| Laplace Redux--Effortless Bayesian Deep Learning E Daxberger*, A Kristiadi*, A Immer*, R Eschenhagen*, M Bauer, ... NeurIPS 2021, 2021 | 519 | 2021 |
| Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks A Kristiadi, M Hein, P Hennig ICML 2020, 2020 | 429 | 2020 |
| Incorporating literals into knowledge graph embeddings A Kristiadi*, MA Khan*, D Lukovnikov, J Lehmann, A Fischer ISWC 2019, 2019 | 157 | 2019 |
| Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI T Papamarkou, M Skoularidou, K Palla, L Aitchison, J Arbel, D Dunson, ... ICML 2024, 2024 | 101* | 2024 |
| Learnable Uncertainty under Laplace Approximations A Kristiadi, M Hein, P Hennig UAI 2021, 2021 | 59 | 2021 |
| Fast predictive uncertainty for classification with Bayesian deep networks M Hobbhahn, A Kristiadi, P Hennig UAI 2022, 2022 | 51 | 2022 |
| A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules? A Kristiadi, F Strieth-Kalthoff, M Skreta, P Poupart, A Aspuru-Guzik, ... ICML 2024, 2024 | 49 | 2024 |
| Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning R Eschenhagen, E Daxberger, P Hennig, A Kristiadi Bayesian Deep Learning Workshop, NeurIPS 2021, 2021 | 41 | 2021 |
| Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge D Chaudhuri, A Kristiadi, J Lehmann, A Fischer CoNLL 2018, 2018 | 37 | 2018 |
| Predictive uncertainty quantification with compound density networks A Kristiadi, S Däubener, A Fischer Bayesian Deep Learning Workshop, NeurIPS 2019, 2019 | 25 | 2019 |
| Being a Bit Frequentist Improves Bayesian Neural Networks A Kristiadi, M Hein, P Hennig AISTATS 2022, 2022 | 22 | 2022 |
| Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks A Kristiadi, R Eschenhagen, P Hennig NeurIPS 2022, 2022 | 20 | 2022 |
| An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence A Kristiadi, M Hein, P Hennig NeurIPS 2021, 2021 | 19* | 2021 |
| Deep Convolutional Level Set Method for Image Segmentation. A Kristiadi Journal of ICT Research & Applications 11 (3), 2017 | 17 | 2017 |
| Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization A Kristiadi, A Immer, R Eschenhagen, V Fortuin AABI 2023, 2023 | 16 | 2023 |
| The Geometry of Neural Nets' Parameter Spaces Under Reparametrization A Kristiadi, F Dangel, P Hennig NeurIPS 2023, 2023 | 15 | 2023 |
| Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets W Lin, F Dangel, R Eschenhagen, K Neklyudov, A Kristiadi, RE Turner, ... ICML 2024, 2024 | 13* | 2024 |
| On the disconnect between theory and practice of neural networks: Limits of the NTK perspective J Wenger, F Dangel, A Kristiadi arXiv preprint arXiv:2310.00137, 2023 | 12* | 2023 |
| Uncertainty-Guided Optimization on Large Language Model Search Trees J Grosse, R Wu, A Rashid, P Hennig, P Poupart, A Kristiadi AABI 2024, 2024 | 6 | 2024 |
| Parallel particle swarm optimization for image segmentation A Kristiadi, P Mudjihartono Universitas Atma Jaya Yogyakarta, 2013 | 6 | 2013 |
Philipp HennigUniversity of Tübingen, Tübingen AI CenterVerified email at uni-tuebingen.de
