Learning Graphical Models
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. (Wikipedia)
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Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics
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Eliciting Categorical Data for Optimal Aggregation
Chien-Ju Ho, Rafael Frongillo, Yiling Chen
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Overleaf Example
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We model episode sessions--parts of the episode where the latent state isfixed--and propose three keymodifications toexisting meta-RL methods: (i) consistency of latent information within sessions, (ii) session masking, and (iii) priorlatent conditioning.
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ff9c70659c39cdd801dd5f5a1201c29e-Paper-Conference.pdf
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Inference via knowledge compilation has also been used for many applications in neuro-symbolic AI,suchasconstrained generation [2,54]andneural logic programming [34,28].
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Provably Mitigating Overoptimization in RLHF: Y our SFT Loss is Implicitly an Adversarial Regularizer
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Then it fine-tunes the LLM to maximize the learned reward using RL techniques.
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