Search Results for author: Yu-Jie Zhang

Found 16 papers, 0 papers with code

Adapting to Continuous Covariate Shift via Online Density Ratio Estimation

no code implementations6 Feb 2023 Yu-Jie Zhang, Zhen-Yu Zhang, Peng Zhao, Masashi Sugiyama

Our density ratio estimation method is proven to perform well by enjoying a dynamic regret bound, which finally leads to an excess risk guarantee for the predictor.

Density Ratio Estimation

Adapting to Online Label Shift with Provable Guarantees

no code implementations5 Jul 2022 Yong Bai, Yu-Jie Zhang, Peng Zhao, Masashi Sugiyama, Zhi-Hua Zhou

In this paper, we formulate and investigate the problem of \emph{online label shift} (OLaS): the learner trains an initial model from the labeled offline data and then deploys it to an unlabeled online environment where the underlying label distribution changes over time but the label-conditional density does not.

Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization

no code implementations29 Dec 2021 Peng Zhao, Yu-Jie Zhang, Lijun Zhang, Zhi-Hua Zhou

Specifically, we introduce novel online algorithms that can exploit smoothness and replace the dependence on $T$ in dynamic regret with problem-dependent quantities: the variation in gradients of loss functions, the cumulative loss of the comparator sequence, and the minimum of these two terms.

Dynamic Regret of Convex and Smooth Functions

no code implementations NeurIPS 2020 Peng Zhao, Yu-Jie Zhang, Lijun Zhang, Zhi-Hua Zhou

We investigate online convex optimization in non-stationary environments and choose the dynamic regret as the performance measure, defined as the difference between cumulative loss incurred by the online algorithm and that of any feasible comparator sequence.

Exploratory Machine Learning with Unknown Unknowns

no code implementations5 Feb 2020 Yu-Jie Zhang, Peng Zhao, Zhi-Hua Zhou

In conventional supervised learning, a training dataset is given with ground-truth labels from a known label set, and the learned model will classify unseen instances to the known labels.

BIG-bench Machine Learning

Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching

no code implementations IJCNLP 2019 Mingtong Liu, Yu-Jie Zhang, Jinan Xu, Yufeng Chen

Unlike existing models, each attention layer of OSOA-DFN is oriented to the original semantic representation of another sentence, which captures the relevant information from a fixed matching target.

Natural Language Inference Paraphrase Identification

An Unbiased Risk Estimator for Learning with Augmented Classes

no code implementations NeurIPS 2020 Yu-Jie Zhang, Peng Zhao, Zhi-Hua Zhou

This paper studies the problem of learning with augmented classes (LAC), where augmented classes unobserved in the training data might emerge in the testing phase.

Deep learning based inverse method for layout design

no code implementations7 Jun 2018 Yu-Jie Zhang, Wenjing Ye

Layout design with complex constraints is a challenging problem to solve due to the non-uniqueness of the solution and the difficulties in incorporating the constraints into the conventional optimization-based methods.

Layout Design

System Description of bjtu\_nlp Neural Machine Translation System

no code implementations WS 2016 Shaotong Li, Jinan Xu, Yufeng Chen, Yu-Jie Zhang

This paper presents our machine translation system that developed for the WAT2016 evalua-tion tasks of ja-en, ja-zh, en-ja, zh-ja, JPCja-en, JPCja-zh, JPCen-ja, JPCzh-ja.

Machine Translation Translation +1

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