Agustinus Kristiadi

48 papers A* 9A 5B 1Journal 31Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	FlashMD: long-stride, universal prediction of molecular dynamics. CoRR Filippo Bigi, Sanggyu Chong, Agustinus Kristiadi, Michele Ceriotti
2025	J	`jnl`	Generative AI for the Design of Molecules: Advances and Challenges. J. Chem. Inf. Model. Yan Sun, Lianghong Chen, Zihao Jing, Yan Yi Li, Dongkyu Kim, Jing-Yan Gao, Reza Noroozi, Grace Y. Yi, Conrard Giresse Tetsassi Feugmo, Anna Klinkova, Kyla Sask, Agustinus Kristiadi, Boyu Wang, Elizabeth R. Gillies, Kun Ping Lu, Haotian Harvey Shi, Pingzhao Hu
2025	J	`jnl`	Introduction to the Analysis of Probabilistic Decision-Making Algorithms. CoRR Agustinus Kristiadi
2025	J	`jnl`	Limits of PRM-Guided Tree Search for Mathematical Reasoning with LLMs. CoRR Tristan Cinquin, Geoff Pleiss, Agustinus Kristiadi
2025	J	`jnl`	One Set to Rule Them All: How to Obtain General Chemical Conditions via Bayesian Optimization over Curried Functions. CoRR Stefan P. Schmid, Ella M. Rajaonson, Cher Tian Ser, Mohammad Haddadnia, Shi Xuan Leong, Alán Aspuru-Guzik, Agustinus Kristiadi, Kjell Jorner, Felix Strieth-Kalthoff
2025	J	`jnl`	Position: Curvature Matrices Should Be Democratized via Linear Operators. CoRR Felix Dangel, Runa Eschenhagen, Weronika Ormaniec, Andres Fernandez, Lukas Tatzel, Agustinus Kristiadi
2025	J	`jnl`	Simplifying Bayesian Optimization Via In-Context Direct Optimum Sampling. CoRR Gustavo Sutter, Mohammed Abdulrahman, Hao Wang, Sriram Ganapathi Subramanian, Marc St-Aubin, Sharon O'Sullivan, Lawrence Wan, Luis A. Ricardez-Sandoval, Pascal Poupart, Agustinus Kristiadi
2025	A*	`conf`	Towards Cost-Effective Reward Guided Text Generation. ICML Ahmad Rashid, Ruotian Wu, Rongqi Fan, Hongliang Li, Agustinus Kristiadi, Pascal Poupart
2025	J	`jnl`	Towards Cost-Effective Reward Guided Text Generation. CoRR Ahmad Rashid, Ruotian Wu, Rongqi Fan, Hongliang Li, Agustinus Kristiadi, Pascal Poupart
2024	J	`jnl`	A Critical Look At Tokenwise Reward-Guided Text Generation. CoRR Ahmad Rashid, Ruotian Wu, Julia Grosse, Agustinus Kristiadi, Pascal Poupart
2024	A*	`conf`	A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules? ICML Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alán Aspuru-Guzik, Geoff Pleiss
2024	J	`jnl`	A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules? CoRR Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alán Aspuru-Guzik, Geoff Pleiss
2024	J	`jnl`	Efficient Weight-Space Laplace-Gaussian Filtering and Smoothing for Sequential Deep Learning. CoRR Joanna Sliwa, Frank Schneider, Nathanael Bosch, Agustinus Kristiadi, Philipp Hennig
2024	J	`jnl`	How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization? CoRR Agustinus Kristiadi, Felix Strieth-Kalthoff, Sriram Ganapathi Subramanian, Vincent Fortuin, Pascal Poupart, Geoff Pleiss
2024	J	`jnl`	Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR 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, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
2024	A*	`conf`	Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 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, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
2024	A	`conf`	Preventing Arbitrarily High Confidence on Far-Away Data in Point-Estimated Discriminative Neural Networks. AISTATS Ahmad Rashid, Serena Hacker, Guojun Zhang, Agustinus Kristiadi, Pascal Poupart
2024	A*	`conf`	Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC. ICML Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani
2024	J	`jnl`	Uncertainty-Guided Optimization on Large Language Model Search Trees. CoRR Julia Grosse, Ruotian Wu, Ahmad Rashid, Philipp Hennig, Pascal Poupart, Agustinus Kristiadi
2023	—	`—`	Low-Cost Bayesian Methods for Fixing Neural Networks' Overconfidence Agustinus Kristiadi
2023	J	`jnl`	On the Disconnect Between Theory and Practice of Overparametrized Neural Networks. CoRR Jonathan Wenger, Felix Dangel, Agustinus Kristiadi
2023	J	`jnl`	Preventing Arbitrarily High Confidence on Far-Away Data in Point-Estimated Discriminative Neural Networks. CoRR Ahmad Rashid, Serena Hacker, Guojun Zhang, Agustinus Kristiadi, Pascal Poupart
2023	J	`jnl`	Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization. CoRR Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin
2023	J	`jnl`	Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets. CoRR Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani
2023	A*	`conf`	The Geometry of Neural Nets' Parameter Spaces Under Reparametrization. NeurIPS Agustinus Kristiadi, Felix Dangel, Philipp Hennig
2023	J	`jnl`	The Geometry of Neural Nets' Parameter Spaces Under Reparametrization. CoRR Agustinus Kristiadi, Felix Dangel, Philipp Hennig
2022	A	`conf`	Being a Bit Frequentist Improves Bayesian Neural Networks. AISTATS Agustinus Kristiadi, Matthias Hein, Philipp Hennig
2022	A	`conf`	Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. AISTATS Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg
2022	J	`jnl`	Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. CoRR Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg
2022	A	`conf`	Fast predictive uncertainty for classification with Bayesian deep networks. UAI Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig
2022	A*	`conf`	Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks. NeurIPS Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig
2022	J	`jnl`	Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks. CoRR Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig
2021	A*	`conf`	An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence. NeurIPS Agustinus Kristiadi, Matthias Hein, Philipp Hennig
2021	J	`jnl`	Being a Bit Frequentist Improves Bayesian Neural Networks. CoRR Agustinus Kristiadi, Matthias Hein, Philipp Hennig
2021	A*	`conf`	Laplace Redux - Effortless Bayesian Deep Learning. NeurIPS Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig
2021	J	`jnl`	Laplace Redux - Effortless Bayesian Deep Learning. CoRR Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig
2021	A	`conf`	Learnable uncertainty under Laplace approximations. UAI Agustinus Kristiadi, Matthias Hein, Philipp Hennig
2021	J	`jnl`	Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning. CoRR Runa Eschenhagen, Erik A. Daxberger, Philipp Hennig, Agustinus Kristiadi
2020	A*	`conf`	Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. ICML Agustinus Kristiadi, Matthias Hein, Philipp Hennig
2020	J	`jnl`	Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. CoRR Agustinus Kristiadi, Matthias Hein, Philipp Hennig
2020	J	`jnl`	Fast Predictive Uncertainty for Classification with Bayesian Deep Networks. CoRR Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig
2020	J	`jnl`	Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features. CoRR Agustinus Kristiadi, Matthias Hein, Philipp Hennig
2020	J	`jnl`	Learnable Uncertainty under Laplace Approximations. CoRR Agustinus Kristiadi, Matthias Hein, Philipp Hennig
2019	—	`conf`	Incorporating Literals into Knowledge Graph Embeddings. ISWC (1) Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov, Jens Lehmann, Asja Fischer
2019	J	`jnl`	Predictive Uncertainty Quantification with Compound Density Networks. CoRR Agustinus Kristiadi, Asja Fischer
2018	B	`conf`	Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge. CoNLL Debanjan Chaudhuri, Agustinus Kristiadi, Jens Lehmann, Asja Fischer
2018	J	`jnl`	Improving Response Selection in Multi-turn Dialogue Systems. CoRR Debanjan Chaudhuri, Agustinus Kristiadi, Jens Lehmann, Asja Fischer
2018	J	`jnl`	Incorporating Literals into Knowledge Graph Embeddings. CoRR Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov, Jens Lehmann, Asja Fischer