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
Jens LehmannPrincipal Scientist at Amazon Inc., Honorary Professor at TU DresdenVerified email at amazon.com
Vincent FortuinPrincipal Investigator, Helmholtz AI & TU MunichVerified email at tum.de
Felix DangelPostdoc at the Vector Institute, TorontoVerified email at vectorinstitute.ai
Pascal PoupartUniversity of WaterlooVerified email at uwaterloo.ca
Felix Strieth-KalthoffAssistant Professor, University of WuppertalVerified email at uni-wuppertal.de
Geoff PleissAssistant Professor, University of British ColumbiaVerified email at stat.ubc.ca
Alireza MakhzaniResearch Scientist, Google DeepMindVerified email at google.com
Alan Aspuru-GuzikProfessor of Chemistry and Computer Science, University of Toronto (starting July 1, 2018)Verified email at utoronto.ca
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	310	2020
Laplace Redux--Effortless Bayesian Deep Learning E Daxberger, A Kristiadi, A Immer, R Eschenhagen, M Bauer, ... NeurIPS 2021, 2021	274	2021
Incorporating literals into knowledge graph embeddings A Kristiadi, MA Khan, D Lukovnikov, J Lehmann, A Fischer ISWC 2019, 2019	122	2019
Learnable Uncertainty under Laplace Approximations A Kristiadi, M Hein, P Hennig UAI 2021, 2021	35	2021
Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge D Chaudhuri, A Kristiadi, J Lehmann, A Fischer CoNLL 2018, 2018	33	2018
Fast predictive uncertainty for classification with Bayesian deep networks M Hobbhahn, A Kristiadi, P Hennig UAI 2022, 2022	28	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	22	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	20*	2024
Predictive uncertainty quantification with compound density networks A Kristiadi, S Däubener, A Fischer Bayesian Deep Learning Workshop, NeurIPS 2019, 2019	17*	2019
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence A Kristiadi, M Hein, P Hennig NeurIPS 2021, 2021	16*	2021
Deep Convolutional Level Set Method for Image Segmentation. A Kristiadi Journal of ICT Research & Applications 11 (3), 2017	15	2017
Being a Bit Frequentist Improves Bayesian Neural Networks A Kristiadi, M Hein, P Hennig AISTATS 2022, 2022	14	2022
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks A Kristiadi, R Eschenhagen, P Hennig NeurIPS 2022, 2022	11	2022
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization A Kristiadi, A Immer, R Eschenhagen, V Fortuin AABI 2023, 2023	6	2023
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	5	2024
Parallel particle swarm optimization for image segmentation A Kristiadi, P Mudjihartono Universitas Atma Jaya Yogyakarta, 2013	4	2013
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization A Kristiadi, F Dangel, P Hennig NeurIPS 2023, 2023	3	2023
On the disconnect between theory and practice of overparametrized neural networks J Wenger, F Dangel, A Kristiadi arXiv preprint arXiv:2310.00137, 2023	3	2023
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
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	2	2024

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