Agustinus Kristiadi

Publication List

In the field of machine learning, publishing in conferences is the norm. Top conferences includes NeurIPS (prev. NIPS), ICML, ICLR, AISTATS, UAI, and others. They are peer-reviewed and highly competitive.


The first or joint-first author (the latter is marked with a '*') of a paper is the lead author. The last or joint-last author is usually the one who came up with the idea and directed the project. They are all considered to be the core authors of the paper.

Conference

  • NeurIPS 2025 Spotlight (Top 4% Paper)

    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
  • COLM 2025

    A Critical Look At Tokenwise Reward-Guided Text Generation

    Ahmad Rashid, Ruotian Wu, Julia Grosse, Agustinus Kristiadi*, Pascal Poupart*
  • ICML 2024

    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
  • 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, 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 Ruegammer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
  • 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
  • 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
  • NeurIPS 2023 Spotlight (Top 4% Paper)

    The Geometry of Neural Nets' Parameter Spaces Under Reparametrization

    Agustinus Kristiadi, Felix Dangel, Philipp Hennig
  • NeurIPS 2022

    Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks

    Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig
  • UAI 2022

    Fast Predictive Uncertainty for Classification with Bayesian Deep Networks

    Marius Hobbhahn, Agustinus Kristiadi, 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
  • NeurIPS 2021 Spotlight (Top 3% Paper)

    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 Long Talk (Top 6% Paper)

    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
  • ISWC 2019

    Incorporating Literals into Knowledge Graph Embeddings

    Agustinus Kristiadi*, Mohammad Asif Khan*, Denis Lukovnikov, Jens Lehmann, Asja Fischer
  • CoNLL 2018

    Improving Response Selection in Multi-turn Dialogue Systems

    Debanjan Chaudhuri, Agustinus Kristiadi, Jens Lehmann, Asja Fischer
  • Digital Enterprise and Information Systems 2013

    Parallel Particle Swarm Optimization for Image Segmentation

    Agustinus Kristiadi, Pranowo, Paulus Mudjihartono
  • Digital Enterprise and Information Systems 2013

    Parallel Particle Swarm Optimization for Image Segmentation

    Agustinus Kristiadi, Pranowo, Paulus Mudjihartono

Journal

  • Journal of Chemical Information and Modeling 2025

    Generative AI for the Design of Molecules: Advances and Challenges

    Yan Sun, Lianghong Chen, Zihao Jing, Yan Yi Li, Dongkyu Kim, Jing-Yan Gao, Reza Noroozi, Grace Yi, Conrard Tetsassi Feugmo, Anna Klinkova, Kyla Sask, Agustinus Kristiadi, Boyu Wang, Elizabeth Gillies, Kun Ping Lu, HaoTian Harvey Shi, Pingzhao Hu
  • Journal of ICT Research and Applications 11(3) 2017

    Deep Convolutional Level Set Method for Image Segmentation

    Agustinus Kristiadi, Pranowo

Workshop

  • AI for Accelerated Materials Design–ICLR 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
  • MATH-AI-NeurIPS 2025

    Limits of PRM-Guided Tree Search for Mathematical Reasoning with LLMs

    Tristan Cinquin, Geoff Pleiss, Agustinus Kristiadi
  • AABI 2024

    How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization?

    Agustinus Kristiadi, Felix Strieth-Kalthoff, Sriram Ganapathi Subramanian, Vincent Fortuin, Pascal Poupart, Geoff Pleiss
  • AABI 2024

    Uncertainty-Guided Optimization on Large Language Model Search Trees

    Julia Grosse, Ruotian Wu, Ahmad Rashid, Philipp Hennig, Pascal Poupart, Agustinus Kristiadi
  • AI4Mat - NeurIPS 2024

    If Optimizing for General Parameters in Chemistry Is Useful, Why Is It Hardly Done?

    Stefan Schmid, Ella Rajaonson, Cher-Tian Ser, Mohammad Haddadnia, Shi Xuan Leong, Alan Aspuru-Guzik, Agustinus Kristiadi, Kjell Jorner, Felix Strieth-Kalthoff
  • AI4Mat - NeurIPS 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
  • AABI 2023

    Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization

    Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin
  • NeurIPS Workshop of Bayesian Deep Learning 2021

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

    Runa Eschenhagen, Erik Daxberger, Philipp Hennig, Agustinus Kristiadi
  • NeurIPS Workshop of Bayesian Deep Learning 2019

    Predictive Uncertainty Quantification with Compound Density Networks

    Agustinus Kristiadi, Sina Daeubener, Asja Fischer

Preprint

  • ArXiv 2025

    Position: Curvature Matrices Should Be Democratized via Linear Operators

    Felix Dangel, Runa Eschenhagen, Weronika Ormaniec, Andres Fernandez, Lukas Tatzel, Agustinus Kristiadi
  • ArXiv 2025

    Simplifying Bayesian Optimization Via In-Context Direct Optimum Sampling

    Gustavo Sutter, Mohammed Abdulrahman, Hao Wang, Sriram Ganapathi Subramanian, Marc St-Aubin, Sharon O'Sullivan, Lawrence Wan, Luis Ricardez-Sandoval, Pascal Poupart, Agustinus Kristiadi
  • ArXiv 2025

    Introduction to the Analysis of Probabilistic Decision-Making Algorithms

    Agustinus Kristiadi
  • ArXiv 2024

    Efficient Weight-Space Laplace-Gaussian Filtering and Smoothing for Sequential Deep Learning

    Joanna Sliwa, Frank Schneider, Nathanael Bosch, Agustinus Kristiadi, Philipp Hennig
  • ArXiv 2023

    On the Disconnect Between Theory and Practice of Overparametrized Neural Networks

    Jonathan Wenger, Felix Dangel, Agustinus Kristiadi

Thesis

  • PhD Thesis, University of Tuebingen 2023

    Low-Cost Bayesian Methods for Fixing Neural Networks' Overconfidence

    Agustinus Kristiadi
  • Master Thesis, University of Bonn 2019

    Predictive Uncertainty Quantification With Compound Density Networks

    Agustinus Kristiadi