7 citations · 11 across the 15 of their papers we have counts for
13 papers · 1 filter
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…
Predicting Symptoms of Amotivation and Anhedonia among University Students with a Novel Oversampling Method
Dang Nguyen, Bao Duong, Arun Kumar +12
University students experience disproportionately high rates of common mental health conditions, such as depression, which can impair learning, social functioning, and overall well…
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…
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…
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…