Search Results for author:
Found 49 papers, 28 papers with code
Adaptive Ensemble Learning with Confidence Bounds
Extracting actionable intelligence from distributed, heterogeneous, correlated and high-dimensional data sources requires run-time processing and learning both locally and globally.
Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process Experts
We develop a personalized real time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs.
Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes
Objective: In this paper, we develop a personalized real-time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs; the proposed risk scoring system ensures timely intensive care unit (ICU) admissions for clinically deteriorating patients.
Personalized Donor-Recipient Matching for Organ Transplantation
Organ transplants can improve the life expectancy and quality of life for the recipient but carries the risk of serious post-operative complications, such as septic shock and organ rejection.
A Semi-Markov Switching Linear Gaussian Model for Censored Physiological Data
Critically ill patients in regular wards are vulnerable to unanticipated clinical dete- rioration which requires timely transfer to the intensive care unit (ICU).
Individualized Risk Prognosis for Critical Care Patients: A Multi-task Gaussian Process Model
We report the development and validation of a data-driven real-time risk score that provides timely assessments for the clinical acuity of ward patients based on their temporal lab tests and vital signs, which allows for timely intensive care unit (ICU) admissions.
ToPs: Ensemble Learning with Trees of Predictors
Our approach constructs a tree of subsets of the feature space and associates a predictor (predictive model) - determined by training one of a given family of base learners on an endogenously determined training set - to each node of the tree; we call the resulting object a tree of predictors.
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks
Existing methods address this estimation problem by interpolating within data streams or imputing across data streams (both of which ignore important information) or ignoring the temporal aspect of the data and imposing strong assumptions about the nature of the data-generating process and/or the pattern of missing data (both of which are especially problematic for medical data).
GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets
Estimating individualized treatment effects (ITE) is a challenging task due to the need for an individual's potential outcomes to be learned from biased data and without having access to the counterfactuals.
Ranked #2 on
Causal Inference
on Jobs
Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks
At runtime, the operator prescribes a performance level or a cost constraint, and Deep Sensing determines what measurements to take and what to infer from those measurements, and then issues predictions.
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks
Training complex machine learning models for prediction often requires a large amount of data that is not always readily available.
GAIN: Missing Data Imputation using Generative Adversarial Nets
Accordingly, we call our method Generative Adversarial Imputation Nets (GAIN).
Measuring the quality of Synthetic data for use in competitions
Machine learning has the potential to assist many communities in using the large datasets that are becoming more and more available.
Feature Selection for Survival Analysis with Competing Risks using Deep Learning
Deep learning models for survival analysis have gained significant attention in the literature, but they suffer from severe performance deficits when the dataset contains many irrelevant features.
MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks
Accurate prediction of disease trajectories is critical for early identification and timely treatment of patients at risk.
PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees
Machine learning has the potential to assist many communities in using the large datasets that are becoming more and more available.
Ranked #2 on
Synthetic Data Generation
on UCI Epileptic Seizure Recognition
(using extra training data)
KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks
We demonstrate the capability of our model to perform feature selection, showing that it performs as well as the originally proposed knockoff generation model in the Gaussian setting and that it outperforms the original model in non-Gaussian settings, including on a real-world dataset.
INVASE: Instance-wise Variable Selection using Neural Networks
The advent of big data brings with it data with more and more dimensions and thus a growing need to be able to efficiently select which features to use for a variety of problems.
ASAC: Active Sensing using Actor-Critic models
The predictor network uses the observations selected by the selector network to predict a label, providing feedback to the selector network (well-selected variables should be predictive of the label).
Data Valuation using Reinforcement Learning
To adaptively learn data values jointly with the target task predictor model, we propose a meta learning framework which we name Data Valuation using Reinforcement Learning (DVRL).
LIMIS: Locally Interpretable Modeling using Instance-wise Subsampling
Understanding black-box machine learning models is crucial for their widespread adoption.
Time-series Generative Adversarial Networks
A good generative model for time-series data should preservetemporal dynamics, in the sense that new sequences respect the original relationships between variablesacross time.
Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate
The second benefit is that, through analysis that we provide inthe paper, we can derive tighter differential privacy guarantees when several queriesare made to this mechanism.
Time-series Generative Adversarial Networks
A good generative model for time-series data should preserve temporal dynamics, in the sense that new sequences respect the original relationships between variables across time.
Hide-and-Seek Privacy Challenge
The clinical time-series setting poses a unique combination of challenges to data modeling and sharing.
Interpretable Sequence Learning for COVID-19 Forecasting
We propose a novel approach that integrates machine learning into compartmental disease modeling to predict the progression of COVID-19.
Learning and Evaluating Representations for Deep One-class Classification
We first learn self-supervised representations from one-class data, and then build one-class classifiers on learned representations.
Ranked #7 on
Anomaly Detection
on One-class CIFAR-100
VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain
We also introduce a novel tabular data augmentation method for self- and semi-supervised learning frameworks.
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data.
Ranked #55 on
Anomaly Detection
on MVTec AD
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection
We demonstrate our method on various unsupervised AD tasks with image and tabular data.
Controlling Neural Networks with Rule Representations
The key aspect of DeepCTRL is that it does not require retraining to adapt the rule strength -- at inference, the user can adjust it based on the desired operation point on accuracy vs. rule verification ratio.
6MapNet: Representing soccer players from tracking data by a triplet network
Although the values of individual soccer players have become astronomical, subjective judgments still play a big part in the player analysis.
Invariant Learning with Partial Group Labels
Such a requirement is impractical in situations where the data labelling efforts for minority or rare groups is significantly laborious or where the individuals comprising the dataset choose to conceal sensitive information.
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types
We define a distance function between images, each of which is represented as a bag of embeddings, by the Euclidean distance between weighted averaged embeddings.
Towards Group Robustness in the presence of Partial Group Labels
Such a requirement is impractical in situations where the data labeling efforts for minority or rare groups are significantly laborious or where the individuals comprising the dataset choose to conceal sensitive information.
Data-Efficient and Interpretable Tabular Anomaly Detection
In addition, the proposed framework, DIAD, can incorporate a small amount of labeled data to further boost anomaly detection performances in semi-supervised settings.
Interpretable Mixture of Experts
In addition to constituting a standalone inherently-interpretable architecture, IME has the premise of being integrated with existing DNNs to offer interpretability to a subset of samples while maintaining the accuracy of the DNNs.
Invariant Structure Learning for Better Generalization and Causal Explainability
ISL splits the data into different environments, and learns a structure that is invariant to the target across different environments by imposing a consistency constraint.
SoccerCPD: Formation and Role Change-Point Detection in Soccer Matches Using Spatiotemporal Tracking Data
In fluid team sports such as soccer and basketball, analyzing team formation is one of the most intuitive ways to understand tactics from domain participants' point of view.
Provable Membership Inference Privacy
However, DP's strong theoretical guarantees often come at the cost of a large drop in its utility for machine learning, and DP guarantees themselves can be difficult to interpret.
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch
Semi-supervised anomaly detection is a common problem, as often the datasets containing anomalies are partially labeled.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
Rediscovery of CNN's Versatility for Text-based Encoding of Raw Electronic Health Records
Making the most use of abundant information in electronic health records (EHR) is rapidly becoming an important topic in the medical domain.
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction
In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain while increasing accuracy and coverage.
Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM
As artificial intelligence spreads out to numerous fields, the application of AI to sports analytics is also in the spotlight.
PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series
In this paper, we present PAITS (Pretraining and Augmentation for Irregularly-sampled Time Series), a framework for identifying suitable pretraining strategies for sparse and irregularly sampled time series datasets.
Search-Adaptor: Embedding Customization for Information Retrieval
Embeddings extracted by pre-trained Large Language Models (LLMs) have significant potential to improve information retrieval and search.
Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs
Large language models (LLMs) have recently shown great advances in a variety of tasks, including natural language understanding and generation.
Clairvoyance: A Pipeline Toolkit for Medical Time Series
Despite exponential growth in electronic patient data, there is a remarkable gap between the potential and realized utilization of ML for clinical research and decision support.
Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
The proposed FeatLLM framework only uses this simple predictive model with the discovered features at inference time.
