Kristiadi, Agustinus

20 publications

NeurIPS 2025 FlashMD: Long-Stride, Universal Prediction of Molecular Dynamics Filippo Bigi, Sanggyu Chong, Agustinus Kristiadi, Michele Ceriotti

ICML 2025 Towards Cost-Effective Reward Guided Text Generation Ahmad Rashid, Ruotian Wu, Rongqi Fan, Hongliang Li, Agustinus Kristiadi, Pascal Poupart

ICLRW 2025 What Actually Matters for Materials Discovery: Pitfalls and Recommendations in Bayesian Optimization Tristan Cinquin, Stanley Lo, Felix Strieth-Kalthoff, Alan Aspuru-Guzik, Geoff Pleiss, Robert Bamler, Tim G. J. Rudner, Vincent Fortuin, Agustinus Kristiadi

ICMLW 2024 A Critical Look at Tokenwise Reward-Guided Text Generation Ahmad Rashid, Ruotian Wu, Julia Grosse, Agustinus Kristiadi, Pascal Poupart

ICML 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, Alan Aspuru-Guzik, Geoff Pleiss

NeurIPSW 2024 Dimension Deficit: Is 3D a Step Too Far for Optimizing Molecules? Andres Guzman Cordero, Luca Thiede, Gary Tom, Alan Aspuru-Guzik, Felix Strieth-Kalthoff, Agustinus Kristiadi

NeurIPSW 2024 If Optimizing for General Parameters in Chemistry Is Useful, Why Is It Hardly Done? Stefan P. Schmid, Ella Miray Rajaonson, Cher Tian Ser, Mohammad Haddadnia, Shi Xuan Leong, Alan Aspuru-Guzik, Agustinus Kristiadi, Kjell Jorner, Felix Strieth-Kalthoff

ICML 2024 Position: Bayesian Deep Learning Is Needed in the Age of Large-Scale AI Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David 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

AISTATS 2024 Preventing Arbitrarily High Confidence on Far-Away Data in Point-Estimated Discriminative Neural Networks Ahmad Rashid, Serena Hacker, Guojun Zhang, Agustinus Kristiadi, Pascal Poupart

ICML 2024 Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani

NeurIPSW 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

NeurIPS 2023 The Geometry of Neural Nets' Parameter Spaces Under Reparametrization Agustinus Kristiadi, Felix Dangel, Philipp Hennig

AISTATS 2022 Being a Bit Frequentist Improves Bayesian Neural Networks Agustinus Kristiadi, Matthias Hein, Philipp Hennig

AISTATS 2022 Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike Von Luxburg

UAI 2022 Fast Predictive Uncertainty for Classification with Bayesian Deep Networks Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig

NeurIPS 2022 Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig

NeurIPS 2021 An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence Agustinus Kristiadi, Matthias Hein, Philipp Hennig

NeurIPS 2021 Laplace Redux - Effortless Bayesian Deep Learning Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig

UAI 2021 Learnable Uncertainty Under Laplace Approximations Agustinus Kristiadi, Matthias Hein, Philipp Hennig

ICML 2020 Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks Agustinus Kristiadi, Matthias Hein, Philipp Hennig