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NeurIPS 2022 Workshop on Score-Based Methods
Yingzhen Li · Yang Song · Valentin De Bortoli · Francois-Xavier Briol · Wenbo Gong · Alexia Jolicoeur-Martineau · Arash Vahdat

Fri Dec 02 06:50 AM -- 03:00 PM (PST) @ Room 293 - 294
Event URL: https://score-based-methods-workshop.github.io/ »

The score function, which is the gradient of the log-density, provides a unique way to represent probability distributions. By working with distributions through score functions, researchers have been able to develop efficient tools for machine learning and statistics, collectively known as score-based methods.

Score-based methods have had a significant impact on vastly disjointed subfields of machine learning and statistics, such as generative modeling, Bayesian inference, hypothesis testing, control variates and Stein’s methods. For example, score-based generative models, or denoising diffusion models, have emerged as the state-of-the-art technique for generating high quality and diverse images. In addition, recent developments in Stein’s method and score-based approaches for stochastic differential equations (SDEs) have contributed to the developement of fast and robust Bayesian posterior inference in high dimensions. These have potential applications in engineering fields, where they could help improve simulation models.

At our workshop, we will bring together researchers from these various subfields to discuss the success of score-based methods, and identify common challenges across different research areas. We will also explore the potential for applying score-based methods to even more real-world applications, including in computer vision, signal processing, and computational chemistry. By doing so, we hope to folster collaboration among researchers and build a more cohesive research community focused on score-based methods.

Author Information

Yingzhen Li (Imperial College London)

Yingzhen Li is a senior researcher at Microsoft Research Cambridge. She received her PhD from the University of Cambridge, and previously she has interned at Disney Research. She is passionate about building reliable machine learning systems, and her approach combines both Bayesian statistics and deep learning. Her contributions to the approximate inference field include: (1) algorithmic advances, such as variational inference with different divergences, combining variational inference with MCMC and approximate inference with implicit distributions; (2) applications of approximate inference, such as uncertainty estimation in Bayesian neural networks and algorithms to train deep generative models. She has served as area chairs at NeurIPS/ICML/ICLR/AISTATS on related research topics, and she is a co-organizer of the AABI2020 symposium, a flagship event of approximate inference.

Yang Song (OpenAI)
Valentin De Bortoli (ENS Ulm, CNRS)
Francois-Xavier Briol (University of Cambridge)
Wenbo Gong (Microsoft)
Alexia Jolicoeur-Martineau (Samsung - SAIT AI Lab, Montreal)
Arash Vahdat (NVIDIA Research)

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