Agustinus Kristiadi

According to our database, Agustinus Kristiadi authored at least 22 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?

Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alán Aspuru-Guzik, Geoff Pleiss

CoRR, 2024

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI.

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

CoRR, 2024

2023

Low-Cost Bayesian Methods for Fixing Neural Networks' Overconfidence

Agustinus Kristiadi

PhD thesis, 2023

Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets.

Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani

CoRR, 2023

Preventing Arbitrarily High Confidence on Far-Away Data in Point-Estimated Discriminative Neural Networks.

Ahmad Rashid, Serena Hacker, Guojun Zhang, Agustinus Kristiadi, Pascal Poupart

CoRR, 2023

On the Disconnect Between Theory and Practice of Overparametrized Neural Networks.

Jonathan Wenger, Felix Dangel, Agustinus Kristiadi

CoRR, 2023

Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization.

Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin

CoRR, 2023

The Geometry of Neural Nets' Parameter Spaces Under Reparametrization.

Agustinus Kristiadi, Felix Dangel, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Fast predictive uncertainty for classification with Bayesian deep networks.

Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks.

Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference.

Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Being a Bit Frequentist Improves Bayesian Neural Networks.

Agustinus Kristiadi, Matthias Hein, Philipp Hennig

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning.

Runa Eschenhagen, Erik Daxberger, Philipp Hennig, Agustinus Kristiadi

CoRR, 2021

Learnable uncertainty under Laplace approximations.

Agustinus Kristiadi, Matthias Hein, Philipp Hennig

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence.

Agustinus Kristiadi, Matthias Hein, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Laplace Redux - Effortless Bayesian Deep Learning.

Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features.

Agustinus Kristiadi, Matthias Hein, Philipp Hennig

CoRR, 2020

Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks.

Agustinus Kristiadi, Matthias Hein, Philipp Hennig

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Predictive Uncertainty Quantification with Compound Density Networks.

Agustinus Kristiadi, Asja Fischer

CoRR, 2019

Incorporating Literals into Knowledge Graph Embeddings.

Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov, Jens Lehmann, Asja Fischer

Proceedings of the Semantic Web - ISWC 2019, 2019

2018

Improving Response Selection in Multi-turn Dialogue Systems.

Debanjan Chaudhuri, Agustinus Kristiadi, Jens Lehmann, Asja Fischer

CoRR, 2018

Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge.

Debanjan Chaudhuri, Agustinus Kristiadi, Jens Lehmann, Asja Fischer

Proceedings of the 22nd Conference on Computational Natural Language Learning, 2018