Agustinus Kristiadi
15 papers · 2018–2025 · 5 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
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Research topics
Machine Learning
(13)
Optimization & Theory
(12)
Artificial Intelligence
(9)
Deep Learning
(6)
Bayesian & Probabilistic Β· Artificial Intelligence
(6)
Core AI
(3)
Application Areas
(2)
Architectures
(2)
Learning Types
(2)
Models
(2)
Mathematics & Optimization
(1)
Natural Language Processing
(1)
Reinforcement Learning
(1)
Bayesian & Probabilistic Β· Machine Learning
(1)
Core Methods
(1)
Keywords
uncertainty quantification
(5)
bayesian neural network
(4)
bayesian deep learning
(3)
variational inference
(3)
predictive uncertainty
(3)
laplace approximation
(3)
bayesian inference
(2)
neural network
(2)
relu network
(2)
out-of-distribution detection
(2)
posterior approximation
(2)
approximate bayesian inference
(1)
model misspecification
(1)
bayesian optimization
(1)
natural gradient descent
(1)
gaussian process
(1)
approximate inference
(1)
hamiltonian monte carlo
(1)
neural network optimization
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point estimation
(1)
Papers
Towards Cost-Effective Reward Guided Text Generation
ICML 2025
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?
ICML 2024
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
ICML 2024
Preventing Arbitrarily High Confidence on Far-Away Data in Point-Estimated Discriminative Neural Networks
AISTATS 2024
Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC
ICML 2024
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization
NeurIPS 2023
Being a Bit Frequentist Improves Bayesian Neural Networks
AISTATS 2022
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference
AISTATS 2022
Fast predictive uncertainty for classification with Bayesian deep networks
UAI 2022
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
NeurIPS 2022
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence
NeurIPS 2021
Laplace Redux - Effortless Bayesian Deep Learning
NeurIPS 2021
Learnable uncertainty under Laplace approximations
UAI 2021
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
ICML 2020
Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge
CoNLL 2018