Analysis of large amounts of data offers new opportunities to understand many processes better. Yet, data accumulation often implies relaxing acquisition procedures or compounding diverse sources, leading to many observations with missing features. From questionnaires to collaborative filtering, from electronic health records to single-cell analysis, missingness is everywhere at play and is rather the norm than the exception. Even "clean" data sets are often barely "cleaned" versions of incomplete data sets—with all the unfortunate biases this cleaning process may have created.
Despite this ubiquity, tackling missing values is often overlooked. Handling missing values poses many challenges, and there is a vast literature in the statistical community, with many implementations available. Yet, there are still many open issues and the need to design new methods or to introduce new point of views: for missing values in a supervised-learning setting, in deep learning architectures, to adapt available methods for high dimensional observed data with different type of missing values, deal with feature mismatch and distribution mismatch. Missing data is one of the eight pillars of causal wisdom for Judea Pearl who brought graphical model reasoning to tackle some missing not at random values.
The goal of the Art of Learning with Missing Values (ARTEMISS) workshop is to give more momentum and exposition to research on missing values, both theoretical, methodological, and applied, and emphasize the connections with other areas of machine learning (e.g. causal inference, semi-supervised learning, generative modelling, uncertainty quantification, transfer learning, distributional shift, etc.). We will also attach importance to discussing the reproducibility problems that can be caused by missing data, the danger of forgetting the missing values issues and the importance of providing sound implementations.
Senior Principal Researcher at Microsoft Research.
Mihaela van der Schaar
John Humphrey Plummer Professor at the University of Cambridge and Turing Fellow at The Alan Turing Institute.
Postdoctoral scholar at CHAI in UC Berkeley.
Assistant Professor at the University of Washington.
The accepted papers are listed below and available at OpenReview. This does not constitute a proceeding for the workshop.
Optimal recovery of missing values for non-negative matrix factorization: A probabilistic error bound
Rebecca Chen, Lav R. Varshney
Visna---Visualising Multivariate Missing Values
Antony Unwin, Alexander Pilhoefer
Causal Discovery in the Presence of Missing Values for Neuropathic Pain Diagnosis
Ruibo Tu, Kun Zhang, Bo Christer Bertilson, Clark Glymour, Hedvig Kjellström, Cheng Zhang
Multi-output prediction of global vegetation distribution with incomplete data
Rita Beigaite, Jesse Read, Indre Zliobaite
A Random Matrix Analysis of Learning with α-Dropout
Mohamed El Amine Seddik, Romain Couillet, Mohamed Tamaazousti
Path Imputation Strategies for Signature Models
Michael Moor, Max Horn, Christian Bock, Karsten Borgwardt, Bastian Rieck
Clustering Data with nonignorable Missingness using Semi-Parametric Mixture Models
Marie Du Roy de Chaumaray, Matthieu Marbac
Estimating conditional density of missing values using deep Gaussian mixture model
Marcin Przewięźlikowski, Marek Śmieja, Łukasz Struski
Does imputation matter? Benchmark for real-life classification problems.
Katarzyna Woźnica, Przemyslaw Biecek
VAEs in the Presence of Missing Data
Mark Collier, Alfredo Nazabal, Chris Williams
Missing the Point: Non-Convergence in Iterative Imputation Algorithms
Hanne I. Oberman, Stef van Buuren, Gerko Vink
Predicting Feature Imputability in the Absence of Ground Truth
Niamh McCombe, Xuemei Ding, Girijesh Prasad, David P Finn, Stephen Todd, Paula L McClean, Kongfatt Wong-Lin
Variance estimation after Kernel Ridge Regression Imputation
Hengfang Wang, Jae Kwang Kim
Online Mixed Missing Value Imputation Using Gaussian Copula
Eric Landgrebe, yuxuan zhao, Madeleine Udell
Imputation of Missing Behavioral Measures in Connectome-based Predictive Modelling
Qinghao Liang, Dustin Scheinost
Handling Missing Data in Decision Trees: A Probabilistic Approach
Pasha Khosravi, antonio vergari, YooJung Choi, Yitao Liang, Guy Van den Broeck
The Dynamic Latent Block Model for Sparse and Evolving Count Matrices
Giulia Marchello, Marco Corneli, Charles Bouveyron
Missing rating imputation based on product reviews via deep latent variable models
Dingge Liang, Marco Corneli, Pierre Latouche, Charles Bouveyron
Lung Segmentation from Chest X-rays using Variational Data Imputation
Raghavendra Selvan, Erik Dam, Nicki Skafte Detlefsen, Sofus Rischel, Kaining Sheng, Mads Nielsen, Akshay Pai
Inferring Causal Dependencies between Chaotic Dynamical Systems from Sporadic Time Series
Edward De Brouwer, Adam Arany, Jaak Simm, Yves Moreau
The impact of incomplete data on quantile regression for longitudinal data
Anneleen Verhasselt, Alvaro José Flórez, Ingrid Van Keilegom, Geert Molenberghs
Multi-label Learning with Missing Values using Combined Facial Action Unit Datasets
Jaspar Pahl, Ines Rieger, Dominik Seuss
A Study on Intentional-Value-Substitution Training for Regression with Incomplete Information
Takuya Fukushima, Tomoharu Nakashima, Taku Hasegawa, Vicenç Torra
How to miss data? Reinforcement learning for environments with high observation cost
Mehmet Koseoglu, Ayca Ozcelikkale
Processing of incomplete images by (graph) convolutional neural networks
Tomasz Danel, Marek Śmieja, Łukasz Struski, Przemysław Spurek, Lukasz Maziarka
Conditioning on "and nothing else": Simple Models of Missing Data between Naive Bayes and Logistic Regression
David Poole, Ali Mohammad Mehr, Wan Shing Martin Wang
Multi-Time Attention Networks for Irregularly Sampled Time Series
Satya Narayan Shukla, Benjamin Marlin
How to deal with missing data in supervised deep learning?
Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
Chao Ma, Sebastian Tschiatschek, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang
Working with Deep Generative Models and Tabular Data Imputation
Ramiro Camino, Christian Hammerschmidt, Radu State
Information Theoretic Approaches for Testing Missingness in Predictive Models
Shreyas A Bhave, Rajesh Ranganath, Adler Perotte
We welcome short papers from both academic and industrial practitioners/researchers. In particular, since missing data is a critical issue in many domains, we would like to federate industrial/applied know-how and various academic approaches. We also welcome very applied work from areas others than machine learning and statistics.
Authors should submit extended abstracts of no more than four pages (excluding references)
using the ICML LaTeX style files. Adding an appendix is permitted but reviewers will not be required to read it.
Submissions will be reviewed single-blind (reviewers are anonymous), so authors names and affiliations should be included in the submission. Submissions and reviews are private; only accepted papers will be publicly available on OpenReview. Public commentary is not allowed.
The deadline for submissions is May 20 2020 11:59PM UTC-0.
The deadline for submissions has been extended to June 10 2020 11:59PM UTC-0.
Please use the ARTEMISS LaTeX style files when submitting the camera ready version.
We thankful to the members of the program committee, who contributed to shaping this workshop: