32 citations · 45 across the 10 of their papers we have counts for
12 papers · 1 filter
Online Data Selection for Instruction Tuning via Gaussian Processes
Jun Wang, Quoc Phong Nguyen, Julien Monteil +1
With Large Language Model (LLM) pre-training and fine-tuning shifting its focus from data volume to data quality, quality data selection has emerged as a critical research topic. E…
Batch Bayesian Optimization for Replicable Experimental Design
Zhongxiang Dai, Quoc Phong Nguyen, Sebastian Shenghong Tay +4
Many real-world experimental design problems (a) evaluate multiple experimental conditions in parallel and (b) replicate each condition multiple times due to large and heteroscedas…
Rectified Max-Value Entropy Search for Bayesian Optimization
Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
Although the existing max-value entropy search (MES) is based on the widely celebrated notion of mutual information, its empirical performance can suffer due to two misconceptions…
Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten
Quoc Phong Nguyen, Ryutaro Oikawa, Dinil Mon Divakaran +2
As the use of machine learning (ML) models is becoming increasingly popular in many real-world applications, there are practical challenges that need to be addressed for model main…
Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization
Quoc Phong Nguyen, Zhaoxuan Wu, Bryan Kian Hsiang Low +1
Information-based Bayesian optimization (BO) algorithms have achieved state-of-the-art performance in optimizing a black-box objective function. However, they usually require sever…
Value-at-Risk Optimization with Gaussian Processes
Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low +1
Value-at-risk (VaR) is an established measure to assess risks in critical real-world applications with random environmental factors. This paper presents a novel VaR upper confidenc…