35 citations · 124 across the 13 of their papers we have counts for
13 papers
Generative Multi-Form Bayesian Optimization
Zhendong Guo, Haitao Liu, Yew-Soon Ong +3
Many real-world problems, such as airfoil design, involve optimizing a black-box expensive objective function over complex structured input space (e.g., discrete space or non-Eucli…
Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training
Junhao Dong, Xinghua Qu, Z. Jane Wang +1
Despite remarkable achievements in deep learning across various domains, its inherent vulnerability to adversarial examples still remains a critical concern for practical deploymen…
Large Language Models for Intent-Driven Session Recommendations
Zhu Sun, Hongyang Liu, Xinghua Qu +3
Intent-aware session recommendation (ISR) is pivotal in discerning user intents within sessions for precise predictions. Traditional approaches, however, face limitations due to th…
Adaptive In-Context Learning with Large Language Models for Bundle Generation
Zhu Sun, Kaidong Feng, Jie Yang +4
Most existing bundle generation approaches fall short in generating fixed-size bundles. Furthermore, they often neglect the underlying user intents reflected by the bundles in the…
Large Language Models as Evolutionary Optimizers
Shengcai Liu, Caishun Chen, Xinghua Qu +2
Evolutionary algorithms (EAs) have achieved remarkable success in tackling complex combinatorial optimization problems. However, EAs often demand carefully-designed operators with…
Towards Building Voice-based Conversational Recommender Systems: Datasets, Potential Solutions, and Prospects
Xinghua Qu, Hongyang Liu, Zhu Sun +4
Conversational recommender systems (CRSs) have become crucial emerging research topics in the field of RSs, thanks to their natural advantages of explicitly acquiring user preferen…
Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective
Pengfei Wei, Lingdong Kong, Xinghua Qu +4
Unsupervised video domain adaptation is a practical yet challenging task. In this work, for the first time, we tackle it from a disentanglement view. Our key idea is to handle the…
DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation
Zhu Sun, Hui Fang, Jie Yang +5
Recently, one critical issue looms large in the field of recommender systems -- there are no effective benchmarks for rigorous evaluation -- which consequently leads to unreproduci…
Language Adaptive Cross-lingual Speech Representation Learning with Sparse Sharing Sub-networks
Yizhou Lu, Mingkun Huang, Xinghua Qu +2
Unsupervised cross-lingual speech representation learning (XLSR) has recently shown promising results in speech recognition by leveraging vast amounts of unlabeled data across mult…
An Improved Transfer Model: Randomized Transferable Machine
Pengfei Wei, Xinghua Qu, Yew Soon Ong +1
Feature-based transfer is one of the most effective methodologies for transfer learning. Existing studies usually assume that the learned new feature representation is \emph{domain…
Adversary Agnostic Robust Deep Reinforcement Learning
Xinghua Qu, Yew-Soon Ong, Abhishek Gupta +1
Deep reinforcement learning (DRL) policies have been shown to be deceived by perturbations (e.g., random noise or intensional adversarial attacks) on state observations that appear…
Subdomain Adaptation with Manifolds Discrepancy Alignment
Pengfei Wei, Yiping Ke, Xinghua Qu +1
Reducing domain divergence is a key step in transfer learning problems. Existing works focus on the minimization of global domain divergence. However, two domains may consist of se…
Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy
Xinghua Qu, Zhu Sun, Yew-Soon Ong +2
Recent studies have revealed that neural network-based policies can be easily fooled by adversarial examples. However, while most prior works analyze the effects of perturbing ever…