Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge. - Debanjan Chaudhuri; Agustinus Kristiadi; Jens Lehmann 001; Asja Fischer (CoNLL, 2018)

Improving Response Selection in Multi-turn Dialogue Systems. - Debanjan Chaudhuri; Agustinus Kristiadi; Jens Lehmann 001; Asja Fischer (CoRR, 2018)

Incorporating Literals into Knowledge Graph Embeddings. - Agustinus Kristiadi; Mohammad Asif Khan 001; Denis Lukovnikov; Jens Lehmann 001; Asja Fischer (ISWC (1), 2019)

Predictive Uncertainty Quantification with Compound Density Networks. - Agustinus Kristiadi; Asja Fischer (CoRR, 2019)

Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. - Agustinus Kristiadi; Matthias Hein 001; Philipp Hennig (ICML, 2020)

Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features. - Agustinus Kristiadi; Matthias Hein 001; Philipp Hennig (CoRR, 2020)

Laplace Redux - Effortless Bayesian Deep Learning. - Erik A. Daxberger; Agustinus Kristiadi; Alexander Immer; Runa Eschenhagen; Matthias Bauer 001; Philipp Hennig (NeurIPS, 2021)

An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence. - Agustinus Kristiadi; Matthias Hein 001; Philipp Hennig (NeurIPS, 2021)

Learnable uncertainty under Laplace approximations. - Agustinus Kristiadi; Matthias Hein 001; Philipp Hennig (UAI, 2021)

Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning. - Runa Eschenhagen; Erik A. Daxberger; Philipp Hennig; Agustinus Kristiadi (CoRR, 2021)

Being a Bit Frequentist Improves Bayesian Neural Networks. - Agustinus Kristiadi; Matthias Hein 001; 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 (AISTATS, 2022)

Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks. - Agustinus Kristiadi; Runa Eschenhagen; Philipp Hennig (NeurIPS, 2022)

Fast predictive uncertainty for classification with Bayesian deep networks. - Marius Hobbhahn; Agustinus Kristiadi; Philipp Hennig (UAI, 2022)

The Geometry of Neural Nets' Parameter Spaces Under Reparametrization. - Agustinus Kristiadi; Felix Dangel; Philipp Hennig (NeurIPS, 2023)

Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization. - Agustinus Kristiadi; Alexander Immer; Runa Eschenhagen; Vincent Fortuin (CoRR, 2023)

On the Disconnect Between Theory and Practice of Overparametrized Neural Networks. - Jonathan Wenger; Felix Dangel; Agustinus Kristiadi (CoRR, 2023)

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 (CoRR, 2023)

Low-Cost Bayesian Methods for Fixing Neural Networks' Overconfidence - Agustinus Kristiadi (2023)

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)

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; Alán Aspuru-Guzik; Geoff Pleiss (ICML, 2024)

Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC. - 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 B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (ICML, 2024)

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (CoRR, 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 (CoRR, 2024)

A Critical Look At Tokenwise Reward-Guided Text Generation. - Ahmad Rashid; Ruotian Wu; Julia Grosse; Agustinus Kristiadi; Pascal Poupart (CoRR, 2024)

Uncertainty-Guided Optimization on Large Language Model Search Trees. - Julia Grosse; Ruotian Wu; Ahmad Rashid; Philipp Hennig; Pascal Poupart; Agustinus Kristiadi (CoRR, 2024)

Efficient Weight-Space Laplace-Gaussian Filtering and Smoothing for Sequential Deep Learning. - Joanna Sliwa 002; Frank Schneider 001; Nathanael Bosch; Agustinus Kristiadi; Philipp Hennig (CoRR, 2024)

Position: Curvature Matrices Should Be Democratized via Linear Operators. - Felix Dangel; Runa Eschenhagen; Weronika Ormaniec; Andres Fernandez; Lukas Tatzel; Agustinus Kristiadi (CoRR, 2025)

Towards Cost-Effective Reward Guided Text Generation. - Ahmad Rashid; Ruotian Wu; Rongqi Fan; Hongliang Li; Agustinus Kristiadi; Pascal Poupart (CoRR, 2025)

One Set to Rule Them All: How to Obtain General Chemical Conditions via Bayesian Optimization over Curried Functions. - Stefan P. Schmid; Ella M. Rajaonson; Cher Tian Ser; Mohammad Haddadnia; Shi Xuan Leong; Alán Aspuru-Guzik; Agustinus Kristiadi; Kjell Jorner; Felix Strieth-Kalthoff (CoRR, 2025)

FlashMD: long-stride, universal prediction of molecular dynamics. - Filippo Bigi; Sanggyu Chong; Agustinus Kristiadi; Michele Ceriotti (CoRR, 2025)

Simplifying Bayesian Optimization Via In-Context Direct Optimum Sampling. - Gustavo Sutter Pessurno de Carvalho; Mohammed Abdulrahman; Hao Wang; Sriram Ganapathi Subramanian; Marc St-Aubin; Sharon O'Sullivan; Lawrence Wan; Luis A. Ricardez-Sandoval; Pascal Poupart; Agustinus Kristiadi (CoRR, 2025)

Introduction to the Analysis of Probabilistic Decision-Making Algorithms. - Agustinus Kristiadi (CoRR, 2025)