216 citations · 454 across the 55 of their papers we have counts for
6 papers · 1 filter
Differentiable Structure Learning and Causal Discovery for General Binary Data
Chang Deng, Bryon Aragam
Existing methods for differentiable structure learning in discrete data typically assume that the data are generated from specific structural equation models. However, these assump…
Optimality and computational barriers in variable selection under dependence
Ming Gao, Bryon Aragam
We study the optimal sample complexity of variable selection in linear regression under general design covariance, and show that subset selection is optimal while under standard co…
Optimal structure learning and conditional independence testing
Ming Gao, Yuhao Wang, Bryon Aragam
We establish a fundamental connection between optimal structure learning and optimal conditional independence testing by showing that the minimax optimal rate for structure learnin…
Intervening to Learn and Compose Causally Disentangled Representations
Alex Markham, Isaac Hirsch, Jeri A. Chang +2
In designing generative models, it is commonly believed that in order to learn useful latent structure, we face a fundamental tension between expressivity and structure. In this pa…
Towards Interpretable Deep Generative Models via Causal Representation Learning
Gemma E. Moran, Bryon Aragam
Recent developments in generative artificial intelligence (AI) rely on machine learning techniques such as deep learning and generative modeling to achieve state-of-the-art perform…
ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization
Yinjie Wang, Ling Yang, Guohao Li +2
Recent research has leveraged large language model multi-agent systems for complex problem-solving while trying to reduce the manual effort required to build them, driving the deve…