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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Conformal Bayesian Computation
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We develop scalable methods for producing conformal Bayesian predictive intervals with finite sample calibration guarantees.
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Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion
Neural Information Processing Systems
Work done as a visiting student at MIT. 38th Conference on Neural Information Processing Systems (NeurIPS 2024).
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Variational Bayesian Unlearning Quoc Phong Nguyen, Bryan Kian Hsiang Low, and Patrick Jaillet
Neural Information Processing Systems
We propose two novel tricks to tackle this challenge.
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