Heinz Koeppl

CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval ACL 2026 Bounded Rationality Equilibrium Learning in Mean Field Games AAAI 2025 Entropic Matching for Expectation Propagation of Markov Jump Processes AISTATS 2025 Learning Mean Field Control on Sparse Graphs ICML 2025 Approximate Control for Continuous-Time POMDPs AISTATS 2024 A Survey of Confidence Estimation and Calibration in Large Language Models NAACL 2024 GeoHard: Towards Measuring Class-wise Hardness through Modelling Class Semantics ACL 2024 Graph Structure Inference with BAM: Neural Dependency Processing via Bilinear Attention NeurIPS 2024 Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behavior ICLR 2024 Learning Discrete-Time Major-Minor Mean Field Games AAAI 2024 Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach ICLR 2024 Major-Minor Mean Field Multi-Agent Reinforcement Learning ICML 2024 MixGR: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity EMNLP 2024 Negative-Binomial Randomized Gamma Dynamical Systems for Heterogeneous Overdispersed Count Time Sequences IJCAI 2024 ECOLA: Enhancing Temporal Knowledge Embeddings with Contextualized Language Representations ACL 2023 Learning Sparse Graphon Mean Field Games AISTATS 2023 Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs NeurIPS 2023 Forward-Backward Latent State Inference for Hidden Continuous-Time semi-Markov Chains NeurIPS 2022 Learning Graphon Mean Field Games and Approximate Nash Equilibria ICLR 2022 Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems ICML 2022 Reinforcement Learning with Non-Exponential Discounting NeurIPS 2022 Active Learning of Continuous-time Bayesian Networks through Interventions ICML 2021 Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning AISTATS 2021 Moment-Based Variational Inference for Stochastic Differential Equations AISTATS 2021 Variational Inference for Continuous-Time Switching Dynamical Systems NeurIPS 2021 A Variational Perturbative Approach to Planning in Graph-Based Markov Decision Processes AAAI 2020 Continuous Time Bayesian Networks with Clocks ICML 2020 POMDPs in Continuous Time and Discrete Spaces NeurIPS 2020 The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks UAI 2020 Correlation Priors for Reinforcement Learning NeurIPS 2019 Moment-Based Variational Inference for Markov Jump Processes ICML 2019 Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data NeurIPS 2019 Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data NeurIPS 2018 Dependent Relational Gamma Process Models for Longitudinal Networks ICML 2018 Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling JMLR 2018 A Variational Approach to Path Estimation and Parameter Inference of Hidden Diffusion Processes JMLR 2016