My research interests lie mainly at the interface between algorithms, complexity theory and machine learning.
I am organizing the Formal Methods team seminar. Do not hesitate to write to me if you want to give a talk in the seminar.
Prior to that, I was a postdoc in the Approximation and Proof Complexity group at KTH Royal Institute of Technology, hosted by Jakob Nordström, Johan Håstad and Per Austrin.
I did my PhD in IRIF (University Paris Diderot), under the supervision of Sophie Laplante and Sylvain Perifel. You can find the manuscript of my thesis here: Contributions to Arithmetic Complexity and Compression.
email: (x, y $\mapsto$ guillaume.lagarde x labri y fr) ([at], [dot])
- Github for fast-ultrametrics, hierarchical clustering based on our ICML paper. The purpose of the algorithm is to beat the quadratic time/space required by the linkage algorithms, which makes them impractical to handle big datasets.
- AlgoDist seminar, On Efficient Low Distortion Ultrametric Embedding, LaBRI, Bordeaux
- WEPA 2020, Distribution-based Search: the case of Probabilistic Context-Free Grammars with Applications to Programming by Example, with Nathanaël Fijalkow and Pierre Ohlmann
- ECAI 2020, Tutorial on Machine Learning Guided Program Synthesis, with Nathanaël Fijalkow,
in Santiago de Compostela, Spainonline conference
- On Efficient Low Distortion Ultrametric Embedding is accepted at ICML’2020. Joint work with Vincent Cohen-Addad and Karthik C. S.