Reposted by: Fredrik Johansson, Serge Belongie
We are delighted to announce the #EurIPS 2025 Workshops 🎉: eurips.cc/workshops/
We received 52 proposals, which were single-blind reviewed by more than 35 expert reviewers, leading to 18 accepted workshops (acceptance rate 34.6%).
We received 52 proposals, which were single-blind reviewed by more than 35 expert reviewers, leading to 18 accepted workshops (acceptance rate 34.6%).
Workshops - A NeurIPS-endorsed conference in Europe
A NeurIPS-endorsed conference in Europe held in Copenhagen, Denmark
eurips.cc
Anton's thesis: research.chalmers.se/publication/...
Lena's thesis: research.chalmers.se/en/publicati...
Lena's thesis: research.chalmers.se/en/publicati...
Interpretable Machine Learning for Modeling, Evaluating, and Refining Clinical Decision-Making
Machine learning offers great promise for developing new treatment policies from observational clinical data. However, a key challenge in this offline setting is reliably assessing the performance of ...
research.chalmers.se
This week has been an absolute joy for me as the leader of the Healthy AI Lab! Two of my students, Anton Matsson (3rd from right) and Lena Stempfle (2nd from right), defended their theses and became the first PhD graduates under my supervision 🎉 You will both be sorely missed!
Reposted by: Fredrik Johansson
We are hiring a *PhD student* in my group to work on machine learning generalization "out-of-table". Help build methods that learn from large volumes of tabular data to generate models for new tasks! Apply here: www.chalmers.se/en/about-cha...
Vacancies
www.chalmers.se
Reposted by: Fredrik Johansson
I'm thrilled to share that Lena Stempfle's, Anton Matsson's and Newton Mwai's paper "Prediction models that learn to avoid missing values" was accepted to ICML and awarded a spotlight! Arxiv here: arxiv.org/abs/2505.03393
More below👇
More below👇
Prediction Models That Learn to Avoid Missing Values
Handling missing values at test time is challenging for machine learning models, especially when aiming for both high accuracy and interpretability. Established approaches often add bias through imput...
arxiv.org
Going to ICML next week? Find Anton and talk to him about our work on prediction models that learn to avoid missing values!
In a tree, features are used depending on the values and availability of other features. By penalizing reliance on missing values in computing predictions, we can learn trees that are unlikely to ask for a feature unless it is available (and predictive)
In this work, we show that you can fit interpretable models that are unlikely to even *ask for* features that are missing at test time by learning to avoid using them in the first place. For trees, this is contextual...
If you want to fit an interpretable ML model like a tree or a GLM, how can you make sure that it's still accurate and interpretable when input features are missing at test time? Standard methods like imputation or missingness indicators tend to break one or the other...
10 days to go to apply for this PhD student position in my lab!
Last but not least: The student will be co-advised by the fantastic @urish.bsky.social!
Our CS department is listing 5 positions. Make sure to select "F. Johansson - Reducing waste in tabular machine learning by generalizing out-of-table" to indicate which project you would like to work in.
Deadline May 15!
Deadline May 15!
Our CS department is listing 5 positions. Make sure to select "F. Johansson - Reducing waste in tabular machine learning by generalizing out-of-table" to indicate which project you would like to work in.
Deadline May 15!
Deadline May 15!
No argument there—I'm pretty sure that's a different question though.
Clearly not but I was surprised at how many orals at NeurIPS were missing speakers too. Any chance of having reserve speakers? Sure, there is an honor in being awarded an oral but, as a conference-goer, having a 20-min gap in the schedule due to a missing speaker seems like a missed opportunity.
The first paper by Herman Bergström, Emil Carlsson, Devdatt Dubhashi, and me, explores how important context is to active preference learning from pairwise feedback: arxiv.org/abs/2405.03059
In the second, I develop a benchmark for evaluating estimators of causal effects. arxiv.org/abs/2405.16069
In the second, I develop a benchmark for evaluating estimators of causal effects. arxiv.org/abs/2405.16069
IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark
Evaluating observational estimators of causal effects demands information that is rarely available: unconfounded interventions and outcomes from the population of interest, created either by randomiza...
arxiv.org
I'm currently at NeurIPS eager to talk about the two papers from my group:
* Active Preference Learning for Ordering Items In-and Out-of-sample (arxiv.org/abs/2405.03059)
* IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark (arxiv.org/abs/2405.16069)
* Active Preference Learning for Ordering Items In-and Out-of-sample (arxiv.org/abs/2405.03059)
* IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark (arxiv.org/abs/2405.16069)
arxiv.org
Reposted by: Fredrik Johansson
Open for work!
Unfortunately, I just found out that all funding and grants for my 2025 projects are canceled.
Would love the opportunity to continue the musical journey started with #Ultros. Comfortable with #GameMusic #GameAudio. Retweets appreciated linktr.ee/ratvader
Unfortunately, I just found out that all funding and grants for my 2025 projects are canceled.
Would love the opportunity to continue the musical journey started with #Ultros. Comfortable with #GameMusic #GameAudio. Retweets appreciated linktr.ee/ratvader
Reposted by: Fredrik Johansson
📣 We have a tenure-track faculty opening in Responsible AI at @ethzurich.bsky.social :
ethz.ch/en/the-eth-z.... Deadline Nov 30 for full consideration. ETH Zurich is a vibrant environment for AI research with the ETH AI Center etc. Please help spread the word!
ethz.ch/en/the-eth-z.... Deadline Nov 30 for full consideration. ETH Zurich is a vibrant environment for AI research with the ETH AI Center etc. Please help spread the word!
Assistant Professor (Tenure Track) of Computer Science – Responsible Artificial Intelligence
ethz.ch