Post example
Discover more about this site template.
I’m a postdoc fellow at Vector Institute, where I’m hosted by Chris J. Maddison. I received my PhD degree in computer science at Purdue University, where I was advised by Bruno Ribeiro. Before that —in a distant and happy land— I was a BSc student (also in CS) at UFMG, Brazil. During my time as an undergrad I worked with distributed algorithms (at UFMG) and quantum computing theory (at University of Calgary).
I’m broadly interested in statistical and causal machine learning. More specifically, I study the interplay between structural knowledge, computation, and learning. How can we leverage these concepts to build better (and practical) machine learning methodology? I believe these insights are specially relevant in problems with complex systems’ data, such as online platforms and biochemistry.
For full list and metrics, see Google Scholar.
Probabilistic Invariant Learning with Randomized Linear Classifiers
L Cotta, G Yehuda, A Schuster, CJ Maddison
NeurIPS, 2023
Causal Lifting and Link Prediction
L Cotta, B Bevilacqua, N Ahmed, B Ribeiro Proceedings of the Royal Society A, 2023
Reconstruction for Powerful Graph Representations L Cotta, C Morris, B Ribeiro NeurIPS, 2021
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models L Cotta, CHC Teixeira, A Swami, B Ribeiro, 2020 NeurIPS, 2020
Graph Pattern Mining and Learning through User-defined Relations CHC Teixeira, L Cotta, B Ribeiro, W Meira Jr ICDM, 2018
AoT: Authentication and Access Control for the Entire IoT Device Life-Cycle AL Maia Neto, ALF Souza, Í Cunha, M Nogueira, IO Nunes, L Cotta, N Gentille, AAF Loureiro, DF Aranha, HK Patil, LB Oliveira ACM SenSys, 2016
Discover more about this site template.