7 citations · 11 across the 16 of their papers we have counts for
17 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…
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…
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…
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…
Domain Generalisation via Risk Distribution Matching
Toan Nguyen, Kien Do, Bao Duong +1
We propose a novel approach for domain generalisation (DG) leveraging risk distributions to characterise domains, thereby achieving domain invariance. In our findings, risk distrib…
Differentiable Bayesian Structure Learning with Acyclicity Assurance
Quang-Duy Tran, Phuoc Nguyen, Bao Duong +1
Score-based approaches in the structure learning task are thriving because of their scalability. Continuous relaxation has been the key reason for this advancement. Despite achievi…
Heteroscedastic Causal Structure Learning
Bao Duong, Thin Nguyen
Heretofore, learning the directed acyclic graphs (DAGs) that encode the cause-effect relationships embedded in observational data is a computationally challenging problem. A recent…
Causal Inference via Style Transfer for Out-of-distribution Generalisation
Toan Nguyen, Kien Do, Duc Thanh Nguyen +2
Out-of-distribution (OOD) generalisation aims to build a model that can generalise well on an unseen target domain using knowledge from multiple source domains. To this end, the mo…
Diffeomorphic Information Neural Estimation
Bao Duong, Thin Nguyen
Mutual Information (MI) and Conditional Mutual Information (CMI) are multi-purpose tools from information theory that are able to naturally measure the statistical dependencies bet…
Conditional Independence Testing via Latent Representation Learning
Bao Duong, Thin Nguyen
Detecting conditional independencies plays a key role in several statistical and machine learning tasks, especially in causal discovery algorithms. In this study, we introduce LCIT…
Efficient Classification with Counterfactual Reasoning and Active Learning
Azhar Mohammed, Dang Nguyen, Bao Duong +1
Data augmentation is one of the most successful techniques to improve the classification accuracy of machine learning models in computer vision. However, applying data augmentation…