1 citations · 1 across the 11 of their papers we have counts for
12 papers
Geometry-Aware Bayesian Parameter-Efficient Fine-Tuning on the Stiefel Manifold via Stein Variational Gradient Descent
Quang-Duy Tran, Trung Le, Bao Duong +2
Several geometry-aware approaches to low-rank adaptation have emerged for parameter-efficient fine-tuning of large pre-trained models. These methods aim to take full advantage of t…
Neural Autoregressive Flows for Markov Boundary Learning
Khoa Nguyen, Bao Duong, Viet Huynh +1
Recovering Markov boundary -- the minimal set of variables that maximizes predictive performance for a response variable -- is crucial in many applications. While recent advances i…
Tractable Approximation of Labeled Multi-Object Posterior Densities
Thi Hong Thai Nguyen, Ba-Ngu Vo, Ba-Tuong Vo
Multi-object estimation in state-space models (SSMs) wherein the system state is represented as a finite set has attracted significant interest in recent years. In Bayesian inferen…
Identifying Causal Direction via Variational Bayesian Compression
Quang-Duy Tran, Bao Duong, Phuoc Nguyen +1
Telling apart the cause and effect between two random variables with purely observational data is a challenging problem that finds applications in various scientific disciplines. A…
Clustering-based Meta Bayesian Optimization with Theoretical Guarantee
Khoa Nguyen, Viet Huynh, Binh Tran +3
Bayesian Optimization (BO) is a well-established method for addressing black-box optimization problems. In many real-world scenarios, optimization often involves multiple functions…
Amortized Conditional Independence Testing
Bao Duong, Nu Hoang, Thin Nguyen
Testing for the conditional independence structure in data is a fundamental and critical task in statistics and machine learning, which finds natural applications in causal discove…
Causal Discovery via Bayesian Optimization
Bao Duong, Sunil Gupta, Thin Nguyen
Existing score-based methods for directed acyclic graph (DAG) learning from observational data struggle to recover the causal graph accurately and sample-efficiently. To overcome t…
Scalable Variational Causal Discovery Unconstrained by Acyclicity
Nu Hoang, Bao Duong, Thin Nguyen
Bayesian causal discovery offers the power to quantify epistemic uncertainties among a broad range of structurally diverse causal theories potentially explaining the data, represen…
Enabling Causal Discovery in Post-Nonlinear Models with Normalizing Flows
Nu Hoang, Bao Duong, Thin Nguyen
Post-nonlinear (PNL) causal models stand out as a versatile and adaptable framework for modeling intricate causal relationships. However, accurately capturing the invertibility con…
Reinforcement Learning for Causal Discovery without Acyclicity Constraints
Bao Duong, Hung Le, Biwei Huang +1
Recently, reinforcement learning (RL) has proved a promising alternative for conventional local heuristics in score-based approaches to learning directed acyclic causal graphs (DAG…
Root Cause Explanation of Outliers under Noisy Mechanisms
Phuoc Nguyen, Truyen Tran, Sunil Gupta +2
Identifying root causes of anomalies in causal processes is vital across disciplines. Once identified, one can isolate the root causes and implement necessary measures to restore t…
Robust Estimation of Causal Heteroscedastic Noise Models
Quang-Duy Tran, Bao Duong, Phuoc Nguyen +1
Distinguishing the cause and effect from bivariate observational data is the foundational problem that finds applications in many scientific disciplines. One solution to this probl…