andres masegosa

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  1. PAC-Chernoff Bounds. Understanding Generalization in the Interpolation Regime

    Talk at ECAI 2025, the European Conference on Artificial Intelligence in Bologna (Italy).

  2.

    Towards Near-Optimal Regularization in Deep Learning

    Contribution at Workshop Machine Learning Theory at D3A 2025 Danish Digitalization, Data Science and AI 3.0 in Nyborg (Denmark).

  3.

    Introduction to Probabilistic Machine Learning

    Lecturing at Summer School on Methods for Statistical Evaluation of AI, co-located with D3A 2025 Danish Digitalization, Data Science and AI 3.0 in Nyborg (Denmark).

  4.

    Variational Inference and Optimization

    Full day lecture at ProbAI 2025 Nordic Probabilistic AI School in Trondheim (Norway).

  5.

    Introduction to Probabilistic Machine Learning

    Lecturing a PhD Course at Aalborg Univeristy (Denmark).

  6.

    PAC-Chernoff Bounds. Understanding Generalization in the Interpolation Regime

    Contribution at Workshop on Bridging the Gap Between Practice and Theory in Deep Learning ICLR 2024 in Vienna (Austria).

  7.

    A Generalisation Analysis of Bayesian Machine Learning

    Invited talk at Workshop of AI Research Moonshots in Copenhagen (Denmark).

  8.

    Variational Inference and Optimization

    Full day lecture at ProbAI 2023 Nordic Probabilistic AI School in Trondheim (Norway).

  9.

    Bayesian Priors and Generalization in Probabilistic Machine Learning

  10.

    On How PAC-Bayesian Bounds Help to Better Understand (and Improve) Bayesian Machine Learning

    Invited talk at the international worksop The Mathematics of Machine Learning in Bilbao (Spain).

  11.

    Variational Inference and Optimization

    Full day lecture at ProbAI 2022 Nordic Probabilistic AI School in Helsinki (Findland).

  12.

    Diversity and Generalization in Neural Network Ensembles

    Talk at the ML Section of the Department of Computer Science at the University of Copenhaguen.

  13.

    Bayesian Model Averaging is not Model Combination. A PAC-Bayesian Analysis of Deep Ensembles

    Invited Talk at NeurIPS 2021 Workshop Your Model is Wrong. Robustness and misspecification in probabilistic modeling.

  14.

    Variational Inference and Optimization

    Full day lecture at ProbAI 2021 Nordic Probabilistic AI School in Trondheim (Norway)

  15.

    Learning Under Model Misspecification. Applications to Variational and Ensemble Methods

    Invited Talk at the Artificial Intelligence Research Group at University of Cambridge and at the Alan Turing Institute (London).

  16.

    Bayesian model averaging is suboptimal for generalization under model misspecification

    Contributed Talk at ICML2020 Workshop Uncertainty and Robustness in Deep Learning Workshop (UDL). Video Recording.

  17.

    Variational Inference and Probabilistic Programming

    Tutorial at the Probabilistic AI 2019 in Trondheim (Norway). Full material at the Github repository.

  18.

    A Bayesian approach for modelling non-stationary data streams.

    Talk at the ML Section of the Department of Computer Science at the University of Copenhaguen.

  19.

    Probabilistic Programming with Deep Neural Networks

    Tutorial at the Autumn school on Machine Learning 2019 in Tbilisi (Georgia). Full material at the Github repository.

  20.

    Bayesian Modelling of Concept Drift

    Talk at ICML 2017 in Sydney (Australia) and the Machine Learning Group in the Technical University of Berlin (Germany).

  21.

    Probabilistic Machine Learning with the AMIDST Toolbox

    Tutorial at the Geilo Winter School 2018 (Norway). Full material at the Github repository.

  22.

    AMIDST Toolbox - Scalable Probabilistic Machine Leanring

    Talk at the Apache Big Data Europe 2016 in Seville (Spain), DeustoTech Mobility group of the University of Deusto (Bilbao, Spain) and Machine Learning Group in the Technical University of Berlin (Germany).

  23.

    Stochastic Discriminative EM - Discriminative Learning of Latent Variable Models

    Talk at UAI 2014 in Quebec (Canada) and the Department of Computer Science in the University of Aalborg (Denmark).

  24.

    Interactive Learning of Bayesian Networks

    Talk at the Department of Information and Computing Sciences in the University of Utrecht (Netherlands) and the Department of Computer Science in the University of Aalborg (Denmark).