no code implementations • 3 Mar 2024 • Yang Xu, Yihong Gu, Cong Fang
It is observed empirically that the large language models (LLM), trained with a variant of regression loss using numerous corpus from the Internet, can unveil causal associations to some extent.
no code implementations • 6 Mar 2023 • Jianqing Fan, Cong Fang, Yihong Gu, Tong Zhang
To the best of our knowledge, this paper is the first to realize statistically efficient invariance learning in the general linear model.
no code implementations • 20 Mar 2022 • Jianqing Fan, Yihong Gu, Wen-Xin Zhou
This paper investigates the stability of deep ReLU neural networks for nonparametric regression under the assumption that the noise has only a finite p-th moment.
1 code implementation • NeurIPS 2020 • Yihong Gu, Weizhong Zhang, Cong Fang, Jason D. Lee, Tong Zhang
With the help of a new technique called {\it neural network grafting}, we demonstrate that even during the entire training process, feature distributions of differently initialized networks remain similar at each layer.
no code implementations • 18 Nov 2019 • Cong Fang, Yihong Gu, Weizhong Zhang, Tong Zhang
This new analysis is consistent with empirical observations that deep neural networks are capable of learning efficient feature representations.
1 code implementation • ICML 2020 • Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon
We formalize the Domain Adaptive Imitation Learning (DAIL) problem, which is a unified framework for imitation learning in the presence of viewpoint, embodiment, and dynamics mismatch.
no code implementations • 25 Sep 2019 • Kun Ho Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon
Informally, CDIL is the process of learning how to perform a task optimally, given demonstrations of the task in a distinct domain.
1 code implementation • EMNLP 2018 • Yihong Gu, Jun Yan, Hao Zhu, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Fen Lin, Leyu Lin
Most language modeling methods rely on large-scale data to statistically learn the sequential patterns of words.
1 code implementation • 18 Sep 2017 • Jiaxin Shi, Jianfei Chen, Jun Zhu, Shengyang Sun, Yucen Luo, Yihong Gu, Yuhao Zhou
In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning.