no code implementations • 23 Feb 2024 • Zhuojun Quan, Yuanyuan Lin, Kani Chen, Wen Yu
We find out that with the availability of the unlabeled data, the intercept parameter can be identified in semi-supervised learning setting.
no code implementations • 27 Oct 2023 • Peng Xie, Zihao Xin, Yang Wang, Shengjun Huang, Tsz Wai Chan, Kani Chen
We proposed a novel evaluation metric called FAL, which assesses an Automatic Speech Recognition (ASR) system based on fidelity to the original audio, accuracy, and latency.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 16 Sep 2023 • Zhiwei Zhang, Weizhong Zhang, Yaowei Huang, Kani Chen
In this paper, we identify an underexplored problem in multivariate traffic series prediction: extreme events.
no code implementations • 14 Sep 2022 • Xinwei Shen, Kani Chen, Tong Zhang
We show that for parametric generative models that are correctly specified, all $f$-divergence GANs with the same discriminator classes are asymptotically equivalent under suitable regularity conditions.
no code implementations • 14 Jan 2022 • Mengyue Zha, Kani Chen, Tong Zhang
We enhance the accuracy and generalization of univariate time series point prediction by an explainable ensemble on the fly.
1 code implementation • 14 Jan 2022 • Mengyue Zha, SiuTim Wong, Mengqi Liu, Tong Zhang, Kani Chen
This paper shows that masked autoencoder with extrapolator (ExtraMAE) is a scalable self-supervised model for time series generation.
2 code implementations • 21 Feb 2020 • Xinwei Shen, Tong Zhang, Kani Chen
This paper considers the general $f$-divergence formulation of bidirectional generative modeling, which includes VAE and BiGAN as special cases.
no code implementations • 20 Feb 2020 • Ruijian Han, Yiming Xu, Kani Chen
Under this setup, we show that the maximum likelihood estimator for the latent score vector of the subjects is uniformly consistent under a near-minimal condition on network sparsity.
no code implementations • 12 Oct 2019 • Ruijian Han, Kani Chen, Chunxi Tan
The design of recommendations strategies in the adaptive learning system focuses on utilizing currently available information to provide individual-specific learning instructions for learners.