30 citations · 66 across the 16 of their papers we have counts for
16 papers
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery
Yashas Annadani, Nick Pawlowski, Joel Jennings +3
Bayesian causal discovery aims to infer the posterior distribution over causal models from observed data, quantifying epistemic uncertainty and benefiting downstream tasks. However…
Learnable Topological Features for Phylogenetic Inference via Graph Neural Networks
Cheng Zhang
Structural information of phylogenetic tree topologies plays an important role in phylogenetic inference. However, finding appropriate topological structures for specific phylogene…
Optimistic Optimization of Gaussian Process Samples
Julia Grosse, Cheng Zhang, Philipp Hennig
Bayesian optimization is a popular formalism for global optimization, but its computational costs limit it to expensive-to-evaluate functions. A competing, computationally more eff…
Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation
Desi R. Ivanova, Joel Jennings, Cheng Zhang +1
The real-world testing of decisions made using causal machine learning models is an essential prerequisite for their successful application. We focus on evaluating and improving co…
A Variational Approach to Bayesian Phylogenetic Inference
Cheng Zhang, Frederick A. Matsen
Bayesian phylogenetic inference is currently done via Markov chain Monte Carlo (MCMC) with simple proposal mechanisms. This hinders exploration efficiency and often requires long r…
Probabilistic DAG Search
Julia Grosse, Cheng Zhang, Philipp Hennig
Exciting contemporary machine learning problems have recently been phrased in the classic formalism of tree search -- most famously, the game of Go. Interestingly, the state-space…
A data-driven and model-based accelerated Hamiltonian Monte Carlo method for Bayesian elliptic inverse problems
Sijing Li, Cheng Zhang, Zhiwen Zhang +1
In this paper, we consider a Bayesian inverse problem modeled by elliptic partial differential equations (PDEs). Specifically, we propose a data-driven and model-based approach to…
Variational Bayesian Supertrees
Michael Karcher, Cheng Zhang, Frederick A Matsen
Given overlapping subsets of a set of taxa (e.g. species), and posterior distributions on phylogenetic tree topologies for each of these taxon sets, how can we infer a posterior di…
Estimating -Rank by Maximizing Information Gain
Tabish Rashid, Cheng Zhang, Kamil Ciosek
Game theory has been increasingly applied in settings where the game is not known outright, but has to be estimated by sampling. For example, meta-games that arise in multi-agent e…
Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows
Cheng Zhang
Variational Bayesian phylogenetic inference (VBPI) provides a promising general variational framework for efficient estimation of phylogenetic posteriors. However, the current diag…
Non-bifurcating phylogenetic tree inference via the adaptive LASSO
Cheng Zhang, Vu Dinh, Frederick A. Matsen
Phylogenetic tree inference using deep DNA sequencing is reshaping our understanding of rapidly evolving systems, such as the within-host battle between viruses and the immune syst…
Generalizing Tree Probability Estimation via Bayesian Networks
Cheng Zhang, Frederick A. Matsen
Probability estimation is one of the fundamental tasks in statistics and machine learning. However, standard methods for probability estimation on discrete objects do not handle ob…
Probabilistic Path Hamiltonian Monte Carlo
Vu Dinh, Arman Bilge, Cheng Zhang +1
Hamiltonian Monte Carlo (HMC) is an efficient and effective means of sampling posterior distributions on Euclidean space, which has been extended to manifolds with boundary. Howeve…
Variational Hamiltonian Monte Carlo via Score Matching
Cheng Zhang, Babak Shahbaba, Hongkai Zhao
Traditionally, the field of computational Bayesian statistics has been divided into two main subfields: variational methods and Markov chain Monte Carlo (MCMC). In recent years, ho…
Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases
Cheng Zhang, Babak Shahbaba, Hongkai Zhao
For big data analysis, high computational cost for Bayesian methods often limits their applications in practice. In recent years, there have been many attempts to improve computati…
Precomputing Strategy for Hamiltonian Monte Carlo Method Based on Regularity in Parameter Space
Cheng Zhang, Babak Shahbaba, Hongkai Zhao
Markov Chain Monte Carlo (MCMC) algorithms play an important role in statistical inference problems dealing with intractable probability distributions. Recently, many MCMC algorith…