367 citations · 1.3k across the 62 of their papers we have counts for
64 papers
Solving Few-Shot Multiobjective Multitask Optimization via Iterative Sequential Transfer
Tingyang Wei, Haofeng Wu, Ananda Phan Iman +3
Applying knowledge transfer across multiple optimization tasks, multitask optimization (MTO) emerges as a promising approach to solving synergistic optimization tasks simultaneousl…
Interpretable and Fair Generalized Additive Neural Networks via Multi-objective Learning
Ziming Wang, Changwu Huang, Ke Tang +2
Interpretability and fairness are two of the most emphasized dimensions in trustworthy artificial intelligence (AI). Various explainable AI methods have been introduced to improve…
PETA:Parameter-Efficient Test-Time Adaptation for Virtual Screening
Jia-Qi Lin, Yinghua Yao, Chang-Dong Wang +2
Accurately ranking active ligands for a target protein pocket from massive chemical libraries remains a central challenge in virtual screening. DrugCLIP and its recent extensions s…
Insights from Multi-tasking the EAX Algorithm for the Travelling Salesperson Problem
Liam Wigney, Aneta Neumann, Yew-Soon Ong +1
Evolutionary multitasking allows several related problems to be solved in a single run of an algorithm. In this paper, we investigate integrating evolutionary multitasking with Edg…
Novelty Search with Cross-Task Collaborative Discovery
Jiao Liu, Yanchi Li, Hua Yu +2
Novelty Search (NS) promotes exploration by rewarding behaviorally novel solutions rather than directly optimizing predefined objectives, making it particularly useful when objecti…
Few-Shot Domain Incremental Learning via Continual Vision-Language Consolidation
Naeem Paeedeh, Mahardhika Pratama, Wolfgang Mayer +3
Existing domain-incremental learning (DIL) strategies call for massive amounts of data to adapt to new domains and suffer from the overfitting problem in the case of data scarcity.…
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…
Co-Learning Bayesian Optimization
Zhendong Guo, Yew-Soon Ong, Tiantian He +1
Bayesian optimization (BO) is well known to be sample-efficient for solving black-box problems. However, the BO algorithms can sometimes get stuck in suboptimal solutions even with…
MEGO: Learning Mixture-of-Experts for General-Purpose Binary Optimization
Shengcai Liu, Zhiyuan Wang, Yew-Soon Ong +2
Discrete optimization is ubiquitous in science and engineering. The vast array of existing discrete optimization problems, coupled with the continuous emergence of new ones, necess…
Meta-learning enhanced next POI recommendation by leveraging check-ins from auxiliary cities
Jinze Wang, Lu Zhang, Zhu Sun +1
Most existing point-of-interest (POI) recommenders aim to capture user preference by employing city-level user historical check-ins, thus facilitating users' exploration of the cit…
Neural Influence Estimator: Towards Real-time Solutions to Influence Blocking Maximization
Wenjie Chen, Shengcai Liu, Yew-Soon Ong +2
Real-time solutions to the influence blocking maximization (IBM) problems are crucial for promptly containing the spread of misinformation. However, achieving this goal is non-triv…
Chance-Constrained Multiple-Choice Knapsack Problem: Model, Algorithms, and Applications
Xuanfeng Li, Shengcai Liu, Jin Wang +3
The multiple-choice knapsack problem (MCKP) is a classic NP-hard combinatorial optimization problem. Motivated by several significant real-world applications, this work investigate…
Prompt Evolution for Generative AI: A Classifier-Guided Approach
Melvin Wong, Yew-Soon Ong, Abhishek Gupta +2
Synthesis of digital artifacts conditioned on user prompts has become an important paradigm facilitating an explosion of use cases with generative AI. However, such models often fa…
Bayesian Federated Learning: A Survey
Longbing Cao, Hui Chen, Xuhui Fan +3
Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure, communication, computing and learning in a privacy-preserving manner. However, the ro…
LSA-PINN: Linear Boundary Connectivity Loss for Solving PDEs on Complex Geometry
Jian Cheng Wong, Pao-Hsiung Chiu, Chinchun Ooi +2
We present a novel loss formulation for efficient learning of complex dynamics from governing physics, typically described by partial differential equations (PDEs), using physics-i…
Policy Dispersion in Non-Markovian Environment
Bohao Qu, Xiaofeng Cao, Jielong Yang +4
Markov Decision Process (MDP) presents a mathematical framework to formulate the learning processes of agents in reinforcement learning. MDP is limited by the Markovian assumption…
A Generic Reinforced Explainable Framework with Knowledge Graph for Session-based Recommendation
Huizi Wu, Hui Fang, Zhu Sun +3
Session-based recommendation (SR) has gained increasing attention in recent years. Quite a great amount of studies have been devoted to designing complex algorithms to improve reco…
Fast, broad-band magnetic resonance spectroscopy with diamond widefield relaxometry
C. Mignon, A. R. Ortiz Moreno, H. Shirzad +5
We present an alternative to conventional Electron Paramagnetic Resonance (EPR) spectroscopy equipment. Avoiding the use of bulky magnets and magnetron equipment, we use the photol…
Not All Neighbors Are Worth Attending to: Graph Selective Attention Networks for Semi-supervised Learning
Tiantian He, Haicang Zhou, Yew-Soon Ong +1
Graph attention networks (GATs) are powerful tools for analyzing graph data from various real-world scenarios. To learn representations for downstream tasks, GATs generally attend…
A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals
Zhu Sun, Yu Lei, Lu Zhang +3
Current study on next POI recommendation mainly explores user sequential transitions with the fine-grained individual-user POI check-in trajectories only, which suffers from the se…
Understanding Diversity in Session-Based Recommendation
Qing Yin, Hui Fang, Zhu Sun +1
Current session-based recommender systems (SBRSs) mainly focus on maximizing recommendation accuracy, while few studies have been devoted to improve diversity beyond accuracy. Mean…
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…
Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction
Qiancheng Fu, Qingshan Xu, Yew-Soon Ong +1
Recently, neural implicit surfaces learning by volume rendering has become popular for multi-view reconstruction. However, one key challenge remains: existing approaches lack expli…
Jacobian Granger Causal Neural Networks for Analysis of Stationary and Nonstationary Data
Suryadi, Yew-Soon Ong, Lock Yue Chew
Granger causality is a commonly used method for uncovering information flow and dependencies in a time series. Here we introduce JGC (Jacobian Granger Causality), a neural network-…