Agustinus Kristiadi is a postdoctoral fellow at the Vector Institute, working primarily with Alán Aspuru-Guzik and Pascal Poupart. He obtained his PhD from the University of Tuebingen in Germany, advised by Philipp Hennig and Matthias Hein. His research interests are in probabilistic inference with large-scale neural networks, decision-making under uncertainty, and their applications in broader scientific fields such as chemistry. His work has been recognized in the form of best PhD thesis award and multiple spotlight papers from flagship machine learning conferences. His contributions to the scientific society include mentoring underrepresented students in Canada under the IBET PhD Project and actively contributing to the open-source community.

• 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

  ICML 2024

  Paper

  Github

• Uncertainty-Guided Optimization on Large Language Model Search Trees

  Julia Grosse, Ruotian Wu, Ahmad Rashid, Philipp Hennig, Pascal Poupart, Agustinus Kristiadi

  AABI 2024

  Paper

  Github

• The Geometry of Neural Nets' Parameter Spaces Under Reparametrization

  Agustinus Kristiadi, Felix Dangel, Philipp Hennig

  NeurIPS 2023

  Spotlight

  Paper

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

  Agustinus Kristiadi, Matthias Hein, Philipp Hennig

  NeurIPS 2021

  Spotlight

  Paper

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

  Agustinus Kristiadi, Matthias Hein, Philipp Hennig

  ICML 2020

  Paper

• Aug 30, 2024
  Writing Advice for Fledging Machine Learning Researchers

  Some bullet-point advice regarding paper-writing I would give to my younger self.

• Aug 3, 2024
  The 'use' Expression in Gleam

  How can we emulate the behavior of Python's `with` and Rust `?` in Gleam?

• Dec 13, 2023
  Volume Forms and Probability Density Functions Under Change of Variables

  From elementary probability theory, it is well known that a probability density function (pdf) is not invariant under ...

• Feb 9, 2023
  The Invariance of the Hessian and Its Eigenvalues, Determinant, and Trace

  In deep learning, the Hessian and its downstream quantities are observed to be not invariant under reparametrization. This ...

• Jun 25, 2022
  Convolution of Gaussians and the Probit Integral

  Gaussian distributions are very useful in Bayesian inference due to their (many!) convenient properties. In this post we ...