Robert Bamler - RankMe

46 papers A* 10A 3Journal 31Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A Note on Generalization in Variational Autoencoders: How Effective Is Synthetic Data and Overparameterization? Trans. Mach. Learn. Res. Tim Z. Xiao, Johannes Zenn, Robert Bamler
2025	J	`jnl`	Flipping Against All Odds: Reducing LLM Coin Flip Bias via Verbalized Rejection Sampling. CoRR Tim Z. Xiao, Johannes Zenn, Zhen Liu, Weiyang Liu, Robert Bamler, Bernhard Schölkopf
2025	J	`jnl`	On the Challenges and Opportunities in Generative AI. Trans. Mach. Learn. Res. 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 T. 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
2025	J	`jnl`	Reducing Storage of Pretrained Neural Networks by Rate-Constrained Quantization and Entropy Coding. CoRR Alexander Conzelmann, Robert Bamler
2025	J	`jnl`	Verbalized Machine Learning: Revisiting Machine Learning with Language Models. Trans. Mach. Learn. Res. Tim Z. Xiao, Robert Bamler, Bernhard Schölkopf, Weiyang Liu
2025	A	`conf`	Well-Defined Function-Space Variational Inference in Bayesian Neural Networks via Regularized KL-Divergence. UAI Tristan Cinquin, Robert Bamler
2025	A	`conf`	Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector. AISTATS Andi Zhang, Tim Z. Xiao, Weiyang Liu, Robert Bamler, Damon Wischik
2024	J	`jnl`	A Compact Representation for Bayesian Neural Networks By Removing Permutation Symmetry. CoRR Tim Z. Xiao, Weiyang Liu, Robert Bamler
2024	J	`jnl`	Balancing Molecular Information and Empirical Data in the Prediction of Physico-Chemical Properties. CoRR Johannes Zenn, Dominik Gond, Fabian Jirasek, Robert Bamler
2024	A*	`conf`	Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target Distribution. ICML Johannes Zenn, Robert Bamler
2024	J	`jnl`	Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target Distribution. CoRR Johannes Zenn, Robert Bamler
2024	A*	`conf`	FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning. NeurIPS Tristan Cinquin, Marvin Pförtner, Vincent Fortuin, Philipp Hennig, Robert Bamler
2024	J	`jnl`	FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning. CoRR Tristan Cinquin, Marvin Pförtner, Vincent Fortuin, Philipp Hennig, Robert Bamler
2024	J	`jnl`	On the Challenges and Opportunities in Generative AI. CoRR Laura Manduchi, 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 T. 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
2024	A*	`conf`	Predictive, scalable and interpretable knowledge tracing on structured domains. ICLR Hanqi Zhou, Robert Bamler, Charley M. Wu, Álvaro Tejero-Cantero
2024	J	`jnl`	Predictive, scalable and interpretable knowledge tracing on structured domains. CoRR Hanqi Zhou, Robert Bamler, Charley M. Wu, Álvaro Tejero-Cantero
2024	J	`jnl`	Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian neural networks. CoRR Tristan Cinquin, Robert Bamler
2024	J	`jnl`	Verbalized Machine Learning: Revisiting Machine Learning with Language Models. CoRR Tim Z. Xiao, Robert Bamler, Bernhard Schölkopf, Weiyang Liu
2024	J	`jnl`	Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector. CoRR Andi Zhang, Tim Z. Xiao, Weiyang Liu, Robert Bamler, Damon Wischik
2023	—	`conf`	Resampling Gradients Vanish in Differentiable Sequential Monte Carlo Samplers. Tiny Papers @ ICLR Johannes Zenn, Robert Bamler
2023	J	`jnl`	Resampling Gradients Vanish in Differentiable Sequential Monte Carlo Samplers. CoRR Johannes Zenn, Robert Bamler
2023	J	`jnl`	The SVHN Dataset Is Deceptive for Probabilistic Generative Models Due to a Distribution Mismatch. CoRR Tim Z. Xiao, Johannes Zenn, Robert Bamler
2023	A*	`conf`	Trading Information between Latents in Hierarchical Variational Autoencoders. ICLR Tim Z. Xiao, Robert Bamler
2023	J	`jnl`	Trading Information between Latents in Hierarchical Variational Autoencoders. CoRR Tim Z. Xiao, Robert Bamler
2023	J	`jnl`	Upgrading VAE Training With Unlimited Data Plans Provided by Diffusion Models. CoRR Tim Z. Xiao, Johannes Zenn, Robert Bamler
2022	J	`jnl`	Understanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspective. CoRR Robert Bamler
2020	A*	`conf`	Extreme Classification via Adversarial Softmax Approximation. ICLR Robert Bamler, Stephan Mandt
2020	J	`jnl`	Extreme Classification via Adversarial Softmax Approximation. CoRR Robert Bamler, Stephan Mandt
2020	A*	`conf`	Improving Inference for Neural Image Compression. NeurIPS Yibo Yang, Robert Bamler, Stephan Mandt
2020	J	`jnl`	Improving Inference for Neural Image Compression. CoRR Yibo Yang, Robert Bamler, Stephan Mandt
2020	A*	`conf`	User-Dependent Neural Sequence Models for Continuous-Time Event Data. NeurIPS Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth
2020	J	`jnl`	User-Dependent Neural Sequence Models for Continuous-Time Event Data. CoRR Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth
2020	J	`jnl`	Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding. CoRR Yibo Yang, Robert Bamler, Stephan Mandt
2020	A*	`conf`	Variational Bayesian Quantization. ICML Yibo Yang, Robert Bamler, Stephan Mandt
2019	J	`jnl`	A Quantum Field Theory of Representation Learning. CoRR Robert Bamler, Stephan Mandt
2019	A	`conf`	Augmenting and Tuning Knowledge Graph Embeddings. UAI Robert Bamler, Farnood Salehi, Stephan Mandt
2019	J	`jnl`	Augmenting and Tuning Knowledge Graph Embeddings. CoRR Robert Bamler, Farnood Salehi, Stephan Mandt
2019	J	`jnl`	Tightening Bounds for Variational Inference by Revisiting Perturbation Theory. CoRR Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt
2018	A*	`conf`	Improving Optimization in Models With Continuous Symmetry Breaking. ICML Robert Bamler, Stephan Mandt
2018	J	`jnl`	Improving Optimization in Models With Continuous Symmetry Breaking. CoRR Robert Bamler, Stephan Mandt
2017	J	`jnl`	Bayesian Paragraph Vectors. CoRR Geng Ji, Robert Bamler, Erik B. Sudderth, Stephan Mandt
2017	J	`jnl`	Dynamic Word Embeddings via Skip-Gram Filtering. CoRR Robert Bamler, Stephan Mandt
2017	A*	`conf`	Dynamic Word Embeddings. ICML Robert Bamler, Stephan Mandt
2017	—	`conf`	Perturbative Black Box Variational Inference. NIPS Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt
2017	J	`jnl`	Perturbative Black Box Variational Inference. CoRR Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt
2017	J	`jnl`	Structured Black Box Variational Inference for Latent Time Series Models. CoRR Robert Bamler, Stephan Mandt