Portrait de Patrik Reizinger

Patrik Reizinger

prmedia17@gmail.com
Postdoctorat - Univeristy of Tübingen
Superviseur⋅e principal⋅e
Simon Lacoste-Julien
Co-supervisor
Dhanya Sridhar
Sujets de recherche
Apprentissage de représentations
Apprentissage profond
Causalité
Généralisation hors distribution (OOD)
Site web
Google Scholar
GitHub

Publications

Who Guards the Guardians? The Challenges of Evaluating Identifiability of Learned Representations
Shruti Joshi
Theo Saulus
Wieland Brendel
Philippe Brouillard
Dhanya Sridhar
Patrik Reizinger
Identifiability in representation learning is commonly evaluated using standard metrics (e.g., MCC, DCI, R^2) on synthetic benchmarks with k… (voir plus)nown ground-truth factors. These metrics are assumed to reflect recovery up to the equivalence class guaranteed by identifiability theory. We show that this assumption holds only under specific structural conditions: each metric implicitly encodes assumptions about both the data-generating process (DGP) and the encoder. When these assumptions are violated, metrics become misspecified and can produce systematic false positives and false negatives. Such failures occur both within classical identifiability regimes and in post-hoc settings where identifiability is most needed. We introduce a taxonomy separating DGP assumptions from encoder geometry, use it to characterise the validity domains of existing metrics, and release an evaluation suite for reproducible stress testing and comparison.
2026-05-30
Conference on Uncertainty in Artificial Intelligence (poster)
doi.org
proceedings.mlr.press
Causality is Key for Interpretability Claims to Generalise
Shruti Joshi
Aaron Mueller
David Klindt
Wieland Brendel
Patrik Reizinger
Dhanya Sridhar
Interpretability research on large language models (LLMs) has yielded important insights into model behaviour, yet recurring pitfalls persis… (voir plus)t: findings that do not generalise, and causal interpretations that outrun the evidence. Our position is that causal inference specifies what constitutes a valid mapping from model activations to invariant high-level structures, the data or assumptions needed to achieve it, and the inferences it can support. Specifically, Pearl's causal hierarchy clarifies what an interpretability study can justify. Observations establish associations between model behaviour and internal components. Interventions (e.g., ablations or activation patching) support claims how these edits affect a behavioural metric (e.g., average change in token probabilities) over a set of prompts. However, counterfactual claims -- i.e., asking what the model output would have been for the same prompt under an unobserved intervention -- remain largely unverifiable without controlled supervision. We show how causal representation learning (CRL) operationalises this hierarchy, specifying which variables are recoverable from activations and under what assumptions. Together, these motivate a diagnostic framework that helps practitioners select methods and evaluations matching claims to evidence such that findings generalise.
2026-02-17
arXiv (prépublication)
doi.org
arxiv.org
Position: Causality is Key for Interpretability Claims to Generalise
Shruti Joshi
Aaron Mueller
David Klindt
Wieland Brendel
Dhanya Sridhar
Patrik Reizinger
Interpretability research on large language models (LLMs) has produced methods that align model components to high-level concepts, yet their… (voir plus) use has been accompanied by recurring failures: findings that do not generalise, and causal language that outruns the evidence. Our position is that Pearl’s causal hierarchy formally defines what constitutes a good alignment, what data or assumptions it requires, and what inferences it supports. Specifically, observations of model behaviour support only associational claims; interventions enable cause-effect claims, but not necessarily predictions of model behaviour; counterfactuals, or predictions of behaviour on unseen examples, are often unverifiable in current studies. We show how interpretability research can benefit from causal representation learning (CRL), which provides tools for provably extracting semantic variables and their relationships from activations, and outline practical requirements for generalisable insights: robustness to distribution shifts, sensitivity to assumptions, and compositionality of interventions. Our diagnostic framework helps practitioners select appropriate methods and mitigate failures to ensure that claims match evidence and findings generalise.
2025-12-31
International Conference on Machine Learning (Accept (regular))
openreview.net
openreview.net