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ORCIDAgustinus Kristiadi
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2020 – today
- 2024
[c15]Ahmad Rashid, Serena Hacker, Guojun Zhang, Agustinus Kristiadi, Pascal Poupart:
Preventing Arbitrarily High Confidence on Far-Away Data in Point-Estimated Discriminative Neural Networks. AISTATS 2024: 3034-3042- [c14]Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alán Aspuru-Guzik, Geoff Pleiss:
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules? ICML 2024 - [c13]Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani:
Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC. ICML 2024 - [c12]Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang:
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 2024 - [i23]Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang:
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR abs/2402.00809 (2024) - [i22]Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alán Aspuru-Guzik, Geoff Pleiss:
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules? CoRR abs/2402.05015 (2024) - [i21]Agustinus Kristiadi, Felix Strieth-Kalthoff, Sriram Ganapathi Subramanian, Vincent Fortuin, Pascal Poupart, Geoff Pleiss:
How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization? CoRR abs/2406.06459 (2024) - [i20]Ahmad Rashid, Ruotian Wu, Julia Grosse, Agustinus Kristiadi, Pascal Poupart:
A Critical Look At Tokenwise Reward-Guided Text Generation. CoRR abs/2406.07780 (2024) - [i19]Julia Grosse, Ruotian Wu, Ahmad Rashid, Philipp Hennig, Pascal Poupart, Agustinus Kristiadi:
Uncertainty-Guided Optimization on Large Language Model Search Trees. CoRR abs/2407.03951 (2024) - [i18]Joanna Sliwa, Frank Schneider, Nathanael Bosch, Agustinus Kristiadi, Philipp Hennig:
Efficient Weight-Space Laplace-Gaussian Filtering and Smoothing for Sequential Deep Learning. CoRR abs/2410.06800 (2024) - 2023
- [b1]Agustinus Kristiadi:
Low-Cost Bayesian Methods for Fixing Neural Networks' Overconfidence. Tübingen University, Germany, 2023 - [c11]Agustinus Kristiadi, Felix Dangel, Philipp Hennig:
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization. NeurIPS 2023 - [i17]Agustinus Kristiadi, Felix Dangel, Philipp Hennig
:
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization. CoRR abs/2302.07384 (2023) - [i16]Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin:
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization. CoRR abs/2304.08309 (2023) - [i15]Jonathan Wenger, Felix Dangel, Agustinus Kristiadi:
On the Disconnect Between Theory and Practice of Overparametrized Neural Networks. CoRR abs/2310.00137 (2023) - [i14]Ahmad Rashid, Serena Hacker, Guojun Zhang, Agustinus Kristiadi, Pascal Poupart:
Preventing Arbitrarily High Confidence on Far-Away Data in Point-Estimated Discriminative Neural Networks. CoRR abs/2311.03683 (2023) - [i13]Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani:
Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets. CoRR abs/2312.05705 (2023) - 2022
- [c10]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Being a Bit Frequentist Improves Bayesian Neural Networks. AISTATS 2022: 529-545 - [c9]Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg:
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. AISTATS 2022: 1503-1526 - [c8]Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig:
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks. NeurIPS 2022 - [c7]Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig:
Fast predictive uncertainty for classification with Bayesian deep networks. UAI 2022: 822-832 - [i12]Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg:
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. CoRR abs/2203.03353 (2022) - [i11]Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig:
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks. CoRR abs/2205.10041 (2022) - 2021
- [c6]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence. NeurIPS 2021: 18789-18800 - [c5]Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig:
Laplace Redux - Effortless Bayesian Deep Learning. NeurIPS 2021: 20089-20103 - [c4]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Learnable uncertainty under Laplace approximations. UAI 2021: 344-353 - [i10]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Being a Bit Frequentist Improves Bayesian Neural Networks. CoRR abs/2106.10065 (2021) - [i9]Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig:
Laplace Redux - Effortless Bayesian Deep Learning. CoRR abs/2106.14806 (2021) - [i8]Runa Eschenhagen, Erik A. Daxberger, Philipp Hennig, Agustinus Kristiadi:
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning. CoRR abs/2111.03577 (2021) - 2020
- [c3]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. ICML 2020: 5436-5446 - [i7]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. CoRR abs/2002.10118 (2020) - [i6]Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig:
Fast Predictive Uncertainty for Classification with Bayesian Deep Networks. CoRR abs/2003.01227 (2020) - [i5]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features. CoRR abs/2010.02709 (2020) - [i4]Agustinus Kristiadi, Matthias Hein, Philipp Hennig:
Learnable Uncertainty under Laplace Approximations. CoRR abs/2010.02720 (2020)
2010 – 2019
- 2019
- [c2]Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov, Jens Lehmann, Asja Fischer:
Incorporating Literals into Knowledge Graph Embeddings. ISWC (1) 2019: 347-363 - [i3]Agustinus Kristiadi, Asja Fischer:
Predictive Uncertainty Quantification with Compound Density Networks. CoRR abs/1902.01080 (2019) - 2018
- [c1]Debanjan Chaudhuri, Agustinus Kristiadi, Jens Lehmann, Asja Fischer:
Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge. CoNLL 2018: 497-507 - [i2]Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov, Jens Lehmann, Asja Fischer:
Incorporating Literals into Knowledge Graph Embeddings. CoRR abs/1802.00934 (2018) - [i1]Debanjan Chaudhuri, Agustinus Kristiadi, Jens Lehmann, Asja Fischer:
Improving Response Selection in Multi-turn Dialogue Systems. CoRR abs/1809.03194 (2018)
Coauthor Index
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