Agustinus Kristiadi

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 34

Top Topics

Bayesian Inference

Dempster Shafer

Top Venues

Publications

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 (2024)
Ahmad Rashid, Ruotian Wu, Julia Grosse, Agustinus Kristiadi, Pascal Poupart
A Critical Look At Tokenwise Reward-Guided Text Generation. CoRR (2024)
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)
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 (2024)
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 (2024)
Julia Grosse, Ruotian Wu, Ahmad Rashid, Philipp Hennig, Pascal Poupart, Agustinus Kristiadi
Uncertainty-Guided Optimization on Large Language Model Search Trees. CoRR (2024)
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 (2023)
Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization. CoRR (2023)
Agustinus Kristiadi, Felix Dangel, Philipp Hennig
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization. NeurIPS (2023)
Agustinus Kristiadi, Felix Dangel, Philipp Hennig
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization. CoRR (2023)
Jonathan Wenger, Felix Dangel, Agustinus Kristiadi
On the Disconnect Between Theory and Practice of Overparametrized Neural Networks. CoRR (2023)
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 (2023)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Being a Bit Frequentist Improves Bayesian Neural Networks. AISTATS (2022)
Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks. NeurIPS (2022)
Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. AISTATS (2022)
Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. CoRR (2022)
Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks. CoRR (2022)
Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig
Fast predictive uncertainty for classification with Bayesian deep networks. UAI (2022)
Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig
Laplace Redux - Effortless Bayesian Deep Learning. NeurIPS (2021)
Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig
Laplace Redux - Effortless Bayesian Deep Learning. CoRR (2021)
Runa Eschenhagen, Erik Daxberger, Philipp Hennig, Agustinus Kristiadi
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning. CoRR (2021)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Learnable uncertainty under Laplace approximations. UAI (2021)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence. NeurIPS (2021)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Being a Bit Frequentist Improves Bayesian Neural Networks. CoRR (2021)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. ICML (2020)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features. CoRR (2020)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Learnable Uncertainty under Laplace Approximations. CoRR (2020)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. CoRR (2020)
Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig
Fast Predictive Uncertainty for Classification with Bayesian Deep Networks. CoRR (2020)
Agustinus Kristiadi, Asja Fischer
Predictive Uncertainty Quantification with Compound Density Networks. CoRR (2019)
Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov, Jens Lehmann, Asja Fischer
Incorporating Literals into Knowledge Graph Embeddings. ISWC (1) (2019)
Debanjan Chaudhuri, Agustinus Kristiadi, Jens Lehmann, Asja Fischer
Improving Response Selection in Multi-turn Dialogue Systems. CoRR (2018)
Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov, Jens Lehmann, Asja Fischer
Incorporating Literals into Knowledge Graph Embeddings. CoRR (2018)
Debanjan Chaudhuri, Agustinus Kristiadi, Jens Lehmann, Asja Fischer
Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge. CoNLL (2018)