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Found 189 papers, 89 papers with code
Do Diffusion Models Suffer Error Propagation? Theoretical Analysis and Consistency Regularization
While diffusion models have achieved promising performances in data synthesis, they might suffer error propagation because of their cascade structure, where the distributional mismatch spreads and magnifies through the chain of denoising modules.
Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models
Drawing upon the reasoning capabilities of LLMs, TEMED-LLM goes beyond traditional extraction techniques, accurately inferring tabular features, even when their names are not explicitly mentioned in the text.
U-PASS: an Uncertainty-guided deep learning Pipeline for Automated Sleep Staging
As machine learning becomes increasingly prevalent in critical fields such as healthcare, ensuring the safety and reliability of machine learning systems becomes paramount.
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time
Machine learning (ML) holds great potential for accurately forecasting treatment outcomes over time, which could ultimately enable the adoption of more individualized treatment strategies in many practical applications.
Learning Representations without Compositional Assumptions
This paper addresses unsupervised representation learning on tabular data containing multiple views generated by distinct sources of measurement.
Synthetic data, real errors: how (not) to publish and use synthetic data
Generating synthetic data through generative models is gaining interest in the ML community and beyond, promising a future where datasets can be tailored to individual needs.
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance
Through this rigorous formalism, we derive (1) two metrics to measure the robustness of any interpretability method with respect to the model symmetry group; (2) theoretical robustness guarantees for some popular interpretability methods and (3) a systematic approach to increase the invariance of any interpretability method with respect to a symmetry group.
Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data
Generating synthetic data through generative models is gaining interest in the ML community and beyond.
Machine Learning with Requirements: a Manifesto
In the recent years, machine learning has made great advancements that have been at the root of many breakthroughs in different application domains.
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization
In this work, we introduce Tabular Neural Gradient Orthogonalization and Specialization (TANGOS), a novel framework for regularization in the tabular setting built on latent unit attributions.
Causal Deep Learning
To address this challenge and make progress in solving real-world problems, we propose a new way of thinking about causality - we call this causal deep learning.
Safe AI for health and beyond -- Monitoring to transform a health service
Machine learning techniques are effective for building predictive models because they identify patterns in large datasets.
T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression
Clustering time-series data in healthcare is crucial for clinical phenotyping to understand patients' disease progression patterns and to design treatment guidelines tailored to homogeneous patient subgroups.
Neural Laplace Control for Continuous-time Delayed Systems
Many real-world offline reinforcement learning (RL) problems involve continuous-time environments with delays.
Membership Inference Attacks against Synthetic Data through Overfitting Detection
In this work we argue for a realistic MIA setting that assumes the attacker has some knowledge of the underlying data distribution.
SurvivalGAN: Generating Time-to-Event Data for Survival Analysis
SurvivalGAN outperforms multiple baselines at generating survival data, and in particular addresses the failure modes as measured by the new metrics, in addition to improving downstream performance of survival models trained on the synthetic data.
Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data
We study the problem of inferring heterogeneous treatment effects (HTEs) from time-to-event data in the presence of competing events.
Improving Adaptive Conformal Prediction Using Self-Supervised Learning
However, the use of self-supervision beyond model pretraining and representation learning has been largely unexplored.
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation
Personalized treatment effect estimates are often of interest in high-stakes applications -- thus, before deploying a model estimating such effects in practice, one needs to be sure that the best candidate from the ever-growing machine learning toolbox for this task was chosen.
TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine
TemporAI is an open source Python software library for machine learning (ML) tasks involving data with a time component, focused on medicine and healthcare use cases.
Joint Training of Deep Ensembles Fails Due to Learner Collusion
In the case of deep ensembles of neural networks, we are provided with the opportunity to directly optimize the true objective: the joint performance of the ensemble as a whole.
Synthcity: facilitating innovative use cases of synthetic data in different data modalities
Synthcity is an open-source software package for innovative use cases of synthetic data in ML fairness, privacy and augmentation across diverse tabular data modalities, including static data, regular and irregular time series, data with censoring, multi-source data, composite data, and more.
Navigating causal deep learning
With CDL, researchers aim to structure and encode causal knowledge in the extremely flexible representation space of deep learning models.
Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes
It is important to guarantee that machine learning algorithms deployed in the real world do not result in unfairness or unintended social consequences.
DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems
However, this remains a nascent area with no standardized framework to guide practitioners to the necessary data-centric considerations or to communicate the design of data-centric driven ML systems.
Composite Feature Selection using Deep Ensembles
To do so, we define predictive groups in terms of linear and non-linear interactions between features.
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data
High model performance, on average, can hide that models may systematically underperform on subgroups of the data.
AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning
However, the use of machine learning introduces a number of technical and practical challenges that have thus far restricted widespread adoption of such techniques in clinical settings.
Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning
Consider making a prediction over new test data without any opportunity to learn from a training set of labelled data - instead given access to a set of expert models and their predictions alongside some limited information about the dataset used to train them.
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation
Consider the problem of improving the estimation of conditional average treatment effects (CATE) for a target domain of interest by leveraging related information from a source domain with a different feature space.
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations
We further demonstrate empirically that CARs offer (1) more accurate descriptions of how concepts are scattered in the DNN's latent space; (2) global explanations that are closer to human concept annotations and (3) concept-based feature importance that meaningfully relate concepts with each other.
Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions
We study the problem of adaptively identifying patient subpopulations that benefit from a given treatment during a confirmatory clinical trial.
DAPDAG: Domain Adaptation via Perturbed DAG Reconstruction
The encoder is designed to serve as an inference device on $E$ while the decoder reconstructs each observed variable conditioned on its graphical parents in the DAG and the inferred $E$.
DAUX: a Density-based Approach for Uncertainty eXplanations
Uncertainty quantification (UQ) is essential for creating trustworthy machine learning models.
D-CIPHER: Discovery of Closed-form Partial Differential Equations
To this end, we propose D-CIPHER, which is robust to measurement artifacts and can uncover a new and very general class of differential equations.
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
To assess solutions to this problem, we propose a controllable simulation environment based on a model of tumor growth for a range of scenarios with irregular sampling reflective of a variety of clinical scenarios.
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability
This allows us to evaluate treatment effect estimators along a new and important dimension that has been overlooked in previous work: We construct a benchmarking environment to empirically investigate the ability of personalized treatment effect models to identify predictive covariates -- covariates that determine differential responses to treatment.
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection
Consider the problem of imputing missing values in a dataset.
Differentiable and Transportable Structure Learning
Directed acyclic graphs (DAGs) encode a lot of information about a particular distribution in their structure.
Neural Laplace: Learning diverse classes of differential equations in the Laplace domain
Neural Ordinary Differential Equations model dynamical systems with ODEs learned by neural networks.
POETREE: Interpretable Policy Learning with Adaptive Decision Trees
Building models of human decision-making from observed behaviour is critical to better understand, diagnose and support real-world policies such as clinical care.
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies
Human decision making is well known to be imperfect and the ability to analyse such processes individually is crucial when attempting to aid or improve a decision-maker's ability to perform a task, e. g. to alert them to potential biases or oversights on their part.
Label-Free Explainability for Unsupervised Models
Unsupervised black-box models are challenging to interpret.
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects
While observational data is confounded, randomized data is unconfounded, but its sample size is usually too small to learn heterogeneous treatment effects.
Inferring Lexicographically-Ordered Rewards from Preferences
Modeling the preferences of agents over a set of alternatives is a principal concern in many areas.
Data-SUITE: Data-centric identification of in-distribution incongruous examples
These estimators can be used to evaluate the congruence of test instances with respect to the training set, to answer two practically useful questions: (1) which test instances will be reliably predicted by a model trained with the training instances?
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
However, no imputation at all also leads to biased estimates, as missingness determined by treatment introduces bias in covariates.
Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time
Choosing the best treatment-plan for each individual patient requires accurate forecasts of their outcome trajectories as a function of the treatment, over time.
Estimating Multi-cause Treatment Effects via Single-cause Perturbation
Most existing methods for conditional average treatment effect estimation are designed to estimate the effect of a single cause - only one variable can be intervened on at one time.
Time-series Generation by Contrastive Imitation
In this work, we study a generative framework that seeks to combine the strengths of both: Motivated by a moment-matching objective to mitigate compounding error, we optimize a local (but forward-looking) transition policy, where the reinforcement signal is provided by a global (but stepwise-decomposable) energy model trained by contrastive estimation.
Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation
Significant effort has been placed on developing decision support tools to improve patient care.
SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes
Most of the medical observational studies estimate the causal treatment effects using electronic health records (EHR), where a patient's covariates and outcomes are both observed longitudinally.
Invariant Causal Imitation Learning for Generalizable Policies
By leveraging data from multiple environments, we propose Invariant Causal Imitation Learning (ICIL), a novel technique in which we learn a feature representation that is invariant across domains, on the basis of which we learn an imitation policy that matches expert behavior.
Conformal Time-series Forecasting
Current approaches for multi-horizon time series forecasting using recurrent neural networks (RNNs) focus on issuing point estimates, which is insufficient for decision-making in critical application domains where an uncertainty estimate is also required.
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms
Missing data is an important problem in machine learning practice.
Explaining Latent Representations with a Corpus of Examples
SimplEx uses the corpus to improve the user's understanding of the latent space with post-hoc explanations answering two questions: (1) Which corpus examples explain the prediction issued for a given test example?
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data
We study the problem of inferring heterogeneous treatment effects from time-to-event data.
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks
In this paper, we introduce DECAF: a GAN-based fair synthetic data generator for tabular data.
Self-Supervision Enhanced Feature Selection with Correlated Gates
Discovering relevant input features for predicting a target variable is a key scientific question.
D-CODE: Discovering Closed-form ODEs from Observed Trajectories
In the experiments, D-CODE successfully discovered the governing equations of a diverse range of dynamical systems under challenging measurement settings with high noise and infrequent sampling.
Conservative Policy Construction Using Variational Autoencoders for Logged Data with Missing Values
Secondly, such datasets are usually imperfect, additionally cursed with missing values in the attributes of features.
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects
Conditional average treatment effects (CATEs) allow us to understand the effect heterogeneity across a large population of individuals.
Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators
The machine learning toolbox for estimation of heterogeneous treatment effects from observational data is expanding rapidly, yet many of its algorithms have been evaluated only on a very limited set of semi-synthetic benchmark datasets.
Inverse Contextual Bandits: Learning How Behavior Evolves over Time
Understanding a decision-maker's priorities by observing their behavior is critical for transparency and accountability in decision processes, such as in healthcare.
Explaining Time Series Predictions with Dynamic Masks
How can we explain the predictions of a machine learning model?
The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation
Understanding decision-making in clinical environments is of paramount importance if we are to bring the strengths of machine learning to ultimately improve patient outcomes.
On Inductive Biases for Heterogeneous Treatment Effect Estimation
We investigate how to exploit structural similarities of an individual's potential outcomes (POs) under different treatments to obtain better estimates of conditional average treatment effects in finite samples.
Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression
To close this gap, we propose the latent hybridisation model (LHM) that integrates a system of expert-designed ODEs with machine-learned Neural ODEs to fully describe the dynamics of the system and to link the expert and latent variables to observable quantities.
Neural graphical modelling in continuous-time: consistency guarantees and algorithms
In this paper, we consider score-based structure learning for the study of dynamical systems.
Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confounding
Unobserved confounding is one of the greatest challenges for causal discovery.
Model-Attentive Ensemble Learning for Sequence Modeling
Medical time-series datasets have unique characteristics that make prediction tasks challenging.
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models
In this paper, we introduce a 3-dimensional evaluation metric, ($\alpha$-Precision, $\beta$-Recall, Authenticity), that characterizes the fidelity, diversity and generalization performance of any generative model in a domain-agnostic fashion.
Scalable Bayesian Inverse Reinforcement Learning
Bayesian inference over the reward presents an ideal solution to the ill-posed nature of the inverse reinforcement learning problem.
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge
We leverage the invariance of causal structures across domains to propose a novel model selection metric specifically designed for ITE methods under the UDA setting.
A Variational Information Bottleneck Approach to Multi-Omics Data Integration
Due to non-uniformity and technical limitations in omics platforms, such integrative analyses on multiple omics, which we refer to as views, involve learning from incomplete observations with various view-missing patterns.
Policy Analysis using Synthetic Controls in Continuous-Time
Counterfactual estimation using synthetic controls is one of the most successful recent methodological developments in causal inference.
Learning Matching Representations for Individualized Organ Transplantation Allocation
Organ transplantation is often the last resort for treating end-stage illness, but the probability of a successful transplantation depends greatly on compatibility between donors and recipients.
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms
The need to evaluate treatment effectiveness is ubiquitous in most of empirical science, and interest in flexibly investigating effect heterogeneity is growing rapidly.
SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups
Phase I clinical trials are designed to test the safety (non-toxicity) of drugs and find the maximum tolerated dose (MTD).
Personalized Education in the AI Era: What to Expect Next?
The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to ultimately meet her desired goal.
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning
Understanding human behavior from observed data is critical for transparency and accountability in decision-making.
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.
Scalable Bayesian Inverse Reinforcement Learning by Auto-Encoding Reward
Bayesian inference over the reward presents an ideal solution to the ill-posed nature of the inverse reinforcement learning problem.
SyncTwin: Transparent Treatment Effect Estimation under Temporal Confounding
Estimating causal treatment effects using observational data is a problem with few solutions when the confounder has a temporal structure, e. g. the history of disease progression might impact both treatment decisions and clinical outcomes.
Generative Time-series Modeling with Fourier Flows
Generating synthetic time-series data is crucial in various application domains, such as medical prognosis, wherein research is hamstrung by the lack of access to data due to concerns over privacy.
Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods
Many ground-breaking advancements in machine learning can be attributed to the availability of a large volume of rich data.
OrganITE: Optimal transplant donor organ offering using an individual treatment effect
Transplant-organs are a scarce medical resource.
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.
Gradient Regularized V-Learning for Dynamic Treatment Regimes
A dynamic treatment regime (DTR) is a sequence of treatment rules indicating how to individualize treatments for a patient based on the previously assigned treatments and the evolving covariate history.
Learning outside the Black-Box: The pursuit of interpretable models
Machine Learning has proved its ability to produce accurate models but the deployment of these models outside the machine learning community has been hindered by the difficulties of interpreting these models.
CASTLE: Regularization via Auxiliary Causal Graph Discovery
Regularization improves generalization of supervised models to out-of-sample data.
Estimating Structural Target Functions using Machine Learning and Influence Functions
Within this framework, we propose two general learning algorithms that build on the idea of nonparametric plug-in bias removal via IFs: the 'IF-learner' which uses pseudo-outcomes motivated by uncentered IFs for regression in large samples and outputs entire target functions without confidence bands, and the 'Group-IF-learner', which outputs only approximations to a function but can give confidence estimates if sufficient information on coarsening mechanisms is available.
CPAS: the UK's National Machine Learning-based Hospital Capacity Planning System for COVID-19
The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources.
Hide-and-Seek Privacy Challenge
The clinical time-series setting poses a unique combination of challenges to data modeling and sharing.
Accounting for Unobserved Confounding in Domain Generalization
Part of the challenge of learning robust models lies in the influence of unobserved confounders that void many of the invariances and principles of minimum error presently used for this problem.
Learning "What-if" Explanations for Sequential Decision-Making
Building interpretable parameterizations of real-world decision-making on the basis of demonstrated behavior -- i. e. trajectories of observations and actions made by an expert maximizing some unknown reward function -- is essential for introspecting and auditing policies in different institutions.
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions
Deep learning models achieve high predictive accuracy across a broad spectrum of tasks, but rigorously quantifying their predictive uncertainty remains challenging.
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift
In this paper, we develop an approximate Bayesian inference scheme based on posterior regularisation, wherein unlabelled target data are used as "pseudo-labels" of model confidence that are used to regularise the model's loss on labelled source data.
Inverse Active Sensing: Modeling and Understanding Timely Decision-Making
Finally, we illustrate how this formulation enables understanding decision-making behavior by quantifying preferences implicit in observed decision strategies (the inverse problem).
Strictly Batch Imitation Learning by Energy-based Distribution Matching
Through experiments with application to control and healthcare settings, we illustrate consistent performance gains over existing algorithms for strictly batch imitation learning.
AutoCP: Automated Pipelines for Accurate Prediction Intervals
Successful application of machine learning models to real-world prediction problems, e. g. financial forecasting and personalized medicine, has proved to be challenging, because such settings require limiting and quantifying the uncertainty in the model predictions, i. e. providing valid and accurate prediction intervals.
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions
Recurrent neural networks (RNNs) are instrumental in modelling sequential and time-series data.
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression
In this paper, we develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest (e. g., adverse events, the onset of comorbidities).
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification
Most of the current methods of subgroup analysis begin with a particular algorithm for estimating individualized treatment effects (ITE) and identify subgroups by maximizing the difference across subgroups of the average treatment effect in each subgroup.
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints
Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds (or combination of them) becomes more complex.
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes
To this end, this paper develops a Bayesian model for predicting the effects of COVID-19 lockdown policies in a global context -- we treat each country as a distinct data point, and exploit variations of policies across countries to learn country-specific policy effects.
A Non-Stationary Bandit-Learning Approach to Energy-Efficient Femto-Caching with Rateless-Coded Transmission
The former problem boils down to a stochastic knapsack problem, and we cast the latter as a multi-armed bandit problem.
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks
While much attention has been given to the problem of estimating the effect of discrete interventions from observational data, relatively little work has been done in the setting of continuous-valued interventions, such as treatments associated with a dosage parameter.
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations
Identifying when to give treatments to patients and how to select among multiple treatments over time are important medical problems with a few existing solutions.
Target-Embedding Autoencoders for Supervised Representation Learning
Autoencoder-based learning has emerged as a staple for disciplining representations in unsupervised and semi-supervised settings.
Learning Overlapping Representations for the Estimation of Individualized Treatment Effects
The choice of making an intervention depends on its potential benefit or harm in comparison to alternatives.
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning
In this paper, we propose a novel Bayesian optimization (BO) algorithm to tackle the challenge of model selection in this setting.
Contextual Constrained Learning for Dose-Finding Clinical Trials
In addition, patient recruitment can be difficult by the fact that clinical trials do not aim to provide a benefit to any given patient in the trial.
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes
Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals.
Individualised Dose-Response Estimation using Generative Adversarial Nets
Our model consists of 3 blocks: (1) a generator, (2) a discriminator, (3) an inference block.
A Bayesian Approach to Modelling Longitudinal Data in Electronic Health Records
Analyzing electronic health records (EHR) poses significant challenges because often few samples are available describing a patient's health and, when available, their information content is highly diverse.
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.
Attentive State-Space Modeling of Disease Progression
Models of disease progression are instrumental for predicting patient outcomes and understanding disease dynamics.
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.
Demystifying Black-box Models with Symbolic Metamodels
A symbolic metamodel is a model of a model, i. e., a surrogate model of a trained (machine learning) model expressed through a succinct symbolic expression that comprises familiar mathematical functions and can be subjected to symbolic manipulation.
Improving Model Robustness Using Causal Knowledge
We show in this paper how prior knowledge in the form of a causal graph can be utilized to guide model selection, i. e., to identify from a set of trained networks the models that are the most robust and invariant to unseen domains.
Actor-Critic Approach for Temporal Predictive Clustering
In this paper, we develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest (e. g., adverse events, the onset of comorbidities, etc.).
The Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions
To address this question, we develop the discriminative jackknife (DJ), a formal inference procedure that constructs predictive confidence intervals for a wide range of deep learning models, is easy to implement, and provides rigorous theoretical guarantees on (1) and (2).
Conditional Independence Testing using Generative Adversarial Networks
We consider the hypothesis testing problem of detecting conditional dependence, with a focus on high-dimensional feature spaces.
Kernel Hypothesis Testing with Set-valued Data
We present a general framework for hypothesis testing on distributions of sets of individual examples.
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).
Lifelong Bayesian Optimization
In this paper, we present Lifelong Bayesian Optimization (LBO), an online, multitask Bayesian optimization (BO) algorithm designed to solve the problem of model selection for datasets arriving and evolving over time.
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.
A Deep Learning Approach for Dynamic Survival Analysis with Competing Risks
Currently available survival analysis methods are limited in their ability to deal with complex, heterogeneous, and longitudinal data such as that available in primary care records, or in their ability to deal with multiple competing risks.
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.
Machine Learning in the Air
Thanks to the recent advances in processing speed and data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner.
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders
The estimation of treatment effects is a pervasive problem in medicine.
Multitask Boosting for Survival Analysis with Competing Risks
The co-occurrence of multiple diseases among the general population is an important problem as those patients have more risk of complications and represent a large share of health care expenditure.
What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems
Our results demonstrate that ML experts cannot accurately predict which system outputs will maximize clinicians' confidence in the underlying neural network model, and suggest additional findings that have broad implications to the future of research into ML interpretability and the use of ML in medicine.
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.
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.
Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning
Estimating the individual treatment effect (ITE) from observational data is essential in medicine.
Risk-Stratify: Confident Stratification Of Patients Based On Risk
A clinician desires to use a risk-stratification method that achieves confident risk-stratification - the risk estimates of the different patients reflect the true risks with a high probability.
Joint Concordance Index
We use the new metric to develop a variable importance ranking approach.
Methodology
Forecasting Individualized Disease Trajectories using Interpretable Deep Learning
In this paper, we develop the phased attentive state space (PASS) model of disease progression, a deep probabilistic model that captures complex representations for disease progression while maintaining clinical interpretability.
Ranked #1 on
Disease Trajectory Forecasting
on UK CF trust
Adaptive Clinical Trials: Exploiting Sequential Patient Recruitment and Allocation
Randomized Controlled Trials (RCTs) are the gold standard for comparing the effectiveness of a new treatment to the current one (the control).
Siamese Survival Analysis with Competing Risks
Survival analysis in the presence of multiple possible adverse events, i. e., competing risks, is a pervasive problem in many industries (healthcare, finance, etc.).
Forecasting Disease Trajectories in Alzheimer's Disease Using Deep Learning
Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time.
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.
Optimal Piecewise Local-Linear Approximations
Piecewise local-linear models provide a natural way to extend local-linear models to explain the global behavior of the model.
GAIN: Missing Data Imputation using Generative Adversarial Nets
Accordingly, we call our method Generative Adversarial Imputation Nets (GAIN).
Disease-Atlas: Navigating Disease Trajectories with Deep Learning
Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time.
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
AUTOPROGNOSIS optimizes ensembles of pipeline configurations efficiently using a novel batched Bayesian optimization (BO) algorithm that learns a low-dimensional decomposition of the pipelines high-dimensional hyperparameter space in concurrence with the BO procedure.
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.
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.
Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms
We investigate the problem of estimating the causal effect of a treatment on individual subjects from observational data, this is a central problem in various application domains, including healthcare, social sciences, and online advertising.
DPSCREEN: Dynamic Personalized Screening
However, there has been limited work to address the personalized screening for these different diseases.
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).
Deep Counterfactual Networks with Propensity-Dropout
The network is trained in alternating phases, where in each phase we use the training examples of one of the two potential outcomes (treated and control populations) to update the weights of the shared layers and the respective outcome-specific layers.
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.
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.
Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis
Our model captures "informatively sampled" patient episodes: the clinicians' decisions on when to observe a hospitalized patient's vital signs and lab tests over time are represented by a marked Hawkes process, with intensity parameters that are modulated by the patient's latent clinical states, and with observable physiological data (mark process) modeled as a switching multi-task Gaussian process.
Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing
In our algorithm, a local controller (LC) in the mobile device of a worker regularly observes the worker's context, her/his decisions to accept or decline tasks and the quality in completing tasks.
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
Stemming from the potential outcomes model, we propose a novel multi- task learning framework in which factual and counterfactual outcomes are mod- eled as the outputs of a function in a vector-valued reproducing kernel Hilbert space (vvRKHS).
Constructing Effective Personalized Policies Using Counterfactual Inference from Biased Data Sets with Many Features
This paper proposes a novel approach for constructing effective personalized policies when the observed data lacks counter-factual information, is biased and possesses many features.
A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference
Modeling continuous-time physiological processes that manifest a patient's evolving clinical states is a key step in approaching many problems in healthcare.
A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics
Estimating patient's clinical state from multiple concurrent physiological streams plays an important role in determining if a therapeutic intervention is necessary and for triaging patients in the hospital.
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).
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.
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.
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition
We develop a Bayesian model for decision-making under time pressure with endogenous information acquisition.
Context-Aware Proactive Content Caching with Service Differentiation in Wireless Networks
We derive a sublinear regret bound, which characterizes the learning speed and proves that our algorithm converges to the optimal cache content placement strategy in terms of maximizing the number of cache hits.
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.
Data-Driven Online Decision Making with Costly Information Acquisition
In most real-world settings such as recommender systems, finance, and healthcare, collecting useful information is costly and requires an active choice on the part of the decision maker.
ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening
A cluster of patients is a set of patients with similar features (e. g. age, breast density, family history, etc.
Personalized Course Sequence Recommendations
Given the variability in student learning it is becoming increasingly important to tailor courses as well as course sequences to student needs.
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.
Predicting Grades
We derive a confidence estimate for the prediction accuracy and demonstrate the performance of our algorithm on a dataset obtained based on the performance of approximately 700 UCLA undergraduate students who have taken an introductory digital signal processing over the past 7 years.
Episodic Multi-armed Bandits
After the $stop$ action is taken, the learner collects a terminal reward, and observes the costs and terminal rewards associated with each step of the episode.
Global Bandits
In the case in which rewards of all arms are deterministic functions of a single unknown parameter, we construct a greedy policy that achieves {\em bounded regret}, with a bound that depends on the single true parameter of the problem.
Contextual Online Learning for Multimedia Content Aggregation
A key challenge for such systems is to accurately predict what type of content each of its consumers prefers in a certain context, and adapt these predictions to the evolving consumers' preferences, contexts and content characteristics.
RELEAF: An Algorithm for Learning and Exploiting Relevance
We prove a general regret bound for our algorithm whose time order depends only on the maximum number of relevant dimensions among all the actions, which in the special case where the relevance relation is single-valued (a function), reduces to $\tilde{O}(T^{2(\sqrt{2}-1)})$; in the absence of a relevance relation, the best known contextual bandit algorithms achieve regret $\tilde{O}(T^{(D+1)/(D+2)})$, where $D$ is the full dimension of the context vector.
Discovering, Learning and Exploiting Relevance
When the relation is a function, i. e., the reward of an action only depends on the context of a single type, and the expected reward of an action is Lipschitz continuous in the context of its relevant type, we propose an algorithm that achieves $\tilde{O}(T^{\gamma})$ regret with a high probability, where $\gamma=2/(1+\sqrt{2})$.
Jamming Bandits
We first present novel online learning algorithms to maximize the jamming efficacy against static transmitter-receiver pairs and prove that our learning algorithm converges to the optimal (in terms of the error rate inflicted at the victim and the energy used) jamming strategy.
Global Bandits with Holder Continuity
Specifically, we prove that the parameter-free (worst-case) regret is sublinear in time, and decreases with the informativeness of the arms.
Forecasting Popularity of Videos using Social Media
This paper presents a systematic online prediction method (Social-Forecast) that is capable to accurately forecast the popularity of videos promoted by social media.
Distributed Online Learning in Social Recommender Systems
In contrast to centralized recommender systems, in which there is a single centralized seller who has access to the complete inventory of items as well as the complete record of sales and user information, in decentralized recommender systems each seller/learner only has access to the inventory of items and user information for its own products and not the products and user information of other sellers, but can get commission if it sells an item of another seller.
Ensemble of Distributed Learners for Online Classification of Dynamic Data Streams
We present an efficient distributed online learning scheme to classify data captured from distributed, heterogeneous, and dynamic data sources.
Decentralized Online Big Data Classification - a Bandit Framework
We model the problem of joint classification by the distributed and heterogeneous learners from multiple data sources as a distributed contextual bandit problem where each data is characterized by a specific context.
Distributed Online Learning via Cooperative Contextual Bandits
At each moment of time, an instance characterized by a certain context may arrive to each learner; based on the context, the learner can select one of its own actions (which gives a reward and provides information) or request assistance from another learner.