Portrait de Ye Yuan

Ye Yuan

ye.yuan3@mail.mcgill.ca
Doctorat - McGill
Superviseur⋅e principal⋅e
Xue (Steve) Liu
Sujets de recherche
Apprentissage profond
IA pour la science
Modèle de langage
Modèles génératifs
Traitement du langage naturel
Site web
Google Scholar
GitHub

Publications

Offline Model-Based Optimization: Comprehensive Review
Minsu Kim
Jiayao Gu
Ye Yuan
Taeyoung YUN
Zixuan Liu
Yoshua Bengio
Can Chen
2025-03-20
ArXiv (prépublication)
doi.org
arxiv.org
Paretodlow: Guided Flows in Multi-Objective Optimization
Ye Yuan
Can (Sam) Chen
Christopher Pal
Xue Liu
In offline multi-objective optimization (MOO), we leverage an offline dataset of designs and their associated labels to simultaneously minim… (voir plus)ize multiple objectives. This setting more closely mirrors complex real-world problems compared to single-objective optimization. Recent works mainly employ evolutionary algorithms and Bayesian optimization, with limited attention given to the generative modeling capabilities inherent in such data. In this study, we explore generative modeling in offline MOO through flow matching, noted for its effectiveness and efficiency. We introduce ParetoFlow, specifically designed to guide flow sampling to approximate the Pareto front. Traditional predictor (classifier) guidance is inadequate for this purpose because it models only a single objective. In response, we propose a multi-objective predictor guidance module that assigns each sample a weight vector, representing a weighted distribution across multiple objective predictions. A local filtering scheme is introduced to address non-convex Pareto fronts. These weights uniformly cover the entire objective space, effectively directing sample generation towards the Pareto front. Since distributions with similar weights tend to generate similar samples, we introduce a neighboring evolution module to foster knowledge sharing among neighboring distributions. This module generates offspring from these distributions, and selects the most promising one for the next iteration. Our method achieves state-of-the-art performance across various tasks.
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Generative AI as a Service in 6G Edge-Cloud: Generation Task Offloading by In-context Learning
Hao Zhou
Chengming Hu
Dun Yuan
Ye Yuan
Di Wu
Xue Liu
Zhu Han
Jianzhong (Charlie) Zhang
Generative artificial intelligence (GAI) is a promising technique towards 6G networks, and generative foundation models such as large langua… (voir plus)ge models (LLMs) have attracted considerable interest from academia and telecom industry. This work considers a novel edge-cloud deployment of foundation models in 6G networks. Specifically, it aims to minimize the service delay of foundation models by radio resource allocation and task offloading, i.e., offloading diverse content generation tasks to proper LLMs at the network edge or cloud. In particular, we first introduce the communication system model, i.e., allocating radio resources and calculating link capacity to support generated content transmission, and then we present the LLM inference model to calculate the delay of content generation. After that, we propose a novel in-context learning method to optimize the task offloading decisions. It utilizes LLM's inference capabilities, and avoids the difficulty of dedicated model training or fine-tuning as in conventional machine learning algorithms. Finally, the simulations demonstrate that the proposed edge-cloud deployment and in-context learning task offloading method can achieve satisfactory generation service quality without dedicated model training or fine-tuning.
2024-12-22
IEEE Wireless Communications Letters (publié)
doi.org
arxiv.org
Importance-aware Co-teaching for Offline Model-based Optimization
Ye Yuan
Can Chen
Zixuan Liu
Willie Neiswanger
Xue Liu
2023-09-20
NeurIPS.cc/2023/Conference (poster)
openreview.net
openreview.net
TaHiD: Tackling Data Hiding in Fake News Detection with News Propagation Networks
Adrien Benamira
Benjamin Devillers
Etienne Lesot
Ayush K. Ray
Manal Saadi
Fragkiskos D 587
Steven Bird
Ewan Klein
Edward Loper
Nat-593
Carlos Castillo
Marcelo Mendoza
Barbara Poblete
Daryna Dementieva
Alexander Panchenko
Jacob Devlin
Ming-Wei Chang
Kenton Lee
Ashish Vaswani
Noam M. Shazeer … (voir 8 de plus)
Niki Parmar
Adriana Romero
Pietro Lio’
Yoshua Bengio
Yaqing Wang
Fenglong Ma
Zhiwei Jin
Ye Yuan
Fake news with detrimental societal effects has 001 attracted extensive attention and research. De-002 spite early success, the state-of-the… (voir plus)-art meth-003 ods fall short of considering the propagation 004 of news. News propagates at different times 005 through different mediums, including users, 006 comments, and sources, which form the news 007 propagation network. Moreover, the serious 008 problem of data hiding arises, which means 009 that fake news publishers disguise fake news 010 as real to confuse users by deleting comments 011 that refute the rumor or deleting the news itself 012 when it has been spread widely. Existing meth-013 ods do not consider the propagation of news 014 and fail to identify what matters in the process, 015 which leads to fake news hiding in the prop-016 agation network and escaping from detection. 017 Inspired by the propagation of news, we pro-018 pose a novel fake news detection framework 019 named TaHiD, which models the propagation 020 as a heterogeneous dynamic graph and contains 021 the propagation attention module to measure 022 the influence of different propagation. Exper-023 iments demonstrate that TaHiD extracts use-024 ful information from the news propagation net-025 work and outperforms state-of-the-art methods 026 on several benchmark datasets for fake news 027 detection. Additional studies also show that 028 TaHiD is capable of identifying fake news in 029 the case of data hiding. 030
2021-12-31
(publié)
www.semanticscholar.org