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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
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Portfolio item number 2
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publications
Structure-sensitive Testimonial Norms
Listening to scientific rockstars is not good for science.
Höltgen, B.: "Structure-sensitive testimonial norms." European Journal for Philosophy of Science 11:80. 2021.
Encoding Causal Macrovariables
An algorithm for causal representation learning and a reflection on causal variables. My first master thesis.
Höltgen, B.: "Encoding causal macrovariables." NeurIPS Workshop: Causal Inference & Machine Learning: Why now?. 2021.
DeDUCE: Generating Counterfactual Explanations Efficiently
An algorithm for generating counterfactual explanations. My second master thesis.
Höltgen, B., Schut, L., Brauner, J., Gal, Y.: "DeDUCE: Generating counterfactual explanations efficiently." NeurIPS Workshop: eXplainable AI approaches for debugging and diagnosis. 2021.
Moral Discourse Boosts Confidence in Moral Judgments
The title says it all.
Heinzelmann, N., Höltgen, B., and Tran, V.: "Moral discourse boosts confidence in moral judgments." Philosophical Psychology 4:8, 1192-1216. 2021.
Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learned
An online batch selection algorithm for time-efficient training of large ML models.
Mindermann, S., Brauner, J., Razzak, M., Sharma, M., Kirsch, W., Xu, W., Höltgen, B., Gomez, A.N., Morisot, A., Farquhar, S., Gal, Y.: "Prioritized training on points that are learnable, worth learning, and not yet learned." ICML. 2022.
On the Richness of Calibration
Exploring the concept of calibration and different ways of measuring it.
Höltgen, B., Williamson, R.C.: "On the richness of calibration." ACM FAccT. 2023.
Causal Modelling Without Introducing Counterfactuals or Abstract Distributions
A different way of modelling causal inference.
Höltgen, B., Williamson, R.C.: "Causal modelling without introducing counterfactuals or abstract distributions." ICML Workshop: Humans, Algorithmic Decision-Making and Society. 2024.
Which Distribution Were You Sampled From? Towards a More Tangible Conception of Data
We should model ML without true distributions, at least in social settings.
Höltgen, B., Williamson, R.C.: "Which distribution were you sampled from? Towards a more tangible conception of data." ICML Workshop: Humans, Algorithmic Decision-Making and Society. 2024.
Practical Foundations for Probability: Prediction Methods and Calibration
How to understand probability.
Höltgen, B.: "Practical foundations for probability: Prediction methods and calibration." PhilPapers preprint. 2024.
Reconsidering Fairness Through Unawareness From the Perspective of Model Multiplicity
Using protected attributes can increase disparate impact without increasing accuracy.
Höltgen, B., Oliver, N.: "Reconsidering fairness through unawareness from the perspective of model multiplicity." ArXiv preprint. 2025.
Position: The Categorization of Race in ML is a Flawed Premise
ML research oversimplifies race and we need to learn how to move beyond categories.
Doh, M., Höltgen, B., Riccio, P., Oliver, N.: "Position: The categorization of race in ML is a flawed premise." ICML. 2025.
talks
On the richness of calibration
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Probability and machine learning
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Machine learning without true probabilities
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.