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CausalProfiler: Generating Synthetic Benchmarks for Rigorous and Transparent Evaluation of Causal Machine Learning
Panayiotis Panayiotou, Audrey Poinsot, Alessandro Leite +3
Causal machine learning (Causal ML) aims to answer "what if" questions using machine learning algorithms, making it a promising tool for high-stakes decision-making. Yet, empirical…
Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption
Audrey Poinsot, Panayiotis Panayiotou, Alessandro Leite +3
Causal machine learning has the potential to revolutionize decision-making by combining the predictive power of machine learning algorithms with the theory of causal inference. How…
Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training
Ismail Labiad, Mathurin Videau, Matthieu Kowalski +4
Gradient-based optimization is the workhorse of deep learning, offering efficient and scalable training via backpropagation. However, exposing gradients during training can leak se…
Evolutionary Retrofitting
Mathurin Videau, Mariia Zameshina, Alessandro Leite +3
AfterLearnER (After Learning Evolutionary Retrofitting) consists in applying evolutionary optimization to refine fully trained machine learning models by optimizing a set of carefu…