Search Results for author: Siyu Wang

Found 23 papers, 3 papers with code

Automatic Keyphrase Generation by Incorporating Dual Copy Mechanisms in Sequence-to-Sequence Learning

no code implementations COLING 2022 Siyu Wang, Jianhui Jiang, Yao Huang, Yin Wang

However, we observed that most of the keyphrases are composed of some important words (seed words) in the source text, and if these words can be identified accurately and copied to create more keyphrases, the performance of the model might be improved.

Keyphrase Generation

On Causally Disentangled State Representation Learning for Reinforcement Learning based Recommender Systems

no code implementations18 Jul 2024 Siyu Wang, Xiaocong Chen, Lina Yao

To tackle this problem, we introduce an innovative causal approach for decomposing the state and extracting \textbf{C}ausal-\textbf{I}n\textbf{D}ispensable \textbf{S}tate Representations (CIDS) in RLRS.

Maximum-Entropy Regularized Decision Transformer with Reward Relabelling for Dynamic Recommendation

no code implementations2 Jun 2024 Xiaocong Chen, Siyu Wang, Lina Yao

In response, we introduce a novel methodology named Max-Entropy enhanced Decision Transformer with Reward Relabeling for Offline RLRS (EDT4Rec).

Recommendation Systems reinforcement-learning

Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents

no code implementations5 May 2024 Junkai Li, Siyu Wang, Meng Zhang, Weitao Li, Yunghwei Lai, Xinhui Kang, Weizhi Ma, Yang Liu

In this paper, we introduce a simulacrum of hospital called Agent Hospital that simulates the entire process of treating illness.

Question Answering

Uncertainty-aware Distributional Offline Reinforcement Learning

no code implementations26 Mar 2024 Xiaocong Chen, Siyu Wang, Tong Yu, Lina Yao

Offline reinforcement learning (RL) presents distinct challenges as it relies solely on observational data.

Offline RL reinforcement-learning +1

Retentive Decision Transformer with Adaptive Masking for Reinforcement Learning based Recommendation Systems

no code implementations26 Mar 2024 Siyu Wang, Xiaocong Chen, Lina Yao

Reinforcement Learning-based Recommender Systems (RLRS) have shown promise across a spectrum of applications, from e-commerce platforms to streaming services.

Computational Efficiency Decision Making +1

Computation Offloading for Multi-server Multi-access Edge Vehicular Networks: A DDQN-based Method

no code implementations21 Feb 2024 Siyu Wang, Bo Yang, Zhiwen Yu, Xuelin Cao, Yan Zhang, Chau Yuen

In this paper, we investigate a multi-user offloading problem in the overlapping domain of a multi-server mobile edge computing system.

Decision Making Edge-computing +1

On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems

no code implementations22 Aug 2023 Xiaocong Chen, Siyu Wang, Julian McAuley, Dietmar Jannach, Lina Yao

Offline reinforcement learning empowers agents to glean insights from offline datasets and deploy learned policies in online settings.

Recommendation Systems reinforcement-learning

Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation

no code implementations17 Apr 2023 Siyu Wang, Xiaocong Chen, Quan Z. Sheng, Yihong Zhang, Lina Yao

This paper introduces the Causal Disentangled Variational Auto-Encoder (CaD-VAE), a novel approach for learning causal disentangled representations from interaction data in recommender systems.

Decision Making Disentanglement +1

Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform

no code implementations16 Feb 2023 Shiwei Zhang, Lansong Diao, Siyu Wang, Zongyan Cao, Yiliang Gu, Chang Si, Ziji Shi, Zhen Zheng, Chuan Wu, Wei Lin

We present Rhino, a system for accelerating tensor programs with automatic parallelization on AI platform for real production environment.

Expediting Distributed DNN Training with Device Topology-Aware Graph Deployment

no code implementations13 Feb 2023 Shiwei Zhang, Xiaodong Yi, Lansong Diao, Chuan Wu, Siyu Wang, Wei Lin

This paper presents TAG, an automatic system to derive optimized DNN training graph and its deployment onto any device topology, for expedited training in device- and topology- heterogeneous ML clusters.

Combinatorial Optimization Graph Neural Network +1

Intrinsically Motivated Reinforcement Learning based Recommendation with Counterfactual Data Augmentation

no code implementations17 Sep 2022 Xiaocong Chen, Siyu Wang, Lina Yao, Lianyong Qi, Yong Li

It is more challenging to balance the exploration and exploitation in DRL RS where RS agent need to deeply explore the informative trajectories and exploit them efficiently in the context of recommender systems.

counterfactual Data Augmentation +3

Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation

no code implementations10 Aug 2022 Siyu Wang, Xiaocong Chen, Lina Yao, Sally Cripps, Julian McAuley

Recent advances in recommender systems have proved the potential of Reinforcement Learning (RL) to handle the dynamic evolution processes between users and recommender systems.

counterfactual Data Augmentation +3

Fast Lossless Neural Compression with Integer-Only Discrete Flows

1 code implementation17 Jun 2022 Siyu Wang, Jianfei Chen, Chongxuan Li, Jun Zhu, Bo Zhang

In this work, we propose Integer-only Discrete Flows (IODF), an efficient neural compressor with integer-only arithmetic.


Model-agnostic Counterfactual Synthesis Policy for Interactive Recommendation

no code implementations1 Apr 2022 Siyu Wang, Xiaocong Chen, Lina Yao

Recent advances have convinced that the ability of reinforcement learning to handle the dynamic process can be effectively applied in the interactive recommendation.

counterfactual reinforcement-learning +1

Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems

no code implementations2 Dec 2021 Siyu Wang, Yuanjiang Cao, Xiaocong Chen, Lina Yao, Xianzhi Wang, Quan Z. Sheng

Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods.

Adversarial Robustness counterfactual +3

Streaming Language Identification using Combination of Acoustic Representations and ASR Hypotheses

no code implementations1 Jun 2020 Chander Chandak, Zeynab Raeesy, Ariya Rastrow, Yuzong Liu, Xiangyang Huang, Siyu Wang, Dong Kwon Joo, Roland Maas

A common approach to solve multilingual speech recognition is to run multiple monolingual ASR systems in parallel and rely on a language identification (LID) component that detects the input language.

Language Identification speech-recognition +1

Curls & Whey: Boosting Black-Box Adversarial Attacks

1 code implementation CVPR 2019 Yucheng Shi, Siyu Wang, Yahong Han

On the one hand, existing iterative attacks add noises monotonically along the direction of gradient ascent, resulting in a lack of diversity and adaptability of the generated iterative trajectories.

Adversarial Attack Diversity

AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine

no code implementations ACL 2017 Minghui Qiu, Feng-Lin Li, Siyu Wang, Xing Gao, Yan Chen, Weipeng Zhao, Haiqing Chen, Jun Huang, Wei Chu

We propose AliMe Chat, an open-domain chatbot engine that integrates the joint results of Information Retrieval (IR) and Sequence to Sequence (Seq2Seq) based generation models.

Chatbot Information Retrieval +1

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