Robert Bamler
I am a machine-learning researcher working on AI infrastructure (specifically model compression) and
machine-learning methods for video compression.
I just finished a position as a professor of data science and machine learning at University of Tübingen, Germany (since November 2020), and I'm about to start a new position in the MPEG-AI team at Nokia in Munich. Before this, I was a postdoctoral scholar in the statistical machine learning group of UC Irvine lead by Stephan Mandt, and before that I was a machine learning researcher at Disney Research (a part of Walt Disney Imagineering) in Pittsburgh and Los Angeles. I received my PhD in theoretical statistical and quantum physics from University of Cologne in 2016, advised by Achim Rosch and with support from German Telekom Foundation.
Machine Learning Publications
Representative publications include compression of deep-learning models and image compression with deep-learning models, as well as improvements to black box variational inference (BBVI) and its applications to natural sciences and to time series models. For more publications, see list below.
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Reducing Storage of Pretrained Neural Networks by Rate-Constrained Quantization and Entropy CodingarXiv:2505.1875 (under review)PDF
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Flipping Against All Odds: Reducing LLM Coin Flip Bias via Verbalized Rejection SamplingarXiv:2506.09998 (under review)PDF
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Well-Defined Function-Space Variational Inference in Bayesian Neural Networks via Regularized KL-DivergenceUAI 2025 (accepted)PDF
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What Actually Matters for Materials Discovery: Pitfalls and Recommendations in Bayesian OptimizationAI for Accelerated Materials Design workshop at ICLR 2025PDF
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Verbalized Machine Learning: Revisiting Machine Learning with Language ModelsTMLR 2025PDF
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Your Finetuned Large Language Model is Already a Powerful Out-of-distribution DetectorAISTATS 2025 (accepted)PDF
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Balancing Molecular Information and Empirical Data in the Prediction of Physico-Chemical PropertiesDigital Discovery, 2025PDF Journal Website
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A Note on Generalization in Variational Autoencoders: How Effective Is Synthetic Data & Overparameterization?TMLR, 2024PDF
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FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep LearningNeurIPS 2024PDF
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Predictive, scalable and interpretable knowledge tracing on structured domainsICLR 2024PDF
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Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence Between Initial and Target DistributionICML 2024PDF
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A Compact Representation for Bayesian Neural Networks By Removing Permutation SymmetryNeurIPS 2023 Workshop on Unifying Representations in Neural ModelsPDF
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The SVHN Dataset Is Deceptive for Probabilistic Generative Models Due to a Distribution MismatchNeurIPS 2023 Workshop on Distribution ShiftsPDF
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Resampling Gradients Vanish in Differentiable Sequential Monte Carlo SamplersICLR 2023 (TinyPapers track)PDF
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Trading Information between Latents in Hierarchical Variational AutoencodersICLR 2023PDF
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Post-Training Neural Network Compression With Variational Bayesian QuantizationNeurIPS 2022 Workshop on Challenges in Deploying and Monitoring Machine Learning SystemsPDF
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Making thermodynamic models of mixtures predictive by machine learning: matrix completion of pair interactionsChemical Science 13 (17), 4854-4862PDF
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Improving Inference for Neural Image CompressionNeurIPS 2020PDF
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User-Dependent Neural Sequence Models for Continuous-Time Event DataNeurIPS 2020PDF
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Hybridizing physical and data-driven prediction methods for physicochemical propertiesChemical Communications, 2020PDF Journal Website
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Variational Bayesian Quantization(former title: Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding)ICML 2020PDF Video abstract by Yibo Yang (15 min)
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Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix CompletionThe Journal of Physical Chemistry Letters, 2020(11) 981-985PDF Journal website
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Extreme Classification via Adversarial Softmax ApproximationICLR 2020PDF Video abstract (5 min.) Slides
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Tightening Bounds for Variational Inference by Revisiting Perturbation TheoryJ. Stat. Mech. (2019) 124004PDF Journal website
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Augmenting and Tuning Knowledge Graph EmbeddingsUAI 2019PDF Video abstract (1 min.) Slides
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Improving Optimization for Models With Continuous Symmetry BreakingICML 2018PDF Related video abstract (4 min.) Slides
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Perturbative Black Box Variational InferenceNeurIPS 2017PDF
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Bayesian Paragraph VectorsNeurIPS 2017 workshop on Advances in Approximate Bayesian InferencePDF
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Dynamic Word EmbeddingsICML 2017PDF Promotional video (3 min.) Conference talk (17 min.)
Physics Publications
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Phase-Space Berry Phases in Chiral Magnets: Skyrmion Charge, Hall Effect, and Dynamics of Magnetic SkyrmionsPh.D. thesis, University of Cologne (2016)PDF
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Equilibration and approximate conservation laws: Dipole oscillations and perfect drag of ultracold atoms in a harmonic trapPhys. Rev. A 91, 063604 (2015)PDF
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Phase-space Berry phases in chiral magnets: Dzyaloshinskii-Moriya interaction and the charge of skyrmionsPhys. Rev. B 88, 214409 (2013)PDF
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Giant generic topological Hall resistivity of MnSi under pressurePhys. Rev. B 87, 134424 (2013)PDF
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Hydrodynamic Object Recognition: When Multipoles CountPhys. Rev. Lett. 102, 058104 (2009)PDF
Selected Talks & Video Abstracts
Unfortunately, video recordings or slides are publicly available only for a few of my past talks. If you have any questions about a talk that's not in the list below (or if the slides don't resolve your question), please feel free to contact me.
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It’s Not Just ChatGPT: Underappreciated Machine-Learning Methods for the Natural SciencesCITENI, Universidade da Coruña, Spain, 9/2025Slides
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Scalable Bayesian Inference: New Tools for New Challengesml4science Cluster Colloquium, University of Tübingen, Germany, 2/2021Video recording (50 min.) Slides
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Extreme Classification via Adversarial Softmax ApproximationICLR 2020Video abstract (5 min.) Slides Paper (PDF)
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Revisiting Variational Expectation MaximizationAI/ML Seminar @ UC Irvine, 2019Slides Related paper 1 (PDF) Related paper 2 (PDF)
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Marrying Big Data And Probabilistic Models With Variational InferenceGerman Aerospace Center (DLR), 2019Slides
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Augmenting and Tuning Knowledge Graph EmbeddingsUAI 2019Video abstract (1 min.) Slides Paper (PDF)
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A Quantum Field Theory of Representation LearningTheoretical Physics for Deep Learning Workshop at ICML 2019Video abstract (4 min.) Slides Workshop paper (PDF) Related paper (PDF)
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Dynamic Word EmbeddingsICML 2017Video recording (17 min.) Paper (PDF)