Lagergren, Jens

12 publications

AISTATS 2024 Benefits of Non-Linear Scale Parameterizations in Black Box Variational Inference Through Smoothness Results and Gradient Variance Bounds Alexandra Maria Hotti, Lennart Alexander Goten, Jens Lagergren

ICML 2024 Efficient Mixture Learning in Black-Box Variational Inference Alexandra Hotti, Oskar Kviman, Ricky Molén, Vı́ctor Elvira, Jens Lagergren

TMLR 2024 Improved Variational Bayesian Phylogenetic Inference Using Mixtures Ricky Molén, Oskar Kviman, Jens Lagergren

ICML 2024 Indirectly Parameterized Concrete Autoencoders Alfred Nilsson, Klas Wijk, Sai Bharath Chandra Gutha, Erik Englesson, Alexandra Hotti, Carlo Saccardi, Oskar Kviman, Jens Lagergren, Ricardo Vinuesa Motilva, Hossein Azizpour

TMLR 2024 The Klarna Product Page Dataset: Web Element Nomination with Graph Neural Networks and Large Language Models Alexandra Hotti, Riccardo Sven Risuleo, Stefan Magureanu, Aref Moradi, Jens Lagergren

AISTATS 2024 Variational Resampling Oskar Kviman, Nicola Branchini, Víctor Elvira, Jens Lagergren

ICML 2023 Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders Oskar Kviman, Ricky Molén, Alexandra Hotti, Semih Kurt, Vı́ctor Elvira, Jens Lagergren

AISTATS 2022 Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximations Oskar Kviman, Harald Melin, Hazal Koptagel, Victor Elvira, Jens Lagergren

NeurIPS 2022 VaiPhy: A Variational Inference Based Algorithm for Phylogeny Hazal Koptagel, Oskar Kviman, Harald Melin, Negar Safinianaini, Jens Lagergren

ECML-PKDD 2020 Orthogonal Mixture of Hidden Markov Models Negar Safinianaini, Camila P. E. de Souza, Henrik Boström, Jens Lagergren

AISTATS 2014 Learning Bounded Tree-Width Bayesian Networks Using Integer Linear Programming Pekka Parviainen, Hossein Shahrabi Farahani, Jens Lagergren

NeurIPS 2011 A Global Structural EM Algorithm for a Model of Cancer Progression Ali Tofigh, Erik Sj̦lund, Mattias H̦glund, Jens Lagergren