Search Results for author: Artur Dubrawski

Found 54 papers, 15 papers with code

HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python

1 code implementation7 Jul 2022 Kin G. Olivares, Federico Garza, David Luo, Cristian Challú, Max Mergenthaler, Artur Dubrawski

Large collections of time series data are commonly organized into cross-sectional structures with different levels of aggregation; examples include product and geographical groupings.

Decision Making Machine Learning +1

Classifying Unstructured Clinical Notes via Automatic Weak Supervision

1 code implementation24 Jun 2022 Chufan Gao, Mononito Goswami, Jieshi Chen, Artur Dubrawski

Healthcare providers usually record detailed notes of the clinical care delivered to each patient for clinical, research, and billing purposes.

Text Classification

The Digital Twin Landscape at the Crossroads of Predictive Maintenance, Machine Learning and Physics Based Modeling

no code implementations21 Jun 2022 Brian Kunzer, Mario Berges, Artur Dubrawski

The application of a digital twin framework is highlighted in the field of predictive maintenance, and its extensions utilizing machine learning and physics based modeling.


Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact

no code implementations18 Jun 2022 Arnab Dey, Mononito Goswami, Joo Heung Yoon, Gilles Clermont, Michael Pinsky, Marilyn Hravnak, Artur Dubrawski

Our weakly supervised models perform competitively with traditional supervised techniques and require less involvement from domain experts, demonstrating their use as efficient and practical alternatives to supervised learning in HC applications of ML.

Weakly Supervised Classification

Doubting AI Predictions: Influence-Driven Second Opinion Recommendation

no code implementations29 Apr 2022 Maria De-Arteaga, Alexandra Chouldechova, Artur Dubrawski

Effective human-AI collaboration requires a system design that provides humans with meaningful ways to make sense of and critically evaluate algorithmic recommendations.

auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data

2 code implementations15 Apr 2022 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.

Machine Learning Time-to-Event Prediction

Constrained Clustering and Multiple Kernel Learning without Pairwise Constraint Relaxation

1 code implementation23 Mar 2022 Benedikt Boecking, Vincent Jeanselme, Artur Dubrawski

However, the common practice of relaxing discrete constraints to a continuous domain to ease optimization when learning kernels or metrics can harm generalization, as information which only encodes linkage is transformed to informing distances.

Generative Modeling Helps Weak Supervision (and Vice Versa)

no code implementations22 Mar 2022 Benedikt Boecking, Nicholas Roberts, Willie Neiswanger, Stefano Ermon, Frederic Sala, Artur Dubrawski

The model outperforms baseline weak supervision label models on a number of multiclass image classification datasets, improves the quality of generated images, and further improves end-model performance through data augmentation with synthetic samples.

Data Augmentation Image Classification

Counterfactual Phenotyping with Censored Time-to-Events

2 code implementations22 Feb 2022 Chirag Nagpal, Mononito Goswami, Keith Dufendach, Artur Dubrawski

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 may be subject to censoring.

N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting

2 code implementations30 Jan 2022 Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski

Recent progress in neural forecasting accelerated improvements in the performance of large-scale forecasting systems.

Time Series Forecasting

Weak Supervision for Affordable Modeling of Electrocardiogram Data

no code implementations9 Jan 2022 Mononito Goswami, Benedikt Boecking, Artur Dubrawski

We explore the use of multiple weak supervision sources to learn diagnostic models of abnormal heartbeats via human designed heuristics, without using ground truth labels on individual data points.

Time Series

Discovery of Crime Event Sequences with Constricted Spatio-Temporal Sequential Patterns

no code implementations3 Dec 2021 Piotr S. Maciąg, Robert Bembenik, Artur Dubrawski

We demonstrate that the set of CSTS patterns is a concise representation of all spatio-temporal sequential patterns that can be discovered in a given dataset.

Provably Robust Model-Centric Explanations for Critical Decision-Making

no code implementations26 Oct 2021 Cecilia G. Morales, Nicholas Gisolfi, Robert Edman, James K. Miller, Artur Dubrawski

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 gleaned from LIME and SHAP, popular data-centric explanation tools in Artificial Intelligence (AI).

Decision Making


no code implementations29 Sep 2021 Mononito Goswami, Chufan Gao, Benedikt Boecking, Saswati Ray, Artur Dubrawski

In domains such as clinical research, where data collection and its careful characterization is particularly expensive and tedious, this reliance on pointillisticaly labeled data is one of the biggest roadblocks to the adoption of modern data-hungry ML algorithms.

Active Learning

Deep Attentive Variational Inference

no code implementations ICLR 2022 Ifigeneia Apostolopoulou, Ian Char, Elan Rosenfeld, Artur Dubrawski

Moreover, the architecture for this class of models favors local interactions among the latent variables between neighboring layers when designing the conditioning factors of the involved distributions.

Variational Inference

Kernel Density Decision Trees

no code implementations29 Sep 2021 Jack Henry Good, Kyle Miller, Artur Dubrawski

FDTs address the sensitivity and tendency to overfitting of decision trees by representing uncertainty through fuzzy partitions.

Density Estimation

End-to-End Weak Supervision

1 code implementation NeurIPS 2021 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 collection of ground truth labels.


Dependency Structure Misspecification in Multi-Source Weak Supervision Models

no code implementations18 Jun 2021 Salva Rühling Cachay, Benedikt Boecking, Artur Dubrawski

Data programming (DP) has proven to be an attractive alternative to costly hand-labeling of data.

DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting

no code implementations7 Jun 2021 Cristian Challu, Kin G. Olivares, Gus Welter, Artur Dubrawski

We validate our proposed method, DMIDAS, on high-frequency healthcare and electricity price data with long forecasting horizons (~1000 timestamps) where we improve the prediction accuracy by 5% over state-of-the-art models, reducing the number of parameters of NBEATS by nearly 70%.

Time Series Forecasting

Leveraging Expert Consistency to Improve Algorithmic Decision Support

no code implementations24 Jan 2021 Maria De-Arteaga, Vincent Jeanselme, Artur Dubrawski, Alexandra Chouldechova

However, there is frequently a gap between decision objectives and what is captured in the observed outcomes used as labels to train ML models.

Machine Learning

Robust Multi-view Representation Learning

no code implementations1 Jan 2021 Sibi Venkatesan, Kyle Miller, Artur Dubrawski

Our synthetic and real-world experiments show promising results for the application of these models to robust representation learning.

Representation Learning Self-Driving Cars

Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling

1 code implementation ICLR 2021 Benedikt Boecking, Willie Neiswanger, Eric Xing, Artur Dubrawski

Our experiments demonstrate that only a small number of feedback iterations are needed to train models that achieve highly competitive test set performance without access to ground truth training labels.

Weakly Supervised Classification

Self-Reflective Variational Autoencoder

no code implementations10 Jul 2020 Ifigeneia Apostolopoulou, Elan Rosenfeld, Artur Dubrawski

The Variational Autoencoder (VAE) is a powerful framework for learning probabilistic latent variable generative models.

Variational Inference

Preference-based Reinforcement Learning with Finite-Time Guarantees

no code implementations NeurIPS 2020 Yichong Xu, Ruosong Wang, Lin F. Yang, Aarti Singh, Artur Dubrawski

If preferences are stochastic, and the preference probability relates to the hidden reward values, we present algorithms for PbRL, both with and without a simulator, that are able to identify the best policy up to accuracy $\varepsilon$ with high probability.


System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis

no code implementations11 May 2020 Kyle Miller, Artur Dubrawski

This paper reviews current literature in the field of predictive maintenance from the system point of view.

Pairwise Feedback for Data Programming

no code implementations16 Dec 2019 Benedikt Boecking, Artur Dubrawski

We propose to improve modeling of latent class variables in the programmatic creation of labeled datasets by incorporating pairwise feedback into the process.

Mutually Regressive Point Processes

1 code implementation NeurIPS 2019 Ifigeneia Apostolopoulou, Scott Linderman, Kyle Miller, Artur Dubrawski

Despite many potential applications, existing point process models are limited in their ability to capture complex patterns of interaction.

Bayesian Inference Point Processes

Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning

no code implementations12 Nov 2019 Chufan Gao, Fabian Falck, Mononito Goswami, Anthony Wertz, Michael R. Pinsky, Artur Dubrawski

By analyzing the clusters of latent embeddings and visualizing them over time, we hypothesize that the clusters correspond to the physiological response patterns that match physicians' intuition.

Machine Learning Survival Prediction +1

Zeroth Order Non-convex optimization with Dueling-Choice Bandits

no code implementations3 Nov 2019 Yichong Xu, Aparna Joshi, Aarti Singh, Artur Dubrawski

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 point with the larger function value.

Active Learning for Graph Neural Networks via Node Feature Propagation

no code implementations16 Oct 2019 Yuexin Wu, Yichong Xu, Aarti Singh, Yiming Yang, Artur Dubrawski

Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data.

Active Learning General Classification +1

Thresholding Bandit Problem with Both Duels and Pulls

no code implementations14 Oct 2019 Yichong Xu, Xi Chen, Aarti Singh, Artur Dubrawski

The Thresholding Bandit Problem (TBP) aims to find the set of arms with mean rewards greater than a given threshold.

Active Learning Graph Neural Networks via Node Feature Propagation

no code implementations25 Sep 2019 Yuexin Wu, Yichong Xu, Aarti Singh, Artur Dubrawski, Yiming Yang

Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data.

Active Learning Node Classification

DASGrad: Double Adaptive Stochastic Gradient

no code implementations25 Sep 2019 Kin Gutierrez, Cristian Challu, Jin Li, Artur Dubrawski

Adaptive moment methods have been remarkably successful for optimization under the presence of high dimensional or sparse gradients, in parallel to this, adaptive sampling probabilities for SGD have allowed optimizers to improve convergence rates by prioritizing examples to learn efficiently.

Transfer Learning

Nonlinear Semi-Parametric Models for Survival Analysis

1 code implementation14 May 2019 Chirag Nagpal, Rohan Sangave, Amit Chahar, Parth Shah, Artur Dubrawski, Bhiksha Raj

Semi-parametric survival analysis methods like the Cox Proportional Hazards (CPH) regression (Cox, 1972) are a popular approach for survival analysis.

Survival Analysis

Double Adaptive Stochastic Gradient Optimization

no code implementations6 Nov 2018 Kin Gutierrez, Jin Li, Cristian Challu, Artur Dubrawski

We observe that the benefits of~\textsc{DASGrad} increase with the model complexity and variability of the gradients, and we explore the resulting utility in extensions of distribution-matching multitask learning.

On the Interaction Effects Between Prediction and Clustering

1 code implementation18 Jul 2018 Matt Barnes, Artur Dubrawski

Machine learning systems increasingly depend on pipelines of multiple algorithms to provide high quality and well structured predictions.

Learning under selective labels in the presence of expert consistency

no code implementations2 Jul 2018 Maria De-Arteaga, Artur Dubrawski, Alexandra Chouldechova

We explore the problem of learning under selective labels in the context of algorithm-assisted decision making.

Data Augmentation Decision Making +1

Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information

no code implementations ICML 2018 Yichong Xu, Hariank Muthakana, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski

Finally, we present experiments that show the efficacy of RR and investigate its robustness to various sources of noise and model-misspecification.

Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information

no code implementations ICML 2018 Yichong Xu, Sivaraman Balakrishnan, Aarti Singh, Artur Dubrawski

In supervised learning, we typically leverage a fully labeled dataset to design methods for function estimation or prediction.

Novel Prediction Techniques Based on Clusterwise Linear Regression

1 code implementation28 Apr 2018 Igor Gitman, Jieshi Chen, Eric Lei, Artur Dubrawski

In this paper we propose two novel approaches on how to solve this problem.

Noise-Tolerant Interactive Learning Using Pairwise Comparisons

no code implementations NeurIPS 2017 Yichong Xu, Hongyang Zhang, Kyle Miller, Aarti Singh, Artur Dubrawski

We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given two instances is more likely to be positive.

Characterization of Hemodynamic Signal by Learning Multi-View Relationships

no code implementations17 Sep 2017 Eric Lei, Kyle Miller, Michael R. Pinsky, Artur Dubrawski

We aim to investigate the usefulness of nonlinear multi-view relations to characterize multi-view data in an explainable manner.

Scaling Active Search using Linear Similarity Functions

1 code implementation30 Apr 2017 Sibi Venkatesan, James K. Miller, Jeff Schneider, Artur Dubrawski

In this paper, we consider the problem of Active Search where we are given a similarity function between data points.

Information Retrieval

Noise-Tolerant Interactive Learning from Pairwise Comparisons

no code implementations19 Apr 2017 Yichong Xu, Hongyang Zhang, Aarti Singh, Kyle Miller, Artur Dubrawski

We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given two instances is more likely to be positive.

Clustering on the Edge: Learning Structure in Graphs

no code implementations5 May 2016 Matt Barnes, Artur Dubrawski

With the recent popularity of graphical clustering methods, there has been an increased focus on the information between samples.

Entity Resolution Semantic Segmentation

Batched Lazy Decision Trees

no code implementations8 Mar 2016 Mathieu Guillame-Bert, Artur Dubrawski

We introduce a batched lazy algorithm for supervised classification using decision trees.

General Classification

Canonical Autocorrelation Analysis

no code implementations19 Nov 2015 Maria De-Arteaga, Artur Dubrawski, Peter Huggins

We present an extension of sparse Canonical Correlation Analysis (CCA) designed for finding multiple-to-multiple linear correlations within a single set of variables.

Anomaly Detection

Lass-0: sparse non-convex regression by local search

no code implementations13 Nov 2015 William Herlands, Maria De-Arteaga, Daniel Neill, Artur Dubrawski

We compute approximate solutions to L0 regularized linear regression using L1 regularization, also known as the Lasso, as an initialization step.

Performance Bounds for Pairwise Entity Resolution

no code implementations10 Sep 2015 Matt Barnes, Kyle Miller, Artur Dubrawski

One significant challenge to scaling entity resolution algorithms to massive datasets is understanding how performance changes after moving beyond the realm of small, manually labeled reference datasets.

Entity Resolution Machine Learning

Real-Time Visual Analysis of Microvascular Blood Flow for Critical Care

no code implementations CVPR 2015 Chao Liu, Hernando Gomez, Srinivasa Narasimhan, Artur Dubrawski, Michael R. Pinsky, Brian Zuckerbraun

Our method is able to extract microcirculatory measurements that are consistent with clinical intuition and it has a potential to become a useful tool in critical care medicine.

Video Stabilization

Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text

no code implementations NeurIPS 2008 Yi Zhang, Artur Dubrawski, Jeff G. Schneider

In an empirical study, we construct 190 different text classification tasks from a real-world benchmark, and the unlabeled documents are a mixture from all these tasks.

General Classification Text Classification

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