Agustinus Kristiadi

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Philipp HennigUniversity of TübingenVerified email at uni-tuebingen.de
Runa EschenhagenPhD student, University of CambridgeVerified email at cam.ac.uk
Matthias HeinProfessor of Computer Science, University of TübingenVerified email at uni-tuebingen.de
Alexander ImmerPhD student, ETH Zürich, Max Planck Institute for Intelligent SystemsVerified email at inf.ethz.ch
Asja FischerProfessor for Machine Learning, Ruhr University BochumVerified email at ini.rub.de
Erik DaxbergerAppleVerified email at apple.com
Vincent FortuinPrincipal Investigator, Helmholtz AI & TU MunichVerified email at tum.de
Jens LehmannPrincipal Scientist at Amazon Inc., Honorary Professor at TU DresdenVerified email at amazon.com
Pascal PoupartUniversity of WaterlooVerified email at uwaterloo.ca
Felix DangelPostdoc at the Vector Institute, TorontoVerified email at vectorinstitute.ai
Felix Strieth-KalthoffAssistant Professor, University of WuppertalVerified email at uni-wuppertal.de
Alan Aspuru-GuzikProfessor of Chemistry and Computer Science, University of Toronto (starting July 1, 2018)Verified email at utoronto.ca
Geoff PleissAssistant Professor, University of British ColumbiaVerified email at stat.ubc.ca
Alireza MakhzaniResearch Scientist, Google DeepMindVerified email at google.com
Jonathan WengerPostdoc, Columbia UniversityVerified email at columbia.edu

Agustinus Kristiadi

Postdoc, Vector Institute

Verified email at vectorinstitute.ai - Homepage

Probabilistic Inference Decision-making Foundation Models AI4Science Riemmanian Geometry


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks A Kristiadi, M Hein, P Hennig ICML 2020, 2020	346	2020
Laplace Redux--Effortless Bayesian Deep Learning E Daxberger, A Kristiadi, A Immer, R Eschenhagen, M Bauer, ... NeurIPS 2021, 2021	330	2021
Incorporating literals into knowledge graph embeddings A Kristiadi, MA Khan, D Lukovnikov, J Lehmann, A Fischer ISWC 2019, 2019	132	2019
Learnable Uncertainty under Laplace Approximations A Kristiadi, M Hein, P Hennig UAI 2021, 2021	42	2021
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	40*	2024
Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge D Chaudhuri, A Kristiadi, J Lehmann, A Fischer CoNLL 2018, 2018	35	2018
Fast predictive uncertainty for classification with Bayesian deep networks M Hobbhahn, A Kristiadi, P Hennig UAI 2022, 2022	33	2022
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	25	2021
Predictive uncertainty quantification with compound density networks A Kristiadi, S Däubener, A Fischer Bayesian Deep Learning Workshop, NeurIPS 2019, 2019	20	2019
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence A Kristiadi, M Hein, P Hennig NeurIPS 2021, 2021	17*	2021
Deep Convolutional Level Set Method for Image Segmentation. A Kristiadi Journal of ICT Research & Applications 11 (3), 2017	16	2017
Being a Bit Frequentist Improves Bayesian Neural Networks A Kristiadi, M Hein, P Hennig AISTATS 2022, 2022	15	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	14	2024
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks A Kristiadi, R Eschenhagen, P Hennig NeurIPS 2022, 2022	12	2022
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	8*	2024
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization A Kristiadi, A Immer, R Eschenhagen, V Fortuin AABI 2023, 2023	8	2023
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization A Kristiadi, F Dangel, P Hennig NeurIPS 2023, 2023	5	2023
On the disconnect between theory and practice of overparametrized neural networks J Wenger, F Dangel, A Kristiadi arXiv preprint arXiv:2310.00137, 2023	5	2023
Parallel particle swarm optimization for image segmentation A Kristiadi, P Mudjihartono Universitas Atma Jaya Yogyakarta, 2013	4	2013
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference L Rendsburg, A Kristiadi, P Hennig, U von Luxburg AISTATS 2022, 2022	3	2022

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