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Found 28 papers, 14 papers with code
Nonnegative spatial factorization
Gaussian processes are widely used for the analysis of spatial data due to their nonparametric flexibility and ability to quantify uncertainty, and recently developed scalable approximations have facilitated application to massive datasets.
Large Linear Multi-output Gaussian Process Learning
Gaussian processes (GPs), or distributions over arbitrary functions in a continuous domain, can be generalized to the multi-output case: a linear model of coregionalization (LMC) is one approach.
Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction
In the scenario of real-time monitoring of hospital patients, high-quality inference of patients' health status using all information available from clinical covariates and lab tests is essential to enable successful medical interventions and improve patient outcomes.
Latent variable modeling with random features
By approximating a nonlinear relationship between the latent space and the observations with a function that is linear with respect to random features, we induce closed-form gradients of the posterior distribution with respect to the latent variable.
Bayesian group latent factor analysis with structured sparsity
Latent factor models are the canonical statistical tool for exploratory analyses of low-dimensional linear structure for an observation matrix with p features across n samples.
Active multi-fidelity Bayesian online changepoint detection
We propose a multi-fidelity approach that makes cost-sensitive decisions about which data fidelity to collect based on maximizing information gain with respect to changepoints.
Sequential Gaussian Processes for Online Learning of Nonstationary Functions
Many machine learning problems can be framed in the context of estimating functions, and often these are time-dependent functions that are estimated in real-time as observations arrive.
Nonparametric Reduced-Rank Regression for Multi-SNP, Multi-Trait Association Mapping
Genome-wide association studies have proven to be essential for understanding the genetic basis of disease.
Nonparametric Deconvolution Models
This uniquely allows NDMs both to deconvolve each observation into its constituent factors, and also to describe how the factor distributions specific to each observation vary across observations and deviate from the corresponding global factors.
Variance Minimization in the Wasserstein Space for Invariant Causal Prediction
Selecting powerful predictors for an outcome is a cornerstone task for machine learning.
Bayesian Non-linear Latent Variable Modeling via Random Fourier Features
The Gaussian process latent variable model (GPLVM) is a popular probabilistic method used for nonlinear dimension reduction, matrix factorization, and state-space modeling.
Nonparametric Bayesian multi-armed bandits for single cell experiment design
The problem of maximizing cell type discovery under budget constraints is a fundamental challenge for the collection and analysis of single-cell RNA-sequencing (scRNA-seq) data.
Applications
Kernel Density Bayesian Inverse Reinforcement Learning
Inverse reinforcement learning~(IRL) is a powerful framework to infer an agent's reward function by observing its behavior, but IRL algorithms that learn point estimates of the reward function can be misleading because there may be several functions that describe an agent's behavior equally well.
PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits
We address the problem of regret minimization in logistic contextual bandits, where a learner decides among sequential actions or arms given their respective contexts to maximize binary rewards.
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions.
A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units
The management of invasive mechanical ventilation, and the regulation of sedation and analgesia during ventilation, constitutes a major part of the care of patients admitted to intensive care units.
Unsupervised Domain Adaptation Using Approximate Label Matching
Domain adaptation addresses the problem created when training data is generated by a so-called source distribution, but test data is generated by a significantly different target distribution.
Coupled Compound Poisson Factorization
We present a general framework, the coupled compound Poisson factorization (CCPF), to capture the missing-data mechanism in extremely sparse data sets by coupling a hierarchical Poisson factorization with an arbitrary data-generating model.
Dynamic Collaborative Filtering with Compound Poisson Factorization
Model-based collaborative filtering analyzes user-item interactions to infer latent factors that represent user preferences and item characteristics in order to predict future interactions.
Hierarchical Compound Poisson Factorization
Here, we introduce hierarchical compound Poisson factorization (HCPF) that has the favorable Gamma-Poisson structure and scalability of HPF to high-dimensional extremely sparse matrices.
Fast moment estimation for generalized latent Dirichlet models
We illustrate the utility of our approach on simulated data, comparing results from MELD to alternative methods, and we show the promise of our approach through the application of MELD to several data sets.
Adaptive Randomized Dimension Reduction on Massive Data
In this paper we develop an approach for dimension reduction that exploits the assumption of low rank structure in high dimensional data to gain both computational and statistical advantages.
Differential gene co-expression networks via Bayesian biclustering models
Further, we develop a method to recover gene co-expression networks from the estimated sparse biclustering matrices.
An Optimal Policy for Patient Laboratory Tests in Intensive Care Units
There exists an inherent trade-off in the selection and timing of lab tests between considerations of the expected utility in clinical decision-making of a given test at a specific time, and the associated cost or risk it poses to the patient.
A latent factor model with a mixture of sparse and dense factors to model gene expression data with confounding effects
To address this problem, we developed a Bayesian sparse latent factor model that uses a three parameter beta prior to flexibly model shrinkage in the loading matrix.
Applications Genomics
Defining Admissible Rewards for High Confidence Policy Evaluation
A key impediment to reinforcement learning (RL) in real applications with limited, batch data is defining a reward function that reflects what we implicitly know about reasonable behaviour for a task and allows for robust off-policy evaluation.
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes
However, capturing the short-term effects of drugs and therapeutic interventions on patient physiological state remains challenging.
Adaptive Interventions with User-Defined Goals for Health Behavior Change
We prove that our modification incurs only a constant penalty on the cumulative regret while preserving the sample complexity benefits of data sharing.
