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💬 About
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).
📝 Research
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.
🗞️ Selected Publications
For full list and metrics, see Google Scholar.
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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
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Reconstruction for Powerful Graph Representations L Cotta, C Morris, B Ribeiro NeurIPS, 2021
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Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models L Cotta, CHC Teixeira, A Swami, B Ribeiro, 2020 NeurIPS, 2020
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Graph Pattern Mining and Learning through User-defined Relations CHC Teixeira, L Cotta, B Ribeiro, W Meira Jr ICDM, 2018
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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
