38 citations · 94 across the 13 of their papers we have counts for
15 papers
BACH: A Bayesian Admixture of Contrastive Heads for Multi-Interest Two-Tower Retrieval
Quoc Phong Nguyen, Paul Albert, Long Vuong +2
Two-tower retrievers compress each user into a single embedding, limiting their ability to serve diverse interests. Multi-interest models give each user several heads scored by a m…
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…
Estimation of Recursive Route Choice Models with Incomplete Trip Observations
Tien Mai, The Viet Bui, Quoc Phong Nguyen +1
This work concerns the estimation of recursive route choice models in the situation that the trip observations are incomplete, i.e., there are unconnected links (or nodes) in the o…
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…
An Information-Theoretic Framework for Unifying Active Learning Problems
Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
This paper presents an information-theoretic framework for unifying active learning problems: level set estimation (LSE), Bayesian optimization (BO), and their generalized variant.…
Top- Ranking Bayesian Optimization
Quoc Phong Nguyen, Sebastian Tay, Bryan Kian Hsiang Low +1
This paper presents a novel approach to top- ranking Bayesian optimization (top- ranking BO) which is a practical and significant generalization of preferential BO to handle…
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization
Sreejith Balakrishnan, Quoc Phong Nguyen, Bryan Kian Hsiang Low +1
The problem of inverse reinforcement learning (IRL) is relevant to a variety of tasks including value alignment and robot learning from demonstration. Despite significant algorithm…
Variational Bayesian Unlearning
Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
This paper studies the problem of approximately unlearning a Bayesian model from a small subset of the training data to be erased. We frame this problem as one of minimizing the Ku…
Inverse Reinforcement Learning with Missing Data
Tien Mai, Quoc Phong Nguyen, Kian Hsiang Low +1
We consider the problem of recovering an expert's reward function with inverse reinforcement learning (IRL) when there are missing/incomplete state-action pairs or observations in…
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection
Quoc Phong Nguyen, Kar Wai Lim, Dinil Mon Divakaran +2
This paper looks into the problem of detecting network anomalies by analyzing NetFlow records. While many previous works have used statistical models and machine learning technique…
A Data-Scalable Randomized Misfit Approach for Solving Large-Scale PDE-Constrained Inverse Problems
Ellen B. Le, Aaron Myers, Tan Bui-Thanh +1
A randomized misfit approach is presented for the efficient solution of large-scale PDE-constrained inverse problems with high-dimensional data. The purpose of this paper is to off…