Search Results for author: Chenxu Wang

Found 12 papers, 5 papers with code

Towards Objectively Benchmarking Social Intelligence for Language Agents at Action Level

no code implementations8 Apr 2024 Chenxu Wang, Bin Dai, Huaping Liu, Baoyuan Wang

To gauge the significance of agent architecture, we implement a target-driven planning (TDP) module as an adjunct to the existing agent.


Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation

no code implementations29 Feb 2024 Wentao Shi, Chenxu Wang, Fuli Feng, Yang Zhang, Wenjie Wang, Junkang Wu, Xiangnan He

Compared to AUC, LLPAUC considers only the partial area under the ROC curve in the Lower-Left corner to push the optimization focus on Top-K. We provide theoretical validation of the correlation between LLPAUC and Top-K ranking metrics and demonstrate its robustness to noisy user feedback.

Recommendation Systems

Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition

1 code implementation1 Nov 2023 Chenxu Wang, Ping Jian, Mu Huang

Essentially, our method seamlessly injects knowledge relevant to discourse relation into pre-trained language models through prompt-based connective prediction.

Discourse Parsing Language Modelling +2

Deep Reinforcement Learning with Task-Adaptive Retrieval via Hypernetwork

1 code implementation19 Jun 2023 Yonggang Jin, Chenxu Wang, Tianyu Zheng, Liuyu Xiang, Yaodong Yang, Junge Zhang, Jie Fu, Zhaofeng He

Deep reinforcement learning algorithms are usually impeded by sampling inefficiency, heavily depending on multiple interactions with the environment to acquire accurate decision-making capabilities.

Decision Making Hippocampus +2

Prediction then Correction: An Abductive Prediction Correction Method for Sequential Recommendation

1 code implementation27 Apr 2023 Yulong Huang, Yang Zhang, Qifan Wang, Chenxu Wang, Fuli Feng

To improve the accuracy of these models, some researchers have attempted to simulate human analogical reasoning to correct predictions for testing data by drawing analogies with the prediction errors of similar training data.

Sequential Recommendation

Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation

1 code implementation22 Sep 2022 Chenxu Wang, Fuli Feng, Yang Zhang, Qifan Wang, Xunhan Hu, Xiangnan He

A standard choice is treating the missing data as negative training samples and estimating interaction likelihood between user-item pairs along with the observed interactions.

Optimal Energy Scheduling and Sensitivity Analysis for Integrated Power-Water-Heat Systems

no code implementations1 Feb 2021 Sidun Fang, Chenxu Wang, Yashen Lin, Changhong Zhao

The conventionally independent power, water, and heating networks are becoming more tightly connected, which motivates their joint optimal energy scheduling to improve the overall efficiency of an integrated energy system.


CLUE: Towards Discovering Locked Cryptocurrencies in Ethereum

no code implementations2 Dec 2020 Xiaoqi Li, Ting Chen, Xiapu Luo, Chenxu Wang

Because the locked cryptocurrencies can never be controlled by users, avoid interacting with the accounts discovered by CLUE and repeating the same mistakes again can help users to save money.

Cryptography and Security

Revisiting Factorizing Aggregated Posterior in Learning Disentangled Representations

no code implementations12 Sep 2020 Ze Cheng, Juncheng Li, Chenxu Wang, Jixuan Gu, Hao Xu, Xinjian Li, Florian Metze

In this paper, we provide a theoretical explanation that low total correlation of sampled representation cannot guarantee low total correlation of the mean representation.

How to Retrain Recommender System? A Sequential Meta-Learning Method

1 code implementation27 May 2020 Yang Zhang, Fuli Feng, Chenxu Wang, Xiangnan He, Meng Wang, Yan Li, Yongdong Zhang

Nevertheless, normal training on new data only may easily cause overfitting and forgetting issues, since the new data is of a smaller scale and contains fewer information on long-term user preference.

Meta-Learning Recommendation Systems


no code implementations25 Sep 2019 Ze Cheng, Juncheng B Li, Chenxu Wang, Jixuan Gu, Hao Xu, Xinjian Li, Florian Metze

In the problem of unsupervised learning of disentangled representations, one of the promising methods is to penalize the total correlation of sampled latent vari-ables.


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