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Dynamical Modeling with Kernels for Nonlinear Time Series Prediction.

, and . NIPS, page 129-136. MIT Press, (2003)

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OKVAR-Boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks., , , and . Bioinform., 29 (11): 1416-1423 (2013)Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels., , , and . CoRR, (2023)Identifying the "right" level of explanation in a given situation., , , , , , , , and . NeHuAI@ECAI, volume 2659 of CEUR Workshop Proceedings, page 63-66. CEUR-WS.org, (2020)Dynamical Modeling with Kernels for Nonlinear Time Series Prediction., and . NIPS, page 129-136. MIT Press, (2003)A Framework to Learn with Interpretation., , and . NeurIPS, page 24273-24285. (2021)Boosting Mixture Models for Semi-supervised Learning., , and . ICANN, volume 2130 of Lecture Notes in Computer Science, page 41-48. Springer, (2001)Exploiting Edge Features in Graph-based Learning with Fused Network Gromov-Wasserstein Distance., , and . Trans. Mach. Learn. Res., (2024)Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels., , , and . AISTATS, volume 238 of Proceedings of Machine Learning Research, page 109-117. PMLR, (2024)Detecting time periods of differential gene expression using Gaussian processes: an application to endothelial cells exposed to radiotherapy dose fraction., , , , , , , , and . Bioinform., 31 (5): 728-735 (2015)Semi-supervised MarginBoost., , and . NIPS, page 553-560. MIT Press, (2001)