Demo Data from SIGIR 2026 / reviewers in the wild / expert
Niels Bruun Ipsen
dblp:241/7122
· DBLP profile ↗
3ranked-venue papers
3as first author
2since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 3 first-author · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
3 papers |
Probabilistic and Bayesian machine learning · 60% Learning paradigms · 12% Generative modeling · 11% |
Topics — the 8 heaviest of 9, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Probabilistic and Bayesian machine learning
missing data |
1.1 | 2 | 2022 | How to deal with missing data in supervised deep learning? · ICLR 2022 not-MIWAE: Deep Generative Modelling with Missing not at Random Data · ICLR 2021 |
Machine learning › Probabilistic and Bayesian machine learning › missing data
missing data imputation |
1.1 | 2 | 2022 | How to deal with missing data in supervised deep learning? · ICLR 2022 not-MIWAE: Deep Generative Modelling with Missing not at Random Data · ICLR 2021 |
Machine learning › Learning paradigms
supervised learning |
0.6 | 1 | 2022 | How to deal with missing data in supervised deep learning? · ICLR 2022 |
Machine learning › Probabilistic and Bayesian machine learning › missing data
missing not at random |
0.5 | 1 | 2021 | not-MIWAE: Deep Generative Modelling with Missing not at Random Data · ICLR 2021 |
Machine learning › Generative modeling
variational autoencoder |
0.5 | 1 | 2021 | not-MIWAE: Deep Generative Modelling with Missing not at Random Data · ICLR 2021 |
Machine learning › Learning theory › statistical learning theory › statistical physics of learning
phase transition analysis |
0.4 | 1 | 2019 | Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size! · ICML 2019 |
Machine learning › Representation and self-supervised learning › representation learning › dimensionality reduction
principal component analysis |
0.4 | 1 | 2019 | Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size! · ICML 2019 |
Machine learning › Probabilistic and Bayesian machine learning › structured models › latent variable model › latent gaussian model
probabilistic PCA |
0.1 | 1 | 2019 | Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size! · ICML 2019 |
Methods — techniques the papers use, named apart from their topics
signal-to-noise ratio analysis · 0.4probabilistic principal component analysis · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | How to deal with missing data in supervised deep learning?
Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen |
ICLR | 1 |
| 2021 | not-MIWAE: Deep Generative Modelling with Missing not at Random Data
Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen |
ICLR | 1 |
| 2019 | Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size!abstractHow does missing data affect our ability to learn signal structures? It has been shown that learning signal structure in terms of principal components is dependent on the ratio of sample size and dimensionality and that a critical number of observations is needed before learning starts (Biehl and Mietzner, 1993). Here we generalize this analysis to include missing data. Probabilistic principal component analysis is regularly used for estimating signal structures in datasets with missing data. Our analytic result suggest that the effect of missing data is to effectively reduce signal-to-noise ratio rather than - as generally believed - to reduce sample size. The theory predicts a phase transition in the learning curves and this is indeed found both in simulation data and in real datasets. Niels Bruun Ipsen, Lars Kai Hansen |
ICML | 1 |