Machine learning and statistical methods in evolutionary biology
Uncertainty quantification, experimental design and control of biological systems
Machine learning
Learning with non-iid data
Active and fast-rate learning.
Journal papers and refereed proceedings
Nguyen, Cuong N., Phong Tran, Lam Si Tung Ho, Vu Dinh, Anh T. Tran, Tal Hassner, and Cuong V. Nguyen.
Simple Transferability Estimation for Regression Tasks. Uncertainty in Artificial Intelligence (UAI 2023)[pdf]
Vu, Nhat L., Thanh P. Nguyen, Binh T. Nguyen, Vu Dinh, and Lam Si Tung Ho.
When can we reconstruct the ancestral state? Beyond Brownian motion. Journal of Mathematical Biology 86.6 (2023) [arxiv]
Lam Si Tung Ho, and Vu Dinh.
When can we reconstruct the ancestral state? A unified theory. Theoretical Population Biology 148 (2022): 22-27[arxiv]
Miller, Allison E., Emily Russell, Darcy S. Reisman, Hyosub E. Kim, and Vu Dinh.
A machine learning approach to identifying important features for achieving step thresholds in individuals with chronic stroke. PLOS One 17.6 (2022): e0270105[doi]
Lam Si Tung Ho, and Vu Dinh.
Searching for minimal optimal neural networks. Statistics & Probability Letters 183 (2022): 109353[arxiv]
Cuong Nguyen, Lam Si Tung Ho, Huan Xu, Vu Dinh and Binh Nguyen.
Bayesian active learning with abstention feedbacks. Neurocomputing 471 (2022): 242-250[arxiv]
Cheng Zhang*, Vu Dinh* and Frederick A. Matsen IV.
Non-bifurcating phylogenetic inference via the adaptive lasso. Journal of the American Statistical Association 116.534 (2021): 858-873[arxiv]
Vu Dinh* and Lam Si Tung Ho*.
Convergence of maximum likelihood supertree reconstruction. AIMS Mathematics 6.8 (2021) [axiv]
Vu Dinh* and Lam Si Tung Ho*.
Consistent feature selection for analytic deep neural networks. Advances in Neural Information Processing Systems (NeurIPS 2020)[pdf]
Vu Dinh* and Lam Si Tung Ho*.
Consistent feature selection for neural networks via Adaptive Group Lasso.
[arxiv]
Lam Si Tung Ho, Binh T Nguyen, Vu Dinh, Duy Nguyen.
Posterior concentration and fast convergence rates for generalized Bayesian learning. Information Sciences 538 (2020): 372-383
Lam Si Tung Ho*, Vu Dinh*, Frederick A. Matsen IV and Marc A Suchard.
On the convergence of the maximum likelihood estimator for the transition rate under a 2-state symmetric model. Journal of Mathematical Biology 80.4 (2020): 1119-1138[arxiv]
David A. Shaw, Vu Dinh and Frederick A. Matsen IV.
Joint maximum likelihood of phylogeny and ancestral states is not consistent. Molecular Biology and Evolution 36.10 (2019): 2352-2357[doi][pdf]
Lam Si Tung Ho, Vu Dinh and Cuong V. Nguyen.
Multi-task learning improves ancestral state reconstruction. Theoretical Population Biology 126 (2019): 33-39[pdf]
Binh T. Nguyen, Duy M. Nguyen, Lam Si Tung Ho and Vu Dinh (2019).
An active learning framework for set inversion. Knowledge-Based Systems 185 (2019): 104917[pdf] A conference version of this paper wins the Best Paper Award at the 17th International Conference on Intelligent Software Methodologies, Tools and Techniques (SoMeT 2018, Granada, Spain, September 2018).
Vu Dinh*, Lam Si Tung Ho*, Marc A. Suchard and Frederick A. Matsen IV.
Consistency and convergence of phylogenetic inference with species tree regularization. The Annals of Statistics 46.4 (2018): 1481-1512[arxiv]
Vu Dinh, Aaron E. Darling and Frederick A. Matsen IV.
Online Bayesian phylogenetic inference: theoretical foundations via Sequential Monte Carlo. Systematic Biology 67.3 (2018) 503–517[arxiv]
Mathieu Fourment, Brian C. Claywell, Vu Dinh, Connor McCoy, Frederick A. Matsen IV and Aaron E. Darling.
Effective online Bayesian phylogenetics via Sequential Monte Carlo with guided proposals. Systematic Biology 67.3 (2018) 490–502[bioRxiv]
Brian C. Claywell, Vu Dinh, Mathieu Fourment, Connor O. McCoy and Frederick A. Matsen IV.
A surrogate function for one-dimensional phylogenetic likelihoods. Molecular Biology and Evolution 35.1 (2018), 242-246[arxiv]
Owen G. Rehrauer, Vu Dinh, Bharat R. Mankani, Gregery T. Buzzard, Bradley Lucier and Dor Ben-Amotz.
Binary-complementary compressive filters for Raman spectroscopy. Applied Spectroscopy 72.1 (2018): 69-78[pdf]
Vu Dinh*, Arman Bilge*, Cheng Zhang* and Frederick A. Matsen IV.
Probabilistic path Hamiltonian Monte Carlo. International Conference on Machine Learning (ICML 2017)
[arxiv]
Vu Dinh and Frederick A. Matsen IV.
The shape of the one-dimensional phylogenetic likelihood function. The Annals of Applied Probability 27.3 (2017): 1646-1677[arxiv]
Ankush Chakrabarty, Vu Dinh, Martin Corless, Ann E. Rundell, Stanislaw H. Zak and Gregery T. Buzzard.
SVM-informed explicit nonlinear model predictive control using low-discrepancy sequences. IEEE Transaction on Automatic Control 62.1 (2017): 135-148[pdf]
Vu Dinh, Ann E. Rundell and Gregery T. Buzzard.
Convergence of Griddy Gibbs sampling and other perturbed Markov chains. Journal of Statistical Computation and Simulation 88.7 (2017): 1379-1400[doi][pdf]
Vu Dinh, Lam Si Tung Ho, Binh T. Nguyen, Duy Nguyen.
Fast learning rates with heavy-tailed losses. Advances in Neural Information Processing Systems (NIPS 2016)
[pdf]
Vu Dinh*, Lam Si Tung Ho*, Nguyen Viet Cuong, Duy Nguyen and Binh T. Nguyen.
Learning from non-iid data: fast rates for the one-vs-all multiclass plug-in classifiers. Theory and Applications of Models of Computation (TAMC 2015) [arxiv]
Vu Dinh, Ann E. Rundell and Gregery T. Buzzard.
Experimental design for dynamic identification of cellular processes. Bulletin of Mathematical Biology 76.3 (2014): 597-626[arxiv]
Vu Dinh, Ann E. Rundell and Gregery T. Buzzard.
Effective sampling schemes for behavior discrimination in nonlinear systems. International Journal of Uncertainty Quantification 4.6 (2014): 535-554 [doi][pdf]
Ankush Chakrabarty, Vu Dinh, Gregery T. Buzzard, Stanislaw H. Zak and Ann E. Rundell.
Robust explicit nonlinear model predictive control with integral sliding mode. American Control Conference (ACC 2014)
[pdf]
Nguyen Viet Cuong, Lam Si Tung Ho and Vu Dinh.
Generalization and robustness of batched weighted average algorithm with V-geometrically ergodic Markov data. Algorithmic Learning Theory (ALT 2013) [arxiv]
Nguyen Viet Cuong, Vu Dinh and Lam Si Tung Ho.
Mel-frequency cepstral coefficients for eye movement identification. IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2012)
[doi][pdf]
Jeffrey P. Perley, Judith Mikolajczak, Vu Dinh,
Marietta L. Harrison, Gregery T. Buzzard and Ann E. Rundell.
Systematically manipulating T-cell signaling dynamics via multiple model informed open-loop controller design. IEEE Conference on Decision and Control (CDC 2012)
[doi][pdf]
Duong Minh Duc*, Ho Si Tung Lam*, Nguyen Quang Thang* and Dinh Cao Duy Thien Vu*.
On Harnack's inequality for non-uniformly p-Laplacian equations. Acta Mathematica Vietnamica 36.2 (2011):199-214 [pdf]