research
∙
06/15/2023
AQuA: A Benchmarking Tool for Label Quality Assessment
Machine learning (ML) models are only as good as the data they are train...
share
research
∙
05/11/2023
HINT: Hierarchical Mixture Networks For Coherent Probabilistic Forecasting
We present the Hierarchical Mixture Networks (HINT), a model family for ...
research
∙
02/24/2023
Recovering Sparse and Interpretable Subgroups with Heterogeneous Treatment Effects with Censored Time-to-Event Outcomes
Studies involving both randomized experiments as well as observational d...
research
∙
01/18/2023
Reslicing Ultrasound Images for Data Augmentation and Vessel Reconstruction
Robot-guided catheter insertion has the potential to deliver urgent medi...
research
∙
07/07/2022
HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python
Large collections of time series data are commonly organized into cross-...
research
∙
06/24/2022
Classifying Unstructured Clinical Notes via Automatic Weak Supervision
Healthcare providers usually record detailed notes of the clinical care ...
research
∙
06/21/2022
The Digital Twin Landscape at the Crossroads of Predictive Maintenance, Machine Learning and Physics Based Modeling
The concept of a digital twin has exploded in popularity over the past d...
research
∙
06/18/2022
Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact
A significant proportion of clinical physiologic monitoring alarms are f...
research
∙
04/29/2022
Doubting AI Predictions: Influence-Driven Second Opinion Recommendation
Effective human-AI collaboration requires a system design that provides ...
research
∙
04/15/2022
auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data
Applications of machine learning in healthcare often require working wit...
research
∙
03/23/2022
Constrained Clustering and Multiple Kernel Learning without Pairwise Constraint Relaxation
Clustering under pairwise constraints is an important knowledge discover...
research
∙
02/22/2022
Counterfactual Phenotyping with Censored Time-to-Events
Estimation of treatment efficacy of real-world clinical interventions in...
research
∙
01/30/2022
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting
Recent progress in neural forecasting accelerated improvements in the pe...
research
∙
01/09/2022
Weak Supervision for Affordable Modeling of Electrocardiogram Data
Analysing electrocardiograms (ECGs) is an inexpensive and non-invasive, ...
research
∙
12/03/2021
Discovery of Crime Event Sequences with Constricted Spatio-Temporal Sequential Patterns
In this article, we introduce a novel type of spatio-temporal sequential...
research
∙
10/26/2021
Provably Robust Model-Centric Explanations for Critical Decision-Making
We recommend using a model-centric, Boolean Satisfiability (SAT) formali...
research
∙
07/05/2021
End-to-End Weak Supervision
Aggregating multiple sources of weak supervision (WS) can ease the data-...
research
∙
06/18/2021
Dependency Structure Misspecification in Multi-Source Weak Supervision Models
Data programming (DP) has proven to be an attractive alternative to cost...
research
∙
06/07/2021
DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting
Neural forecasting has shown significant improvements in the accuracy of...
research
∙
04/12/2021
Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx
We extend the neural basis expansion analysis (NBEATS) to incorporate ex...
research
∙
01/24/2021
Leveraging Expert Consistency to Improve Algorithmic Decision Support
Due to their promise of superior predictive power relative to human asse...
research
∙
07/10/2020
Self-Reflective Variational Autoencoder
The Variational Autoencoder (VAE) is a powerful framework for learning p...
research
∙
06/16/2020
Preference-based Reinforcement Learning with Finite-Time Guarantees
Preference-based Reinforcement Learning (PbRL) replaces reward values in...
research
∙
05/11/2020
System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis
This paper reviews current literature in the field of predictive mainten...
research
∙
03/02/2020
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks
We describe a new approach to estimating relative risks in time-to-event...
research
∙
12/16/2019
Pairwise Feedback for Data Programming
The scalability of the labeling process and the attainable quality of la...
research
∙
11/12/2019
Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning
Monitoring physiological responses to hemodynamic stress can help in det...
research
∙
11/03/2019
Zeroth Order Non-convex optimization with Dueling-Choice Bandits
We consider a novel setting of zeroth order non-convex optimization, whe...
research
∙
10/16/2019
Active Learning for Graph Neural Networks via Node Feature Propagation
Graph Neural Networks (GNNs) for prediction tasks like node classificati...
research
∙
10/14/2019
Thresholding Bandit Problem with Both Duels and Pulls
The Thresholding Bandit Problem (TBP) aims to find the set of arms with ...
research
∙
05/14/2019
Nonlinear Semi-Parametric Models for Survival Analysis
Semi-parametric survival analysis methods like the Cox Proportional Haza...
research
∙
11/06/2018
Double Adaptive Stochastic Gradient Optimization
Adaptive moment methods have been remarkably successful in deep learning...
research
∙
07/18/2018
Dependency Leakage: Analysis and Scalable Estimators
In this paper, we prove the first theoretical results on dependency leak...
research
∙
07/02/2018
Learning under selective labels in the presence of expert consistency
We explore the problem of learning under selective labels in the context...
research
∙
06/08/2018
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
In supervised learning, we leverage a labeled dataset to design methods ...
research
∙
04/28/2018
Novel Prediction Techniques Based on Clusterwise Linear Regression
In this paper we explore different regression models based on Clusterwis...
research
∙
11/17/2017
Discovery of Complex Anomalous Patterns of Sexual Violence in El Salvador
When sexual violence is a product of organized crime or social imaginary...
research
∙
09/17/2017
Learning Mixtures of Multi-Output Regression Models by Correlation Clustering for Multi-View Data
In many datasets, different parts of the data may have their own pattern...
research
∙
04/30/2017
Scaling Active Search using Linear Similarity Functions
Active Search has become an increasingly useful tool in information retr...
research
∙
04/19/2017
Noise-Tolerant Interactive Learning from Pairwise Comparisons
We study the problem of interactively learning a binary classifier using...
research
∙
05/05/2016
Clustering on the Edge: Learning Structure in Graphs
With the recent popularity of graphical clustering methods, there has be...
research
∙
11/19/2015
Canonical Autocorrelation Analysis
We present an extension of sparse Canonical Correlation Analysis (CCA) d...
research
∙
11/13/2015
Lass-0: sparse non-convex regression by local search
We compute approximate solutions to L0 regularized linear regression usi...
research
∙
09/10/2015
