| Intensity-Free Learning of Temporal Point Processes O Shchur, M Biloš, S Günnemann International Conference on Learning Representations, 2020 | 201 | 2020 |
| Lag-llama: Towards foundation models for time series forecasting K Rasul, A Ashok, AR Williams, A Khorasani, G Adamopoulos, ... R0-FoMo: Robustness of Few-shot and Zero-shot Learning in Large Foundation …, 2023 | 119 | 2023 |
| Neural flows: Efficient alternative to neural ODEs M Biloš, J Sommer, SS Rangapuram, T Januschowski, S Günnemann Advances in neural information processing systems 34, 21325-21337, 2021 | 84 | 2021 |
| Lag-llama: Towards foundation models for probabilistic time series forecasting K Rasul, A Ashok, AR Williams, H Ghonia, R Bhagwatkar, A Khorasani, ... arXiv preprint arXiv:2310.08278, 2023 | 60 | 2023 |
| Modeling temporal data as continuous functions with stochastic process diffusion M Biloš, K Rasul, A Schneider, Y Nevmyvaka, S Günnemann International Conference on Machine Learning, 2452-2470, 2023 | 57* | 2023 |
| Uncertainty on asynchronous time event prediction M Biloš, B Charpentier, S Günnemann Neural Information Processing Systems, 2019 | 48 | 2019 |
| Fast and flexible temporal point processes with triangular maps O Shchur, N Gao, M Biloš, S Günnemann Neural Information Processing Systems, 2020 | 41 | 2020 |
| Scalable Normalizing Flows for Permutation Invariant Densities M Biloš, S Günnemann International Conference on Machine Learning, 2021 | 33* | 2021 |
| Deep representation learning and clustering of traffic scenarios N Harmening, M Biloš, S Günnemann arXiv preprint arXiv:2007.07740, 2020 | 18 | 2020 |
| Add and thin: Diffusion for temporal point processes D Lüdke, M Biloš, O Shchur, M Lienen, S Günnemann Advances in Neural Information Processing Systems 36, 56784-56801, 2023 | 13 | 2023 |
| Variational schr\" odinger diffusion models W Deng, W Luo, Y Tan, M Biloš, Y Chen, Y Nevmyvaka, RTQ Chen arXiv preprint arXiv:2405.04795, 2024 | 9 | 2024 |
| Intensity-free learning of temporal point processes.(2019) O Shchur, M Biloš, S Günnemann ICLR, 2019 | 5 | 2019 |
| Irregularly-sampled time series modeling with spline networks M Biloš, E Ramneantu, S Günnemann arXiv preprint arXiv:2210.10630, 2022 | 4 | 2022 |
| Towards linking social media profiles with user’s WiFi preferred network list A Dagelić, M Čagalj, T Perković, M Biloš Ad Hoc Networks 107, 102244, 2020 | 4 | 2020 |
| Recurrent Interpolants for Probabilistic Time Series Prediction Y Chen, M Biloš, S Mittal, W Deng, K Rasul, A Schneider arXiv preprint arXiv:2409.11684, 2024 | 1 | 2024 |
| Machine Learning for Irregularly-Sampled Time Series M Biloš Technische Universität München, 2024 | | 2024 |
