259 citations · 463 across the 39 of their papers we have counts for
25 papers · 1 filter
auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data
Chirag Nagpal, Willa Potosnak, Artur Dubrawski
Applications of machine learning in healthcare often require working with time-to-event prediction tasks including prognostication of an adverse event, re-hospitalization or death.…
Constrained Clustering and Multiple Kernel Learning without Pairwise Constraint Relaxation
Benedikt Boecking, Vincent Jeanselme, Artur Dubrawski
Clustering under pairwise constraints is an important knowledge discovery tool that enables the learning of appropriate kernels or distance metrics to improve clustering performanc…
Counterfactual Phenotyping with Censored Time-to-Events
Chirag Nagpal, Mononito Goswami, Keith Dufendach +1
Estimation of treatment efficacy of real-world clinical interventions involves working with continuous outcomes such as time-to-death, re-hospitalization, or a composite event that…
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting
Cristian Challu, Kin G. Olivares, Boris N. Oreshkin +3
Recent progress in neural forecasting accelerated improvements in the performance of large-scale forecasting systems. Yet, long-horizon forecasting remains a very difficult task. T…
Discovery of Crime Event Sequences with Constricted Spatio-Temporal Sequential Patterns
Piotr S. Maciąg, Robert Bembenik, Artur Dubrawski
In this article, we introduce a novel type of spatio-temporal sequential patterns called Constricted Spatio-Temporal Sequential (CSTS) patterns and thoroughly analyze their propert…
Provably Robust Model-Centric Explanations for Critical Decision-Making
Cecilia G. Morales, Nicholas Gisolfi, Robert Edman +2
We recommend using a model-centric, Boolean Satisfiability (SAT) formalism to obtain useful explanations of trained model behavior, different and complementary to what can be glean…
End-to-End Weak Supervision
Salva Rühling Cachay, Benedikt Boecking, Artur Dubrawski
Aggregating multiple sources of weak supervision (WS) can ease the data-labeling bottleneck prevalent in many machine learning applications, by replacing the tedious manual collect…
Dependency Structure Misspecification in Multi-Source Weak Supervision Models
Salva Rühling Cachay, Benedikt Boecking, Artur Dubrawski
Data programming (DP) has proven to be an attractive alternative to costly hand-labeling of data. In DP, users encode domain knowledge into \emph{labeling functions} (LF), heuristi…
DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting
Cristian Challu, Kin G. Olivares, Gus Welter +1
Neural forecasting has shown significant improvements in the accuracy of large-scale systems, yet predicting extremely long horizons remains a challenging task. Two common problems…
Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx
Kin G. Olivares, Cristian Challu, Grzegorz Marcjasz +2
We extend the neural basis expansion analysis (NBEATS) to incorporate exogenous factors. The resulting method, called NBEATSx, improves on a well performing deep learning model, ex…
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling
Benedikt Boecking, Willie Neiswanger, Eric Xing +1
Obtaining large annotated datasets is critical for training successful machine learning models and it is often a bottleneck in practice. Weak supervision offers a promising alterna…
Leveraging Expert Consistency to Improve Algorithmic Decision Support
Maria De-Arteaga, Vincent Jeanselme, Artur Dubrawski +1
Machine learning (ML) is increasingly being used to support high-stakes decisions. However, there is frequently a construct gap: a gap between the construct of interest to the deci…
Self-Reflective Variational Autoencoder
Ifigeneia Apostolopoulou, Elan Rosenfeld, Artur Dubrawski
The Variational Autoencoder (VAE) is a powerful framework for learning probabilistic latent variable generative models. However, typical assumptions on the approximate posterior di…
Preference-based Reinforcement Learning with Finite-Time Guarantees
Yichong Xu, Ruosong Wang, Lin F. Yang +2
Preference-based Reinforcement Learning (PbRL) replaces reward values in traditional reinforcement learning by preferences to better elicit human opinion on the target objective, e…
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks
Chirag Nagpal, Xinyu Rachel Li, Artur Dubrawski
We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner. Our approach does not require making s…
Pairwise Feedback for Data Programming
Benedikt Boecking, Artur Dubrawski
The scalability of the labeling process and the attainable quality of labels have become limiting factors for many applications of machine learning. The programmatic creation of la…
Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning
Chufan Gao, Fabian Falck, Mononito Goswami +3
Monitoring physiological responses to hemodynamic stress can help in determining appropriate treatment and ensuring good patient outcomes. Physicians' intuition suggests that the h…
Zeroth Order Non-convex optimization with Dueling-Choice Bandits
Yichong Xu, Aparna Joshi, Aarti Singh +1
We consider a novel setting of zeroth order non-convex optimization, where in addition to querying the function value at a given point, we can also duel two points and get the poin…
Active Learning for Graph Neural Networks via Node Feature Propagation
Yuexin Wu, Yichong Xu, Aarti Singh +2
Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structu…
Thresholding Bandit Problem with Both Duels and Pulls
Yichong Xu, Xi Chen, Aarti Singh +1
The Thresholding Bandit Problem (TBP) aims to find the set of arms with mean rewards greater than a given threshold. We consider a new setting of TBP, where in addition to pulling…
Nonlinear Semi-Parametric Models for Survival Analysis
Chirag Nagpal, Rohan Sangave, Amit Chahar +3
Semi-parametric survival analysis methods like the Cox Proportional Hazards (CPH) regression (Cox, 1972) are a popular approach for survival analysis. These methods involve fitting…
Learning under selective labels in the presence of expert consistency
Maria De-Arteaga, Artur Dubrawski, Alexandra Chouldechova
We explore the problem of learning under selective labels in the context of algorithm-assisted decision making. Selective labels is a pervasive selection bias problem that arises w…
Novel Prediction Techniques Based on Clusterwise Linear Regression
Igor Gitman, Jieshi Chen, Eric Lei +1
In this paper we explore different regression models based on Clusterwise Linear Regression (CLR). CLR aims to find the partition of the data into clusters, such that linear re…
Suppressing Background Radiation Using Poisson Principal Component Analysis
P. Tandon, P. Huggins, A. Dubrawski +2
Performance of nuclear threat detection systems based on gamma-ray spectrometry often strongly depends on the ability to identify the part of measured signal that can be attributed…