Angela Bitto-Nemling

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Sepp HochreiterInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
Sylvia Frühwirth-SchnatterProfessor of Applied Statistics and Econometrics, WU Vienna University of Economics and BusinessVerified email at wu.ac.at
Vihang PatilInstitute for Machine Learning, JKU, LinzVerified email at ml.jku.at
Markus HofmarcherInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
Peter KnausVisiting Researcher at Department of Statistics, Harvard UniversityVerified email at wu.ac.at
Marius-Constantin DinuPhD Candidate, Institute of Machine Learning, JKU, Linz, ExtensityAI Research ScientistVerified email at ml.jku.at
Sebastian LehnerJKU Linz, Institute for Machine LearningVerified email at ml.jku.at
Günter KlambauerAssoc. Prof., LIT AI Lab & Institute for Machine Learning, Johannes Kepler UniversityVerified email at bioinf.jku.at
Hubert RamsauerResearch Assistant at Institute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
Fei TangHere TechnologiesVerified email at here.com
Elisabeth RumetshoferInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
Andreas FürstJohannes Kepler University Linz, Institute for Machine LearningVerified email at ml.jku.at
Johannes LehnerJKU LinzVerified email at ml.jku.at
Michael WidrichMachine Learning Scientist @ FreenomeVerified email at freenome.com
Johannes BrandstetterJohannes Kepler University (JKU) LinzVerified email at ml.jku.at
Andreas MayrInstitute for Machine Learning, Johannes Kepler University LinzVerified email at ml.jku.at
Philipp RenzJKU LinzVerified email at ml.jku.at
Gregor KastnerProfessor of Statistics, Department of Statistics, University of KlagenfurtVerified email at aau.at
Alexandra PosekanyUniversity of Technology ViennaVerified email at tgm.ac.at
Michael KoppAI researcher

Angela Bitto-Nemling

Institute for Applied Statistics, Johannes Kepler University Linz, Austria

Verified email at jku.at

State Space Models Bayesian Statistics AI Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Achieving shrinkage in a time-varying parameter model framework A Bitto, S Frühwirth-Schnatter Journal of Econometrics 210 (1), 75-97, 2019	188	2019
Cloob: Modern hopfield networks with infoloob outperform clip A Fürst, E Rumetshofer, J Lehner, VT Tran, F Tang, H Ramsauer, D Kreil, ... Advances in neural information processing systems 35, 20450-20468, 2022	85	2022
History compression via language models in reinforcement learning F Paischer, T Adler, V Patil, A Bitto-Nemling, M Holzleitner, S Lehner, ... International Conference on Machine Learning, 17156-17185, 2022	27	2022
Shrinkage in the time-varying parameter model framework using the R package shrinkTVP P Knaus, A Bitto-Nemling, A Cadonna, S Frühwirth-Schnatter Journal of Statistical Software 100 (13), 2021	24	2021
Boundary graph neural networks for 3d simulations A Mayr, S Lehner, A Mayrhofer, C Kloss, S Hochreiter, J Brandstetter Proceedings of the AAAI Conference on Artificial Intelligence 37 (8), 9099-9107, 2023	22	2023
Hopular: Modern hopfield networks for tabular data B Schäfl, L Gruber, A Bitto-Nemling, S Hochreiter arXiv preprint arXiv:2206.00664, 2022	19	2022
Modern hopfield networks for return decomposition for delayed rewards M Widrich, M Hofmarcher, VP Patil, A Bitto-Nemling, S Hochreiter Deep RL Workshop NeurIPS 2021, 2021	18	2021
Understanding the effects of dataset characteristics on offline reinforcement learning K Schweighofer, M Hofmarcher, MC Dinu, P Renz, A Bitto-Nemling, ... arXiv preprint arXiv:2111.04714, 2021	17	2021
A dataset perspective on offline reinforcement learning K Schweighofer, M Dinu, A Radler, M Hofmarcher, VP Patil, ... Conference on Lifelong Learning Agents, 470-517, 2022	7	2022
Reactive exploration to cope with non-stationarity in lifelong reinforcement learning CA Steinparz, T Schmied, F Paischer, MC Dinu, VP Patil, A Bitto-Nemling, ... Conference on Lifelong Learning Agents, 441-469, 2022	7	2022
shrinkTVP: Efficient Bayesian Inference for Time-Varying Parameter Models with Shrinkage P Knaus, A Bitto-Nemling, A Cadonna, S Frühwirth-Schnatter R package version 2 (1), 2020	7	2020
Modern Hopfield networks for sample-efficient return decomposition from demonstrations M Widrich, M Hofmarcher, VP Patil, A Bitto-Nemling, S Hochreiter Offline Reinforcement Learning Workshop NeurIPS, 2021	1	2021
Bayesian Statistics from Methods to Models and Applications: Research from BAYSM 2014 S Frühwirth-Schnatter, A Bitto, G Kastner, A Posekany Springer, 2015	1	2015
Package ‘shrinkTVP’ P Knaus, A Bitto-Nemling, A Cadonna, S Frühwirth-Schnatter, D Winkler, ...		2024
Modern Hopfield Networks as Memory for Iterative Learning on Tabular Data B Schäfl, L Gruber, A Bitto-Nemling, S Hochreiter Associative Memory {\&} Hopfield Networks in 2023, 2023		2023

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