Publication List
In the field of machine learning, publishing in conferences is the norm. Top conferences includes NeurIPS (prev. NIPS), ICML, ICLR, AISTATS, UAI, and others. They are peer-reviewed and highly competitive.
The first or joint-first author (the latter is marked with a '*') of a paper is the lead author. The last or joint-last author is usually the one who came up with the idea and directed the project. They are all considered to be the core authors of the paper.
Conference
- Towards Cost-Effective Reward Guided Text GenerationAhmad Rashid, Ruotian Wu, Rongqi Fan, Hongliang Li, Agustinus Kristiadi, Pascal Poupart
- Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AITheodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, 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 Ruegammer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
- Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU NetworksAgustinus Kristiadi, Matthias Hein, Philipp Hennig
Journal
Workshop
- What Actually Matters for Materials Discovery: Pitfalls and Recommendations in Bayesian OptimizationTristan Cinquin, Stanley Lo, Felix Strieth-Kalthoff, Alan Aspuru-Guzik, Geoff Pleiss, Robert Bamler, Tim G. J. Rudner, Vincent Fortuin, Agustinus Kristiadi
- A Critical Look At Tokenwise Reward-Guided Text GenerationAhmad Rashid, Ruotian Wu, Julia Grosse, Agustinus Kristiadi*, Pascal Poupart*
- If Optimizing for General Parameters in Chemistry Is Useful, Why Is It Hardly Done?Stefan Schmid, Ella Rajaonson, Cher-Tian Ser, Mohammad Haddadnia, Shi Xuan Leong, Alan Aspuru-Guzik, Agustinus Kristiadi, Kjell Jorner, Felix Strieth-Kalthoff
- Dimension Deficit: Is 3D a Step Too Far for Optimizing Molecules?Andres Guzman Cordero, Luca Thiede, Gary Tom, Alan Aspuru-Guzik, Felix Strieth-Kalthoff, Agustinus Kristiadi
- Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep LearningRuna Eschenhagen, Erik Daxberger, Philipp Hennig, Agustinus Kristiadi
Preprint
- Position: Curvature Matrices Should Be Democratized via Linear OperatorsFelix Dangel, Runa Eschenhagen, Weronika Ormaniec, Andres Fernandez, Lukas Tatzel, Agustinus Kristiadi
- Simplifying Bayesian Optimization Via In-Context Direct Optimum SamplingGustavo Sutter, Mohammed Abdulrahman, Hao Wang, Sriram Ganapathi Subramanian, Marc St-Aubin, Sharon O'Sullivan, Lawrence Wan, Luis Ricardez-Sandoval, Pascal Poupart, Agustinus Kristiadi
- FlashMD: Long-Stride, Universal Prediction of Molecular DynamicsFilippo Bigi, Sanggyu Chong, Agustinus Kristiadi, Michele Ceriotti
- Efficient Weight-Space Laplace-Gaussian Filtering and Smoothing for Sequential Deep LearningJoanna Sliwa, Frank Schneider, Nathanael Bosch, Agustinus Kristiadi, Philipp Hennig
- On the Disconnect Between Theory and Practice of Overparametrized Neural NetworksJonathan Wenger, Felix Dangel, Agustinus Kristiadi