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Found 21 papers, 4 papers with code
Neural Markov Prolog
The recent rapid advance of AI has been driven largely by innovations in neural network architectures.
Differentially Private Multi-Site Treatment Effect Estimation
In this work, we take a fresh look at federated learning with a focus on causal inference; specifically, we look at estimating the average treatment effect (ATE), an important task in causal inference for healthcare applications, and provide a federated analytics approach to enable ATE estimation across multiple sites along with differential privacy (DP) guarantees at each site.
On Neural Networks as Infinite Tree-Structured Probabilistic Graphical Models
Deep neural networks (DNNs) lack the precise semantics and definitive probabilistic interpretation of probabilistic graphical models (PGMs).
Variable Importance Matching for Causal Inference
Our goal is to produce methods for observational causal inference that are auditable, easy to troubleshoot, accurate for treatment effect estimation, and scalable to high-dimensional data.
Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach
Predicting and discovering drug-drug interactions (DDIs) using machine learning has been studied extensively.
High-Throughput Approach to Modeling Healthcare Costs Using Electronic Healthcare Records
Accurate estimation of healthcare costs is crucial for healthcare systems to plan and effectively negotiate with insurance companies regarding the coverage of patient-care costs.
Stochastic Learning for Sparse Discrete Markov Random Fields with Controlled Gradient Approximation Error
We study the $L_1$-regularized maximum likelihood estimator/estimation (MLE) problem for discrete Markov random fields (MRFs), where efficient and scalable learning requires both sparse regularization and approximate inference.
Temporal Poisson Square Root Graphical Models
We propose temporal Poisson square root graphical models (TPSQRs), a generalization of Poisson square root graphical models (PSQRs) specifically designed for modeling longitudinal event data.
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods
We study the problem of learning Granger causality between event types from asynchronous, interdependent, multi-type event sequences.
AutoBlock: A Hands-off Blocking Framework for Entity Matching
Entity matching seeks to identify data records over one or multiple data sources that refer to the same real-world entity.
Beyond Textual Data: Predicting Drug-Drug Interactions from Molecular Structure Images using Siamese Neural Networks
Predicting and discovering drug-drug interactions (DDIs) is an important problem and has been studied extensively both from medical and machine learning point of view.
High-Throughput Machine Learning from Electronic Health Records
For the first time, we can get a much more complete picture of how well risks for thousands of different diagnosis codes can be predicted.
A Simple Text Mining Approach for Ranking Pairwise Associations in Biomedical Applications
We present a simple text mining method that is easy to implement, requires minimal data collection and preparation, and is easy to use for proposing ranked associations between a list of target terms and a key phrase.
Machine Learning to Predict Developmental Neurotoxicity with High-throughput Data from 2D Bio-engineered Tissues
We thus compare the accuracy of predictive models trained on data from a 2D tissue model with those trained on data from a 3D tissue model, and find the 2D model to be substantially more accurate.
Privacy-Preserving Collaborative Prediction using Random Forests
We study the problem of privacy-preserving machine learning (PPML) for ensemble methods, focusing our effort on random forests.
An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge
Hexoses are simple sugars that play a key role in many cellular pathways, and in the regulation of development and disease mechanisms.
A Screening Rule for l1-Regularized Ising Model Estimation
We discover a screening rule for l1-regularized Ising model estimation.
An Efficient Pseudo-likelihood Method for Sparse Binary Pairwise Markov Network Estimation
In this way, many insights and optimization procedures for sparse logistic regression can be applied to the learning of discrete Markov networks.
Computational Drug Repositioning Using Continuous Self-controlled Case Series
Computational Drug Repositioning (CDR) is the task of discovering potential new indications for existing drugs by mining large-scale heterogeneous drug-related data sources.
Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models
In large-scale applications of undirected graphical models, such as social networks and biological networks, similar patterns occur frequently and give rise to similar parameters.
Multiplicative Forests for Continuous-Time Processes
Learning temporal dependencies between variables over continuous time is an important and challenging task.
