3 papers
stat.ML2025
Probabilistic Modelling is Sufficient for Causal Inference
Bruno Mlodozeniec, David Krueger, Richard E. Turner
Causal inference is a key research area in machine learning, yet confusion reigns over the tools needed to tackle it. There are prevalent claims in the machine learning literature…
cs.LG2025
Fresh in memory: Training-order recency is linearly encoded in language model activations
Dmitrii Krasheninnikov, Richard E. Turner, David Krueger
We show that language models' activations linearly encode when information was learned during training. Our setup involves creating a model with a known training order by sequentia…
cs.LG2025
Distributional Training Data Attribution: What do Influence Functions Sample?
Bruno Mlodozeniec, Isaac Reid, Sam Power +4
Randomness is an unavoidable part of training deep learning models, yet something that traditional training data attribution algorithms fail to rigorously account for. They ignore…