Koeppl, Heinz

37 publications

AAAI 2025 Bounded Rationality Equilibrium Learning in Mean Field Games Yannick Eich, Christian Fabian, Kai Cui, Heinz Koeppl

AISTATS 2025 Entropic Matching for Expectation Propagation of Markov Jump Processes Yannick Eich, Bastian Alt, Heinz Koeppl

ICML 2025 Learning Mean Field Control on Sparse Graphs Christian Fabian, Kai Cui, Heinz Koeppl

TMLR 2025 Mean-Field RL for Large-Scale Unit-Capacity Pickup-and-Delivery Problems Kai Cui, Sharif Azem, Christian Fabian, Kirill Kuroptev, Ramin Khalili, Osama Abboud, Florian Steinke, Heinz Koeppl

NeurIPS 2025 What Do You Know? Bayesian Knowledge Inference for Navigating Agents Matthias Schultheis, Jana-Sophie Schönfeld, Constantin A. Rothkopf, Heinz Koeppl

AISTATS 2024 Approximate Control for Continuous-Time POMDPs Yannick Eich, Bastian Alt, Heinz Koeppl

NeurIPS 2024 Graph Structure Inference with BAM: Neural Dependency Processing via Bilinear Attention Philipp Froehlich, Heinz Koeppl

ICLR 2024 Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behavior Kai Cui, Sascha H. Hauck, Christian Fabian, Heinz Koeppl

AAAI 2024 Learning Discrete-Time Major-Minor Mean Field Games Kai Cui, Gökçe Dayanikli, Mathieu Laurière, Matthieu Geist, Olivier Pietquin, Heinz Koeppl

ICLR 2024 Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach Christian Fabian, Kai Cui, Heinz Koeppl

ICML 2024 Major-Minor Mean Field Multi-Agent Reinforcement Learning Kai Cui, Christian Fabian, Anam Tahir, Heinz Koeppl

IJCAI 2024 Negative-Binomial Randomized Gamma Dynamical Systems for Heterogeneous Overdispersed Count Time Sequences Rui Huang, Sikun Yang, Heinz Koeppl

ICMLW 2024 Partially Observable Multi-Agent Reinforcement Learning Using Mean Field Control Kai Cui, Sascha H. Hauck, Christian Fabian, Heinz Koeppl

AISTATS 2023 Learning Sparse Graphon Mean Field Games Christian Fabian, Kai Cui, Heinz Koeppl

NeurIPS 2023 Probabilistic Inverse Optimal Control for Non-Linear Partially Observable Systems Disentangles Perceptual Uncertainty and Behavioral Costs Dominik Straub, Matthias Schultheis, Heinz Koeppl, Constantin A Rothkopf

ICCVW 2023 The TYC Dataset for Understanding Instance-Level Semantics and Motions of Cells in Microstructures Christoph Reich, Tim Prangemeier, Heinz Koeppl

NeurIPS 2022 Forward-Backward Latent State Inference for Hidden Continuous-Time Semi-Markov Chains Nicolai Engelmann, Heinz Koeppl

ICLR 2022 Learning Graphon Mean Field Games and Approximate Nash Equilibria Kai Cui, Heinz Koeppl

ICML 2022 Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems Lukas Köhs, Bastian Alt, Heinz Koeppl

NeurIPS 2022 Reinforcement Learning with Non-Exponential Discounting Matthias Schultheis, Constantin A Rothkopf, Heinz Koeppl

AISTATS 2021 Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning Kai Cui, Heinz Koeppl

AISTATS 2021 Moment-Based Variational Inference for Stochastic Differential Equations Christian Wildner, Heinz Koeppl

ICML 2021 Active Learning of Continuous-Time Bayesian Networks Through Interventions Dominik Linzner, Heinz Koeppl

NeurIPS 2021 Variational Inference for Continuous-Time Switching Dynamical Systems Lukas Köhs, Bastian Alt, Heinz Koeppl

AAAI 2020 A Variational Perturbative Approach to Planning in Graph-Based Markov Decision Processes Dominik Linzner, Heinz Koeppl

ICML 2020 Continuous Time Bayesian Networks with Clocks Nicolai Engelmann, Dominik Linzner, Heinz Koeppl

NeurIPS 2020 POMDPs in Continuous Time and Discrete Spaces Bastian Alt, Matthias Schultheis, Heinz Koeppl

UAI 2020 The Hawkes Edge Partition Model for Continuous-Time Event-Based Temporal Networks Sikun Yang, Heinz Koeppl

NeurIPS 2019 Correlation Priors for Reinforcement Learning Bastian Alt, Adrian Šošić, Heinz Koeppl

ICML 2019 Moment-Based Variational Inference for Markov Jump Processes Christian Wildner, Heinz Koeppl

NeurIPS 2019 Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data Dominik Linzner, Michael Schmidt, Heinz Koeppl

AAAI 2018 A Poisson Gamma Probabilistic Model for Latent Node-Group Memberships in Dynamic Networks Sikun Yang, Heinz Koeppl

NeurIPS 2018 Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data Dominik Linzner, Heinz Koeppl

ICML 2018 Dependent Relational Gamma Process Models for Longitudinal Networks Sikun Yang, Heinz Koeppl

JMLR 2018 Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling Adrian Šošić, Elmar Rueckert, Jan Peters, Abdelhak M. Zoubir, Heinz Koeppl

JMLR 2016 A Variational Approach to Path Estimation and Parameter Inference of Hidden Diffusion Processes Tobias Sutter, Arnab Ganguly, Heinz Koeppl

AAAI 2016 Marginalized Continuous Time Bayesian Networks for Network Reconstruction from Incomplete Observations Lukas Studer, Loïc Paulevé, Christoph Zechner, Matthias Reumann, María Rodríguez Martínez, Heinz Koeppl