Bamler, Robert

23 publications

TMLR 2025 A Note on Generalization in Variational Autoencoders: How Effective Is Synthetic Data and Overparameterization? Tim Z. Xiao, Johannes Zenn, Robert Bamler

TMLR 2025 On the Challenges and Opportunities in Generative AI Laura Manduchi, Clara Meister, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin

TMLR 2025 Verbalized Machine Learning: Revisiting Machine Learning with Language Models Tim Z. Xiao, Robert Bamler, Bernhard Schölkopf, Weiyang Liu

UAI 2025 Well-Defined Function-Space Variational Inference in Bayesian Neural Networks via Regularized KL-Divergence Tristan Cinquin, Robert Bamler

ICLRW 2025 What Actually Matters for Materials Discovery: Pitfalls and Recommendations in Bayesian Optimization Tristan Cinquin, Stanley Lo, Felix Strieth-Kalthoff, Alan Aspuru-Guzik, Geoff Pleiss, Robert Bamler, Tim G. J. Rudner, Vincent Fortuin, Agustinus Kristiadi

AISTATS 2025 Your Finetuned Large Language Model Is Already a Powerful Out-of-Distribution Detector Andi Zhang, Tim Z. Xiao, Weiyang Liu, Robert Bamler, Damon Wischik

ICML 2024 Differentiable Annealed Importance Sampling Minimizes the Jensen-Shannon Divergence Between Initial and Target Distribution Johannes Zenn, Robert Bamler

NeurIPS 2024 FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning Tristan Cinquin, Marvin Pförtner, Vincent Fortuin, Philipp Hennig, Robert Bamler

ICLR 2024 Predictive, Scalable and Interpretable Knowledge Tracing on Structured Domains Hanqi Zhou, Robert Bamler, Charley M Wu, Álvaro Tejero-Cantero

ICMLW 2024 Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian Neural Networks Tristan Cinquin, Robert Bamler

ICMLW 2024 Verbalized Machine Learning: Revisiting Machine Learning with Language Models Tim Z. Xiao, Robert Bamler, Bernhard Schölkopf, Weiyang Liu

ICMLW 2024 Verbalized Machine Learning: Revisiting Machine Learning with Language Models Tim Z. Xiao, Robert Bamler, Bernhard Schölkopf, Weiyang Liu

NeurIPSW 2023 A Compact Representation for Bayesian Neural Networks by Removing Permutation Symmetry Tim Z. Xiao, Weiyang Liu, Robert Bamler

NeurIPSW 2023 The SVHN Dataset Is Deceptive for Probabilistic Generative Models Due to a Distribution Mismatch Tim Z. Xiao, Johannes Zenn, Robert Bamler

ICLR 2023 Trading Information Between Latents in Hierarchical Variational Autoencoders Tim Z. Xiao, Robert Bamler

ICLR 2020 Extreme Classification via Adversarial SoftMax Approximation Robert Bamler, Stephan Mandt

NeurIPS 2020 Improving Inference for Neural Image Compression Yibo Yang, Robert Bamler, Stephan Mandt

NeurIPS 2020 User-Dependent Neural Sequence Models for Continuous-Time Event Data Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth

ICML 2020 Variational Bayesian Quantization Yibo Yang, Robert Bamler, Stephan Mandt

UAI 2019 Augmenting and Tuning Knowledge Graph Embeddings Robert Bamler, Farnood Salehi, Stephan Mandt

ICML 2018 Improving Optimization for Models with Continuous Symmetry Breaking Robert Bamler, Stephan Mandt

ICML 2017 Dynamic Word Embeddings Robert Bamler, Stephan Mandt

NeurIPS 2017 Perturbative Black Box Variational Inference Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt