A computable figure of merit for quasi-Monte Carlo point sets. - Makoto Matsumoto; Mutsuo Saito; Kyle Matoba (Math. Comput., 2014)

Challenges for Using Impact Regularizers to Avoid Negative Side Effects. - David Lindner; Kyle Matoba; Alexander Meulemans (SafeAI@AAAI, 2021)

Efficient Training of Low-Curvature Neural Networks. - Suraj Srinivas; Kyle Matoba; Himabindu Lakkaraju; François Fleuret (NeurIPS, 2022)

The Theoretical Expressiveness of Maxpooling. - Kyle Matoba; Nikolaos Dimitriadis; François Fleuret (CoRR, 2022)

Flatten the Curve: Efficiently Training Low-Curvature Neural Networks. - Suraj Srinivas; Kyle Matoba; Himabindu Lakkaraju; François Fleuret (CoRR, 2022)

Accurate Extrinsic Prediction of Physical Systems Using Transformers. - Arnaud Pannatier; Kyle Matoba; François Fleuret (CoRR, 2022)

MEDITRON-70B: Scaling Medical Pretraining for Large Language Models. - Zeming Chen 001; Alejandro Hernández-Cano; Angelika Romanou; Antoine Bonnet; Kyle Matoba; Francesco Salvi; Matteo Pagliardini; Simin Fan; Andreas Köpf; Amirkeivan Mohtashami; Alexandre Sallinen; Alireza Sakhaeirad; et al. (CoRR, 2023)

Benefits of Max Pooling in Neural Networks: Theoretical and Experimental Evidence. - Kyle Matoba; Nikolaos Dimitriadis; François Fleuret (Trans. Mach. Learn. Res., 2023)

Inference from Real-World Sparse Measurements. - Arnaud Pannatier; Kyle Matoba; François Fleuret (Trans. Mach. Learn. Res., 2024)