$$ \newcommand{\dint}{\mathrm{d}} \newcommand{\vphi}{\boldsymbol{\phi}} \newcommand{\vpi}{\boldsymbol{\pi}} \newcommand{\vpsi}{\boldsymbol{\psi}} \newcommand{\vomg}{\boldsymbol{\omega}} \newcommand{\vsigma}{\boldsymbol{\sigma}} \newcommand{\vzeta}{\boldsymbol{\zeta}} \renewcommand{\vx}{\mathbf{x}} \renewcommand{\vy}{\mathbf{y}} \renewcommand{\vz}{\mathbf{z}} \renewcommand{\vh}{\mathbf{h}} \renewcommand{\b}{\mathbf} \renewcommand{\vec}{\mathrm{vec}} \newcommand{\vecemph}{\mathrm{vec}} \newcommand{\mvn}{\mathcal{MN}} \newcommand{\G}{\mathcal{G}} \newcommand{\M}{\mathcal{M}} \newcommand{\N}{\mathcal{N}} \newcommand{\S}{\mathcal{S}} \newcommand{\I}{\mathcal{I}} \newcommand{\diag}[1]{\mathrm{diag}(#1)} \newcommand{\diagemph}[1]{\mathrm{diag}(#1)} \newcommand{\tr}[1]{\text{tr}(#1)} \renewcommand{\C}{\mathbb{C}} \renewcommand{\R}{\mathbb{R}} \renewcommand{\E}{\mathbb{E}} \newcommand{\D}{\mathcal{D}} \newcommand{\inner}[1]{\langle #1 \rangle} \newcommand{\innerbig}[1]{\left \langle #1 \right \rangle} \newcommand{\abs}[1]{\lvert #1 \rvert} \newcommand{\norm}[1]{\lVert #1 \rVert} \newcommand{\two}{\mathrm{II}} \newcommand{\GL}{\mathrm{GL}} \newcommand{\Id}{\mathrm{Id}} \newcommand{\grad}[1]{\mathrm{grad} \, #1} \newcommand{\gradat}[2]{\mathrm{grad} \, #1 \, \vert_{#2}} \newcommand{\Hess}[1]{\mathrm{Hess} \, #1} \newcommand{\T}{\text{T}} \newcommand{\dim}[1]{\mathrm{dim} \, #1} \newcommand{\partder}[2]{\frac{\partial #1}{\partial #2}} \newcommand{\rank}[1]{\mathrm{rank} \, #1} \newcommand{\inv}1 \newcommand{\map}{\text{MAP}} \newcommand{\L}{\mathcal{L}} \DeclareMathOperator*{\argmax}{arg\,max} \DeclareMathOperator*{\argmin}{arg\,min} $$

Agustinus Kristiadi

Last-year Ph.D. student at the University of Tübingen

Conference

Fast Predictive Uncertainty for Classification with Bayesian Deep Networks

Marius Hobbhahn, Agustinus Kristiadi, and Philipp Hennig.

UAI 2022 [arxiv]

Being a Bit Frequentist Improves Bayesian Neural Networks

Agustinus Kristiadi, Matthias Hein, and Philipp Hennig.

AISTATS 2022 [arxiv] [code]

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

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

AISTATS 2022 [arxiv] [code]

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

Agustinus Kristiadi, Matthias Hein, and Philipp Hennig.

NeurIPS 2021 [Spotlight] [paper] [code]

Laplace Redux -- Effortless Bayesian Deep Learning

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

NeurIPS 2021 [paper] [code]

Learnable Uncertainty under Laplace Approximations

Agustinus Kristiadi, Matthias Hein, and Philipp Hennig.

UAI 2021 [paper] [code]

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

Agustinus Kristiadi, Matthias Hein, and Philipp Hennig.

ICML 2020 [paper] [code]

Incorporating Literals into Knowledge Graph Embeddings

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

ISWC 2019 [arxiv] [code]

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

Debanjan Chauduri, Agustinus Kristiadi, Jens Lehmann, Asja Fischer.

CoNLL 2018 [arxiv] [code]

Deep Convolutional Level Set Method for Image Segmentation

Agustinus Kristiadi and Pranowo.

Journal of ICT Research and Applications 11.3 (2017) [pdf] [code]

Parallel Particle Swarm Optimization for Image Segmentation

Agustinus Kristiadi, Pranowo, and Paulus Mudjihartono.

DEIS 2013 [pdf] [code]

Workshop

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

Runa Eschenhagen, Erik Daxberger, Philipp Hennig, Agustinus Kristiadi.

Bayesian Deep Learning Workshop, NeurIPS 2021 [arxiv]

Predictive Uncertainty Quantification with Compound Density Networks

Agustinus Kristiadi, Sina Däubener, and Asja Fischer.

Bayesian Deep Learning Workshop, NeurIPS 2019 [arxiv] [code]