Search Results for author: Chaosheng Dong

Found 12 papers, 0 papers with code

G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer

no code implementations25 Jun 2023 Yuchen Zhuang, Xin Shen, Yan Zhao, Chaosheng Dong, Ming Wang, Jin Li, Chao Zhang

The detection of the underlying shopping intentions of users based on their historical interactions is a crucial aspect for e-commerce platforms, such as Amazon, to enhance the convenience and efficiency of their customers' shopping experiences.

Sequential Recommendation

AdaSelection: Accelerating Deep Learning Training through Data Subsampling

no code implementations19 Jun 2023 Minghe Zhang, Chaosheng Dong, Jinmiao Fu, Tianchen Zhou, Jia Liang, Jia Liu, Bo Liu, Michinari Momma, Bryan Wang, Yan Gao, Yi Sun

In this paper, we introduce AdaSelection, an adaptive sub-sampling method to identify the most informative sub-samples within each minibatch to speed up the training of large-scale deep learning models without sacrificing model performance.

Multi-Label Learning to Rank through Multi-Objective Optimization

no code implementations7 Jul 2022 Debabrata Mahapatra, Chaosheng Dong, Yetian Chen, Deqiang Meng, Michinari Momma

Moreover, it formulates multiple goals that may be conflicting yet important to optimize for simultaneously, e. g., in product search, a ranking model can be trained based on product quality and purchase likelihood to increase revenue.

Information Retrieval Learning-To-Rank +2

Incentivized Bandit Learning with Self-Reinforcing User Preferences

no code implementations19 May 2021 Tianchen Zhou, Jia Liu, Chaosheng Dong, Jingyuan Deng

In this paper, we investigate a new multi-armed bandit (MAB) online learning model that considers real-world phenomena in many recommender systems: (i) the learning agent cannot pull the arms by itself and thus has to offer rewards to users to incentivize arm-pulling indirectly; and (ii) if users with specific arm preferences are well rewarded, they induce a "self-reinforcing" effect in the sense that they will attract more users of similar arm preferences.

Recommendation Systems

One Backward from Ten Forward, Subsampling for Large-Scale Deep Learning

no code implementations27 Apr 2021 Chaosheng Dong, Xiaojie Jin, Weihao Gao, Yijia Wang, Hongyi Zhang, Xiang Wu, Jianchao Yang, Xiaobing Liu

Deep learning models in large-scale machine learning systems are often continuously trained with enormous data from production environments.

Inverse Multiobjective Optimization Through Online Learning

no code implementations12 Oct 2020 Chaosheng Dong, Yijia Wang, Bo Zeng

We study the problem of learning the objective functions or constraints of a multiobjective decision making model, based on a set of sequentially arrived decisions.

Decision Making Multiobjective Optimization

Learning Risk Preferences from Investment Portfolios Using Inverse Optimization

no code implementations4 Oct 2020 Shi Yu, Haoran Wang, Chaosheng Dong

Our approach allows the learner to continuously estimate real-time risk preferences using concurrent observed portfolios and market price data.

Decision Making Management

Wasserstein Distributionally Robust Inverse Multiobjective Optimization

no code implementations30 Sep 2020 Chaosheng Dong, Bo Zeng

To hedge against the uncertainties in the hypothetical DMP, the data, and the parameter space, we investigate in this paper the distributionally robust approach for inverse multiobjective optimization.

Decision Making Multiobjective Optimization +1

Generalized Inverse Optimization through Online Learning

no code implementations NeurIPS 2018 Chaosheng Dong, Yiran Chen, Bo Zeng

Inverse optimization is a powerful paradigm for learning preferences and restrictions that explain the behavior of a decision maker, based on a set of external signal and the corresponding decision pairs.

Inferring Parameters Through Inverse Multiobjective Optimization

no code implementations2 Aug 2018 Chaosheng Dong, Bo Zeng

Given a set of human's decisions that are observed, inverse optimization has been developed and utilized to infer the underlying decision making problem.

Decision Making Multiobjective Optimization

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