Publication List

Thesis

  • Low-Cost Bayesian Methods for Fixing Neural Networks' Overconfidence
    Agustinus Kristiadi
    PhD Thesis, University of Tuebingen 2023

    Paper
  • Predictive Uncertainty Quantification With Compound Density Networks
    Agustinus Kristiadi
    Master Thesis, University of Bonn 2019

    Paper

Conference

  • 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
  • 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

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

    Paper

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

    Spotlight

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

    Paper

    Github
  • Fast Predictive Uncertainty for Classification with Bayesian Deep Networks
    Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig
  • Being a Bit Frequentist Improves Bayesian Neural Networks
    Agustinus Kristiadi, Matthias Hein, Philipp Hennig
    AISTATS 2022

    Paper

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

    Paper

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

    Spotlight

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

    Paper

    Github
  • Learnable Uncertainty under Laplace Approximations
    Agustinus Kristiadi, Matthias Hein, Philipp Hennig
  • Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
    Agustinus Kristiadi, Matthias Hein, Philipp Hennig
    ICML 2020

    Paper
  • Incorporating Literals into Knowledge Graph Embeddings
    Agustinus Kristiadi*, Mohammad Asif Khan*, Denis Lukovnikov, Jens Lehmann, Asja Fischer
  • Improving Response Selection in Multi-turn Dialogue Systems
    Debanjan Chaudhuri, Agustinus Kristiadi, Jens Lehmann, Asja Fischer
    CoNLL 2018

    Paper

    Github
  • Parallel Particle Swarm Optimization for Image Segmentation
    Agustinus Kristiadi, Pranowo, Paulus Mudjihartono
    Digital Enterprise and Information Systems 2013

    Paper

    Github
  • Parallel Particle Swarm Optimization for Image Segmentation
    Agustinus Kristiadi, Pranowo, Paulus Mudjihartono
    Digital Enterprise and Information Systems 2013

    Paper

    Github

Journal

  • Deep Convolutional Level Set Method for Image Segmentation
    Agustinus Kristiadi, Pranowo
    Journal of ICT Research and Applications 11(3) 2017

    Paper

    Github

Workshop

  • A Critical Look At Tokenwise Reward-Guided Text Generation
    Ahmad Rashid, Ruotian Wu, Julia Grosse, Agustinus Kristiadi*, Pascal Poupart*
    ICML Workshop on Foundation Models in the Wild 2024

    Paper
  • How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization?
    Agustinus Kristiadi, Felix Strieth-Kalthoff, Sriram Ganapathi Subramanian, Vincent Fortuin, Pascal Poupart, Geoff Pleiss
  • Uncertainty-Guided Optimization on Large Language Model Search Trees
    Julia Grosse, Ruotian Wu, Ahmad Rashid, Philipp Hennig, Pascal Poupart, Agustinus Kristiadi
  • 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

    Paper
  • 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
    AI4Mat - NeurIPS 2024

    Paper
  • Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization
    Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin
  • 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 2021

    Paper
  • Predictive Uncertainty Quantification with Compound Density Networks
    Agustinus Kristiadi, Sina Daeubener, Asja Fischer
    NeurIPS Workshop of Bayesian Deep Learning 2019

    Paper

    Github

Preprint

  • On the Disconnect Between Theory and Practice of Overparametrized Neural Networks
    Jonathan Wenger, Felix Dangel, Agustinus Kristiadi
    ArXiv 2023

    Paper