20 citations · 29 across the 12 of their papers we have counts for
4 papers · 1 filter
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…
Better Hessians Matter: Studying the Impact of Curvature Approximations in Influence Functions
Steve Hong, Runa Eschenhagen, Bruno Mlodozeniec +1
Influence functions offer a principled way to trace model predictions back to training data, but their use in deep learning is hampered by the need to invert a large, ill-condition…
Rotary Position Encodings for Graphs
Isaac Reid, Arijit Sehanobish, Cederik Höfs +7
We study the extent to which rotary position encodings (RoPE), a recent transformer position encoding algorithm broadly adopted in large language models (LLMs) and vision transform…
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…