Latest

• Towards Verifiable Text Generation with Symbolic References
• Learning to Decode Collaboratively with Multiple Language Models
• Prediction-powered Generalization of Causal Inferences
• A Data-Centric Approach to Generate Faithful and High Quality Patient Summaries with Large Language Models
• Benchmarking observational studies with experimental data under right-censoring
• Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing
• Effective Human-AI Teams via Learned Natural Language Rules and Onboarding
• Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes
• Large-Scale Study of Temporal Shift in Health Insurance Claims
• A Deep Dive into Single-Cell RNA Sequencing Foundation Models
• Conformalized Unconditional Quantile Regression
• Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions
• TabLLM: Few-shot Classification of Tabular Data with Large Language Models
• Who Should Predict? Exact Algorithms For Learning to Defer to Humans
• Training Subset Selection for Weak Supervision
• Evaluating Robustness to Dataset Shift via Parametric Robustness Sets
• Falsification before Extrapolation in Causal Effect Estimation
• Sample Efficient Learning of Predictors that Complement Humans
• Co-training Improves Prompt-based Learning for Large Language Models
• Bias-robust Integration of Observational and Experimental Estimators
• Clustering Interval-Censored Time-Series for Disease Phenotyping
• ETAB: A Benchmark Suite for Visual Representation Learning in Echocardiography
• Large Language Models are Few-Shot Clinical Information Extractors
• Leveraging Time Irreversibility with Order-Contrastive Pre-training
• Single cell characterization of myeloma and its precursor conditions reveals transcriptional signatures of early tumorigenesis
• Teaching Humans When To Defer to a Classifier via Exemplars
• The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It
• Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models
• Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance
• MedKnowts: Unified Documentation and Information Retrieval for Electronic Health Records
• Graph cuts always find a global optimum for Potts models (with a catch)
• Neural Pharmacodynamic State Space Modeling
• Regularizing towards Causal Invariance: Linear Models with Proxies
• Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative
• Automated NLP Extraction of Clinical Rationale for Treatment Discontinuation in Breast Cancer
• Trajectory Inspection: A Method for Iterative Clinician-Driven Design of Reinforcement Learning Studies
• Beyond perturbation stability: LP recovery guarantees for MAP inference on noisy stable instances
• PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming
• Deep Contextual Clinical Prediction with Reverse Distillation
• Directing Human Attention in Event Localization for Clinical Timeline Creation
• Pulse of the Pandemic: Iterative Topic Filtering for Clinical Information Extraction from Social Media
• A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection
• Characterization of Overlap in Observational Studies
• Consistent Estimators for Learning to Defer to an Expert
• Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
• Estimation of Bounds on Potential Outcomes For Decision Making
• Fast, Structured Clinical Documentation via Contextual Autocomplete
• Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects
• Predicting human health from biofluid-based metabolomics using machine learning
• Predicting Remission Among Patients With Rheumatoid Arthritis Starting Tocilizumab Monotherapy: Model Derivation and Remission Score Development
• Robust Benchmarking for Machine Learning of Clinical Entity Extraction
• Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph
• Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes
• Derivation and validation of a machine learning record linkage algorithm between emergency medical services and the emergency department
• Block Stability for MAP Inference
• Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
• Overcomplete Independent Component Analysis via SDP
• Support and Invertibility in Domain-Invariant Representations
• Train and Test Tightness of LP Relaxations in Structured Prediction
• Cell-specific prediction and application of drug-induced gene expression profiles
• Evaluating Reinforcement Learning Algorithms in Observational Health Settings
• Learning Topic Models - Provably and Efficiently
• Learning Weighted Representations for Generalization Across Designs
• Max-margin learning with the Bayes Factor
• Optimality of Approximate Inference Algorithms on Stable Instances
• Recurrent Neural Networks for Multivariate Time Series with Missing Values
• Semi-Amortized Variational Autoencoders
• Why Is My Classifier Discriminatory?
• Causal Effect Inference with Deep Latent-Variable Models
• Contextual Autocomplete: A Novel User Interface Using Machine Learning to Improve Ontology Usage and Structured Data Capture for Presenting Problems in the Emergency Department
• Creating an Automated Trigger for Sepsis Clinical Decision Support at Emergency Department Triage using Machine Learning
• Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning
• Early Identification of Patients with Acute Decompensated Heart Failure
• Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network
• Estimating individual treatment effect: generalization bounds and algorithms
• Learning a Health Knowledge Graph from Electronic Medical Records
• Objective Assessment of Depressive Symptoms with Machine Learning and Wearable Sensors Data
• Simultaneous Learning of Trees and Representations for Extreme Classification and Density Estimation
• Structured Inference Networks for Nonlinear State Space Models
• Using Machine Learning to Recommend Oncology Clinical Trials
• Character-Aware Neural Language Models
• Clinical Tagging with Joint Probabilistic Models
• Comparison of approaches for heart failure case identification from electronic health record data
• Electronic Medical Record Phenotyping using the Anchor & Learn Framework
• Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization
• Learning Low-Dimensional Representations of Medical Concepts
• Learning Representations for Counterfactual Inference
• Multi-task Prediction of Disease Onsets from Longitudinal Laboratory Tests
• Population-Level Prediction of Type 2 Diabetes using Claims Data and Analysis of Risk Factors
• Tightness of LP Relaxations for Almost Balanced Models
• Train and Test Tightness of LP Relaxations in Structured Prediction
• A Fast Variational Approach for Learning Markov Random Field Language Models
• Anchored Discrete Factor Analysis
• Barrier Frank-Wolfe for Marginal Inference
• Deep Kalman Filters
• How Hard is Inference for Structured Prediction?
• Temporal Convolutional Neural Networks for Diagnosis from Lab Tests
• Visual Exploration of Temporal Data in Electronic Medical Records
• Instance Segmentation of Indoor Scenes using a Coverage Loss
• Lifted Tree-Reweighted Variational Inference
• Understanding the Bethe Approximation: When and How can it go Wrong?
• Unsupervised Learning of Disease Progression Models
• Using Anchors to Estimate Clinical State without Labeled Data
• A Practical Algorithm for Topic Modeling with Provable Guarantees
• Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests
• Predicting Chief Complaints at Triage Time in the Emergency Department
• SparsityBoost: A New Scoring Function for Learning Bayesian Network Structure
• Unsupervised Learning of Noisy-Or Bayesian Networks
• A Comparison of Dimensionality Reduction Techniques for Unstructured Clinical Text
• Efficiently Searching for Frustrated Cycles in MAP Inference
• Introduction to Dual Decomposition for Inference
• Probabilistic models for personalizing web search
• Complexity of Inference in Latent Dirichlet Allocation
• Personalizing web search results by reading level
• Dual Decomposition for Parsing with Non-Projective Head Automata
• Learning Bayesian Network Structure using LP Relaxations
• Learning Efficiently with Approximate Inference via Dual Losses
• More data means less inference: A pseudo-max approach to structured learning
• On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing
• PhD Thesis: Approximate Inference in Graphical Models using LP Relaxations
• Clusters and Coarse Partitions in LP Relaxations
• Scaling All-Pairs Overlay Routing
• Tree Block Coordinate Descent for MAP in Graphical Models
• New Outer Bounds on the Marginal Polytope
• Tightening LP Relaxations for MAP using Message-Passing
• Master's Thesis: Cutting Plane Algorithms for Variational Inference in Graphical Models
• Probabilistic Modeling of Systematic Errors in Two-Hybrid Experiments
• Approximate Inference for Infinite Contingent Bayesian Networks
• BLOG: probabilistic models with unknown objects