Dynamic Word Embeddings. - Robert Bamler; Stephan Mandt (ICML, 2017)

Perturbative Black Box Variational Inference. - Robert Bamler; Cheng Zhang 005; Manfred Opper; Stephan Mandt (NIPS, 2017)

Dynamic Word Embeddings via Skip-Gram Filtering. - Robert Bamler; Stephan Mandt (CoRR, 2017)

Structured Black Box Variational Inference for Latent Time Series Models. - Robert Bamler; Stephan Mandt (CoRR, 2017)

Bayesian Paragraph Vectors. - Geng Ji 001; Robert Bamler; Erik B. Sudderth; Stephan Mandt (CoRR, 2017)

Improving Optimization in Models With Continuous Symmetry Breaking. - Robert Bamler; Stephan Mandt (ICML, 2018)

Augmenting and Tuning Knowledge Graph Embeddings. - Robert Bamler; Farnood Salehi; Stephan Mandt (UAI, 2019)

A Quantum Field Theory of Representation Learning. - Robert Bamler; Stephan Mandt (CoRR, 2019)

Tightening Bounds for Variational Inference by Revisiting Perturbation Theory. - Robert Bamler; Cheng Zhang 005; Manfred Opper; Stephan Mandt (CoRR, 2019)

Extreme Classification via Adversarial Softmax Approximation. - Robert Bamler; Stephan Mandt (ICLR, 2020)

Variational Bayesian Quantization. - Yibo Yang; Robert Bamler; Stephan Mandt (ICML, 2020)

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

Improving Inference for Neural Image Compression. - Yibo Yang; Robert Bamler; Stephan Mandt (NeurIPS, 2020)

Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding. - Yibo Yang; Robert Bamler; Stephan Mandt (CoRR, 2020)

Understanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspective. - Robert Bamler (CoRR, 2022)

Trading Information between Latents in Hierarchical Variational Autoencoders. - Tim Z. Xiao; Robert Bamler (ICLR, 2023)

Resampling Gradients Vanish in Differentiable Sequential Monte Carlo Samplers. - Johannes Zenn; Robert Bamler (Tiny Papers @ ICLR, 2023)

Upgrading VAE Training With Unlimited Data Plans Provided by Diffusion Models. - Tim Z. Xiao; Johannes Zenn; Robert Bamler (CoRR, 2023)

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

Predictive, scalable and interpretable knowledge tracing on structured domains. - Hanqi Zhou; Robert Bamler; Charley M. Wu; Álvaro Tejero-Cantero (ICLR, 2024)

Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target Distribution. - Johannes Zenn; Robert Bamler (ICML, 2024)

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

A Compact Representation for Bayesian Neural Networks By Removing Permutation Symmetry. - Tim Z. Xiao; Weiyang Liu; Robert Bamler (CoRR, 2024)

On the Challenges and Opportunities in Generative AI. - Laura Manduchi; Kushagra Pandey; Robert Bamler; Ryan Cotterell; Sina Däubener; Sophie Fellenz; Asja Fischer; Thomas Gärtner 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; Christoph Lippert; et al. (CoRR, 2024)

Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian neural networks. - Tristan Cinquin; Robert Bamler (CoRR, 2024)

Balancing Molecular Information and Empirical Data in the Prediction of Physico-Chemical Properties. - Johannes Zenn; Dominik Gond; Fabian Jirasek; Robert Bamler (CoRR, 2024)

Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector. - Andi Zhang 001; Tim Z. Xiao; Weiyang Liu; Robert Bamler; Damon Wischik (AISTATS, 2025)

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

Verbalized Machine Learning: Revisiting Machine Learning with Language Models. - Tim Z. Xiao; Robert Bamler; Bernhard Schölkopf; Weiyang Liu (Trans. Mach. Learn. Res., 2025)

A Note on Generalization in Variational Autoencoders: How Effective Is Synthetic Data and Overparameterization? - Tim Z. Xiao; Johannes Zenn; Robert Bamler (Trans. Mach. Learn. Res., 2025)

Reducing Storage of Pretrained Neural Networks by Rate-Constrained Quantization and Entropy Coding. - Alexander Conzelmann; Robert Bamler (CoRR, 2025)

Flipping Against All Odds: Reducing LLM Coin Flip Bias via Verbalized Rejection Sampling. - Tim Z. Xiao; Johannes Zenn; Zhen Liu 019; Weiyang Liu; Robert Bamler; Bernhard Schölkopf (CoRR, 2025)