no code implementations • 18 Sep 2023 • Ethan X. Fang, Yajun Mei, Yuyang Shi, Qunzhi Xu, Tuo Zhao
We consider the linear discriminant analysis problem in the high-dimensional settings.
no code implementations • NeurIPS 2023 • Yuyang Shi, Valentin De Bortoli, Andrew Campbell, Arnaud Doucet
However, while it is desirable in many applications to approximate the deterministic dynamic Optimal Transport (OT) map which admits attractive properties, DDMs and FMMs are not guaranteed to provide transports close to the OT map.
1 code implementation • 7 Nov 2022 • Joe Benton, Yuyang Shi, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet
We propose a unifying framework generalising this approach to a wide class of spaces and leading to an original extension of score matching.
no code implementations • NeurIPS 2023 • Kamélia Daudel, Joe Benton, Yuyang Shi, Arnaud Doucet
We then provide two complementary theoretical analyses of the VR-IWAE bound and thus of the standard IWAE bound.
1 code implementation • 27 Feb 2022 • Yuyang Shi, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet
We extend the Schr\"odinger bridge framework to conditional simulation.
no code implementations • 23 Feb 2022 • Badr-Eddine Chérief-Abdellatif, Yuyang Shi, Arnaud Doucet, Benjamin Guedj
Despite its wide use and empirical successes, the theoretical understanding and study of the behaviour and performance of the variational autoencoder (VAE) have only emerged in the past few years.
1 code implementation • NeurIPS 2021 • Andrew Campbell, Yuyang Shi, Tom Rainforth, Arnaud Doucet
We present a variational method for online state estimation and parameter learning in state-space models (SSMs), a ubiquitous class of latent variable models for sequential data.
no code implementations • ICLR Workshop DeepGenStruct 2019 • Zhehui Chen, Haoming Jiang, Yuyang Shi, Bo Dai, Tuo Zhao
From the perspective of generative learning, our proposed method can be viewed as learning a deep generative model for generating adversarial samples, which is adaptive to the robust classification.
no code implementations • 3 Nov 2018 • Haoming Jiang, Zhehui Chen, Yuyang Shi, Bo Dai, Tuo Zhao
Adversarial training provides a principled approach for training robust neural networks.