7 papers
TimeLAVA: Learning-Agnostic Valuation for Time Series Data
Wenqin Liu, Weizhi Quan, Aoqi Zuo +5
Data valuation quantifies the intrinsic quality of individual samples to enable principled data curation, quality control, and robust learning. For time series in critical domains…
Adaptive Hard-Soft Physics-Informed Neural Networks for Robust Boundary-Constrained PDE Solving
Duc Tien Nguyen, Trinh Minh Tuan, Nguyen Duc Manh +2
Physics-informed neural networks (PINNs) provide an effective way to solve partial differential equations (PDEs) by embedding physical principles into the learning process. However…
MUSS: Multilevel Subset Selection for Relevance and Diversity
Vu Nguyen, Andrey Kan
The problem of relevant and diverse subset selection has a wide range of applications, including recommender systems and retrieval-augmented generation (RAG). For example, in recom…
On the Mechanisms of Collaborative Learning in VAE Recommenders
Tung-Long Vuong, Julien Monteil, Hien Dang +3
Variational Autoencoders (VAEs) are a powerful alternative to matrix factorization for recommendation. A common technique in VAE-based collaborative filtering (CF) consists in appl…
Rejection via Learning Density Ratios
Alexander Soen, Hisham Husain, Philip Schulz +1
Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions. The predominant approach is to alter the supervised learning pi…
High Dimensional Bayesian Optimization using Lasso Variable Selection
Vu Viet Hoang, Hung The Tran, Sunil Gupta +1
Bayesian optimization (BO) is a leading method for optimizing expensive black-box optimization and has been successfully applied across various scenarios. However, BO suffers from…