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Agustinus's Portrait

Agustinus Kristiadi

Email
agustinus[at]kristia.de

Interest

Uncertainty quantification in deep learning
Bayesian inference
Riemannian and information geometry

Education

University of Tübingen, Germany

Ph.D., Computer Science, 2019-
Methods of Machine Learning group, advised by Philipp Hennig

University of Bonn, Germany

M.Sc., Computer Science, 2017-2019
Theoretical Computer Science, Intelligent Systems
Grade: 1.1 (3.9 GPA equivalent)

Universitas Atma Jaya Yogyakarta, Indonesia

B.Eng., Informatics Engineering, 2009-2013
Numerics, Soft Computing
Thesis: Parallel Particle Swarm Optimization for Image Segmentation
GPA: 3.9

Teaching

University of Tübingen

Tutor, Data Literacy, Winter Semester 2021-2022
Tutor, Probabilistic Machine Learning, Summer Semester 2021
Tutor, Data Literacy, Winter Semester 2020-2021
Tutor, Probabilistic Machine Learning, Summer Semester 2020
Tutor, Data Literacy, Winter Semester 2019-2020

Universitas Atma Jaya Yogyakarta

Tutor, Advanced Data Structure, 2012
Tutor, Database, 2011

Academic Experience

Methods of Machine Learning group, University of Tübingen

Research Assistant, 2019-

Smart Data Analytics (SDA) group, University of Bonn

Student Research Assistant, 2017-2019

Universitas Atma Jaya Yogyakarta

Research Assistant, 2016-2017

Industry Experience

GDP Labs

Software Engineer, 2013-2015

Astra International

Software Engineering Intern, 2012

Language

English (IELTS 8.0)
German (A2)
Indonesian (native)
Javanese (native)

Publication

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

Kristiadi, Agustinus, Matthias Hein, and Philipp Hennig.
NeurIPS 2021 [Spotlight] [arxiv]

Laplace Redux -- Effortless Bayesian Deep Learning

Daxberger*, Erik, Agustinus Kristiadi*, Alexander Immer*, Runa Eschenhagen*, Matthias Bauer, and Philipp Hennig.
NeurIPS 2021 [arxiv] [code]

Learnable Uncertainty under Laplace Approximations

Kristiadi, Agustinus, Matthias Hein, and Philipp Hennig.
UAI 2021 [arxiv] [code]

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

Kristiadi, Agustinus, Matthias Hein, and Philipp Hennig.
ICML 2020 [abs] [code]

Predictive Uncertainty Quantification with Compound Density Networks

Kristiadi, Agustinus, Sina Däubener, and Asja Fischer.
Bayesian Deep Learning Workshop, NeurIPS 2019 [arxiv] [code]

Incorporating Literals into Knowledge Graph Embeddings

Kristiadi, Agustinus*, 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

Chauduri, Debanjan, Agustinus Kristiadi, Jens Lehmann, Asja Fischer.
CoNLL 2018 [arxiv] [code]

Deep Convolutional Level Set Method for Image Segmentation

Kristiadi, Agustinus, and Pranowo Pranowo.
Journal of ICT Research and Applications 11.3 (2017) [pdf] [code]

Parallel Particle Swarm Optimization for Image Segmentation

Kristiadi, Agustinus, Pranowo Pranowo, and Paulus Mudjihartono.
DEIS 2013 [pdf] [code]

Preprint

Being a Bit Frequentist Improves Bayesian Neural Networks

Agustinus Kristiadi, Matthias Hein, and Philipp Hennig.
[arxiv]

Fast Predictive Uncertainty for Classification with Bayesian Deep Networks

Hobbhahn, Marius, Agustinus Kristiadi, and Philipp Hennig.
[arxiv]