BFS-based LLM framework reduces causal graph discovery queries from quadratic to linear while incorporating observational data and reporting state-of-the-art results on real graphs.
Lmpriors: Pre-trained language models as task-specific priors.arXiv preprint arXiv: 2210.12530
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A Graph Attention Network pretrained solely on synthetic MDPs solves held-out tabular RL benchmarks in context, outperforming UCB-VI and Q-learning online while matching VI-LCB offline.
Proposes causal risk minimization via higher-order moment-balancing error decomposition and attribute projection for high-dimensional treatments, with experiments on continuous, discrete, and text data.
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Efficient Causal Graph Discovery Using Large Language Models
BFS-based LLM framework reduces causal graph discovery queries from quadratic to linear while incorporating observational data and reporting state-of-the-art results on real graphs.
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Reinforcement Learning Foundation Models Should Already Be A Thing
A Graph Attention Network pretrained solely on synthetic MDPs solves held-out tabular RL benchmarks in context, outperforming UCB-VI and Q-learning online while matching VI-LCB offline.
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Causal Risk Minimization for High-Dimensional Treatments
Proposes causal risk minimization via higher-order moment-balancing error decomposition and attribute projection for high-dimensional treatments, with experiments on continuous, discrete, and text data.