age…
Neural Architecture Search using Property Guided Synthesis
Charles Jin, Phitchaya Mangpo Phothilimthana, Sudip Roy
In the past few years, neural architecture search (NAS) has become an increasingly important tool within the deep learning community. Despite the many recent successes of NAS, howe…
Incompatibility Clustering as a Defense Against Backdoor Poisoning Attacks
Charles Jin, Melinda Sun, Martin Rinard
We propose a novel clustering mechanism based on an incompatibility property between subsets of data that emerges during model training. This mechanism partitions the dataset into…
Towards Context-Agnostic Learning Using Synthetic Data
Charles Jin, Martin Rinard
We propose a novel setting for learning, where the input domain is the image of a map defined on the product of two sets, one of which completely determines the labels. We derive a…
Manifold Regularization for Locally Stable Deep Neural Networks
Charles Jin, Martin Rinard
We apply concepts from manifold regularization to develop new regularization techniques for training locally stable deep neural networks. Our regularizers are based on a sparsifica…