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08/09/2023
Do Diffusion Models Suffer Error Propagation? Theoretical Analysis and Consistency Regularization
While diffusion models have achieved promising performances in data synt...
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06/08/2023
Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models
Tabular data is often hidden in text, particularly in medical diagnostic...
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06/07/2023
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time
Machine learning (ML) holds great potential for accurately forecasting t...
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06/07/2023
U-PASS: an Uncertainty-guided deep learning Pipeline for Automated Sleep Staging
As machine learning becomes increasingly prevalent in critical fields su...
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05/31/2023
Learning Representations without Compositional Assumptions
This paper addresses unsupervised representation learning on tabular dat...
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05/16/2023
Synthetic data, real errors: how (not) to publish and use synthetic data
Generating synthetic data through generative models is gaining interest ...
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04/13/2023
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance
Interpretability methods are valuable only if their explanations faithfu...
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04/07/2023
Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data
Generating synthetic data through generative models is gaining interest ...
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04/07/2023
Machine Learning with Requirements: a Manifesto
In the recent years, machine learning has made great advancements that h...
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03/09/2023
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization
Despite their success with unstructured data, deep neural networks are n...
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03/03/2023
Causal Deep Learning
Causality has the potential to truly transform the way we solve a large ...
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03/02/2023
Learning machines for health and beyond
Machine learning techniques are effective for building predictive models...
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02/24/2023
T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression
Clustering time-series data in healthcare is crucial for clinical phenot...
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02/24/2023
Neural Laplace Control for Continuous-time Delayed Systems
Many real-world offline reinforcement learning (RL) problems involve con...
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02/24/2023
Membership Inference Attacks against Synthetic Data through Overfitting Detection
Data is the foundation of most science. Unfortunately, sharing data can ...
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02/23/2023
Improving Adaptive Conformal Prediction Using Self-Supervised Learning
Conformal prediction is a powerful distribution-free tool for uncertaint...
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02/23/2023
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)...
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02/06/2023
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-st...
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01/28/2023
TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine
TemporAI is an open source Python software library for machine learning ...
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01/26/2023
Joint Training of Deep Ensembles Fails Due to Learner Collusion
Ensembles of machine learning models have been well established as a pow...
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01/18/2023
Synthcity: facilitating innovative use cases of synthetic data in different data modalities
Synthcity is an open-source software package for innovative use cases of...
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12/01/2022
Navigating causal deep learning
Causal deep learning (CDL) is a new and important research area in the l...
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11/11/2022
Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes
It is important to guarantee that machine learning algorithms deployed i...
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11/09/2022
DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems
While there have been a number of remarkable breakthroughs in machine le...
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10/24/2022
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data
High model performance, on average, can hide that models may systematica...
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10/21/2022
AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning
Diagnostic and prognostic models are increasingly important in medicine ...
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10/11/2022
Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning
Consider making a prediction over new test data without any opportunity ...
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10/08/2022
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation
Consider the problem of improving the estimation of conditional average ...
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09/22/2022
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations
Concept-based explanations permit to understand the predictions of a dee...
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08/11/2022
Adaptively Identifying Patient Populations With Treatment Benefit in Clinical Trials
We study the problem of adaptively identifying patient subpopulations th...
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08/02/2022
DAPDAG: Domain Adaptation via Perturbed DAG Reconstruction
Leveraging labelled data from multiple domains to enable prediction in a...
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07/11/2022
DAUX: a Density-based Approach for Uncertainty eXplanations
Uncertainty quantification (UQ) is essential for creating trustworthy ma...
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06/21/2022
D-CIPHER: Discovery of Closed-form PDEs
Closed-form differential equations, including partial differential equat...
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06/16/2022
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability
Estimating personalized effects of treatments is a complex, yet pervasiv...
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06/16/2022
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
Estimating counterfactual outcomes over time has the potential to unlock...
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06/15/2022
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection
Consider the problem of imputing missing values in a dataset. One the on...
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06/13/2022
Differentiable and Transportable Structure Learning
We are interested in unsupervised structure learning with a particular f...
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06/10/2022
Neural Laplace: Learning diverse classes of differential equations in the Laplace domain
Neural Ordinary Differential Equations model dynamical systems with ODEs...
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03/15/2022
POETREE: Interpretable Policy Learning with Adaptive Decision Trees
Building models of human decision-making from observed behaviour is crit...
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03/14/2022
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies
Human decision making is well known to be imperfect and the ability to a...
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03/03/2022
Label-Free Explainability for Unsupervised Models
Unsupervised black-box models are challenging to interpret. Indeed, most...
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02/25/2022
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects
Estimating heterogeneous treatment effects is an important problem acros...
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02/21/2022
Inferring Lexicographically-Ordered Rewards from Preferences
Modeling the preferences of agents over a set of alternatives is a princ...
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02/17/2022
Data-SUITE: Data-centric identification of in-distribution incongruous examples
Systematic quantification of data quality is critical for consistent mod...
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02/04/2022
To Impute or not to Impute? – Missing Data in Treatment Effect Estimation
Missing data is a systemic problem in practical scenarios that causes no...
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12/07/2021
Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time
Choosing the best treatment-plan for each individual patient requires ac...
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11/04/2021
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms
Missing data is an important problem in machine learning practice. Start...
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10/28/2021
Explaining Latent Representations with a Corpus of Examples
Modern machine learning models are complicated. Most of them rely on con...
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10/26/2021
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data
We study the problem of inferring heterogeneous treatment effects from t...
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10/25/2021