12 citations
- F. Bach2 profiles21 · h 108
- Alessandro Rudi3 profiles9 · h 27
- Francis R. Bach4 profiles6 · h 7
- Francis Bach3 profiles5 · h 3
- P. Gaillard5 · h 25
- Adrien B. Taylor2 profiles3 · h 24
- Eliot Beyler3 · h 2
- Lawrence Stewart3 · h 6
- Pierre Gaillard2 profiles3 · h 2
- Blake E. Woodworth2 · h 21
- Claire Boyer2 · h 6
- Gérard Biau2 · h 6
- Département d'InformatiqueFR10 papers
- École Normale Supérieure - PSLFR5 papers
- Institut national de recherche en sciences et technologies du numériqueFR4 papers
- CERMICSFR2 papers
- International University of Fundamental StudiesRU2 papers
- Amazon (United States)US1 paper
- Bocconi UniversityIT1 paper
- Carnegie Mellon UniversityUS1 paper
- Centre de Robotique1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
- Centrum Wiskunde & InformaticaNL1 paper
- College of Western IdahoUS1 paper
4 papers · 1 filter
The N-Grammys: Accelerating Autoregressive Inference with Learning-Free Batched Speculation
Lawrence Stewart, Matthew Trager, Sujan Kumar Gonugondla +1
Speculative decoding aims to speed up autoregressive generation of a language model by verifying in parallel the tokens generated by a smaller draft model.In this work, we explore…
Variational Inference on the Boolean Hypercube with the Quantum Entropy
Eliot Beyler, Francis Bach
In this paper, we derive variational inference upper-bounds on the log-partition function of pairwise Markov random fields on the Boolean hypercube, based on quantum relaxations of…
Variational Dynamic Programming for Stochastic Optimal Control
Marc Lambert, Francis Bach, Silvère Bonnabel
We consider the problem of stochastic optimal control, where the state-feedback control policies take the form of a probability distribution and where a penalty on the entropy is a…
Physics-informed machine learning as a kernel method
Nathan Doumèche, Francis Bach, Gérard Biau +1
Physics-informed machine learning combines the expressiveness of data-based approaches with the interpretability of physical models. In this context, we consider a general regressi…