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<body>
<div class="sidebar sticky">
<div class="fade">
<div class="author_avatar"><img src="robert-bamler.jpg" class="author_avatar" alt="Robert Bamler"></div>
<div class="author_content">
<div class="author_name">Robert Bamler</div>
<p class="author_bio">Professor of Data Science and Machine Learning at University of Tübingen, Germany, and
member of the Cluster of Excellence "Machine Learning: New Perspectives for Science".</p>
</div>
</div>
<ul class="social-links">
<li><a class="icon email" href="mailto:robert.bamler-guessthischaracter-uni-tuebingen.de">Email</a>
</li>
<li><a class="icon googlescholar" href="https://scholar.google.com/citations?user=LwvdNAgAAAAJ">Google
Scholar</a></li>
<li><a class="icon github" href="https://github.com/robamler">Github</a>
</li>
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<article class="page">
<header>
<h1 class="page_title">Robert Bamler</h1>
</header>
<section class="page_content">
<p>
<a href="https://uni-tuebingen.de" class='ut-logo'><img src='ut-logo.png' alt='University of Tübingen'></a>
I am a Professor of Data Science and Machine Learning at <a href="https://uni-tuebingen.de/">University of
Tübingen</a>, Germany, and a member of the <a
href="https://uni-tuebingen.de/en/research/core-research/cluster-of-excellence-machine-learning/home/">Cluster
of Excellence "Machine Learning: New Perspectives for Science"</a> and <a
href="https://tuebingen.ai/">Tübingen AI Center</a>.
</p>
<p>
<a href="https://bamler-lab.github.io/">My group</a> performs research on scalable approximate Bayesian
inference methods (especially variational methods)
and on deep probabilistic models, with applications to neural data compression and natural sciences.
Our research is at the intersection of probabilistic models, efficient inference algorithms, and novel powerful
approximation techniques.
The latter are often inspired by ideas from natural sciences due to my personal background
in statistical physics.
</p>
<p>
<strong>Find out more about my group</strong> at our <a href="https://bamler-lab.github.io/">group website</a>.
</p>
<p>
I joined University of Tübingen and the Cluster of Excellence for Machine Learning in November 2020.
Before this, I was a postdoctoral scholar in the statistical machine learning group of UC Irvine lead by <a
href="http://stephanmandt.com/">Stephan Mandt</a>, and before that I was a machine learning researcher at <a
href="https://www.disneyresearch.com/">Disney Research</a> in Pittsburgh and Los Angeles.
I received my PhD in theoretical statistical and quantum physics from <a
href="http://www.thp.uni-koeln.de/">University of Cologne</a> in 2016, advised by <a
href="http://www.thp.uni-koeln.de/rosch/A_Rosch.html">Achim Rosch</a> and with support from <a
href="https://www.telekom-stiftung.de/en">German Telekom Foundation</a>.
</p>
<ul class="sitemap">
<li><a class="icon down" href="#publications">Machine Learning Publications</a></li>
<li><a class="icon down" href="#physics-publications">Physics Publications</a></li>
<li><a class="icon down" href="#talks">Talks & Video Abstracts</a></li>
</ul>
<h2 id="publications">Machine Learning Publications</h2>
<p>
<strong>Representative publications</strong> include
<a class="inline-icon pdf" href="files/bamler-pbbvi-nips-2017.pdf">improvements to black box variational
inference (BBVI)</a> and applications of BBVI to
<a class="inline-icon pdf" href="files/yang-vbq-icml-2020.pdf">neural data compression</a>, to
<a class="inline-icon pdf" href="files/bamler-dynamic-word-embeddings-icml-2017.pdf">time series models</a>, and
to
<a class="inline-icon pdf" href="files/jirasek-ml-in-td-2020.pdf">natural sciences</a>, as well as
<a class="inline-icon pdf" href="files/bamler-goldstone-gd-icml-2018.pdf">resource efficient learning
algorithms</a>.
For more publications, see list below.
</p>
<ul>
<li>
<div class="item_title">Verbalized Machine Learning: Revisiting Machine Learning with Language Models</div>
<div class="paper_authors">T. Z. Xiao, R. Bamler, B. Schölkopf, W. Liu</div>
<div class="venue">TMLR 2025 (accepted)</div>
<a class="icon pdf" href="files/xiao-verbalized-ml-tmlr-2025.pdf">PDF</a>
</li>
<li>
<div class="item_title">Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector
</div>
<div class="paper_authors">A. Zhang, T. Z. Xiao, W. Liu, R. Bamler, and
D. Wischik</div>
<div class="venue">AISTATS 2025 (accepted)</div>
<a class="icon pdf" href="files/zhang-finetuned-llm-ood-detector-aistats-2025.pdf">PDF</a>
</li>
<li>
<div class="item_title">Balancing Molecular Information and Empirical Data in the Prediction of
Physico-Chemical Properties</div>
<div class="paper_authors">J. Zenn, D. Gond, F. Jirasek, and R. Bamler</div>
<div class="venue">Digital Discovery, 2025</div>
<a class="icon pdf" href="files/zenn-ml-in-td-variational-em.pdf">PDF</a>
<a class="icon external" target="_blank"
href="https://pubs.rsc.org/en/content/articlehtml/2025/dd/d4dd00154k">Journal Website</a>
</li>
<li>
<div class="item_title">A Note on Generalization in Variational Autoencoders:
How Effective Is Synthetic Data & Overparameterization?</div>
<div class="paper_authors">T. Xiao, J. Zenn, and R. Bamler</div>
<div class="venue">TMLR, 2024</div>
<a class="icon pdf" href="files/xiao-zenn-generalization-in-vaes-tmlr-2024.pdf">PDF</a>
</li>
<li>
<div class="item_title">FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep
Learning</div>
<div class="paper_authors">T. Cinquin, M. Pförtner, V. Fortuin, P. Hennig, and
R. Bamler</div>
<div class="venue">NeurIPS 2024</div>
<a class="icon pdf" href="files/cinquin-fsp-laplace-neurips-2024.pdf">PDF</a>
</li>
<li>
<div class="item_title">Predictive, scalable and interpretable knowledge tracing on structured domains</div>
<div class="paper_authors">H. Zhou, R. Bamler, C. M. Wu, and Á. Tejero-Cantero</div>
<div class="venue">ICLR 2024</div>
<a class="icon pdf" href="files/zhou-knowledge-tracing-iclr-2024.pdf">PDF</a>
</li>
<li>
<div class="item_title">Differentiable Annealed Importance Sampling Minimizes The Jensen-Shannon Divergence
Between Initial and Target Distribution</div>
<div class="paper_authors">J. Zenn and R. Bamler</div>
<div class="venue">ICML 2024</div>
<a class="icon pdf" href="files/zenn-dais-icml-2024.pdf">PDF</a>
</li>
<li>
<div class="item_title">A Compact Representation for Bayesian Neural Networks By Removing Permutation
Symmetry</div>
<div class="paper_authors">T. Xiao and R. Bamler</div>
<div class="venue">NeurIPS 2023 Workshop on Unifying Representations in Neural Models</div>
<a class="icon pdf" href="files/xiao-permutations-neurips-2023.pdf">PDF</a>
</li>
<li>
<div class="item_title">The SVHN Dataset Is Deceptive for Probabilistic Generative Models Due to a
Distribution Mismatch</div>
<div class="paper_authors">T. Z. Xiao, J. Zenn*, and R. Bamler</div>
<div class="venue">NeurIPS 2023 Workshop on Distribution Shifts</div>
<a class="icon pdf" href="files/xiao-zenn-svhn-neurips-2023.pdf">PDF</a>
</li>
<li>
<div class="item_title">Resampling Gradients Vanish in Differentiable Sequential Monte Carlo Samplers</div>
<div class="paper_authors">J. Zenn and R. Bamler</div>
<div class="venue">ICLR 2023 (TinyPapers track)</div>
<a class="icon pdf" href="files/zenn-resampling-gradients-iclr-2023.pdf">PDF</a>
</li>
<li>
<div class="item_title">Trading Information between Latents in Hierarchical Variational Autoencoders</div>
<div class="paper_authors">T. Z. Xiao and R. Bamler</div>
<div class="venue">ICLR 2023</div>
<a class="icon pdf" href="files/xiao-rd-vaes-iclr-2023.pdf">PDF</a>
</li>
<li>
<div class="item_title">Post-Training Neural Network Compression With Variational Bayesian Quantization</div>
<div class="paper_authors">Z. Tan and R. Bamler</div>
<div class="venue">NeurIPS 2022 Workshop on Challenges in
Deploying and Monitoring Machine Learning Systems</div>
<a class="icon pdf" href="files/tan-network-compression-2022.pdf">PDF</a>
</li>
<li>
<div class="item_title">Making thermodynamic models of mixtures predictive by machine learning: matrix
completion of pair interactions</div>
<div class="paper_authors">F. Jirasek, R. Bamler, S. Fellenz, M. Bortz, M. Kloft,
S. Mandt, and H. Hasse</div>
<div class="venue">Chemical Science 13 (17), 4854-4862</div>
<a class="icon pdf" href="files/jirasek-ml-in-td-parametric-2022.pdf">PDF</a>
</li>
<li>
<div class="item_title">Improving Inference for Neural Image Compression</div>
<div class="paper_authors">Y. Yang, R. Bamler, and S. Mandt</div>
<div class="venue">NeurIPS 2020</div>
<a class="icon pdf"
href="files/yang-improving-inference-for-neural-image-compression-neurips-2020.pdf">PDF</a>
</li>
<li>
<div class="item_title">User-Dependent Neural Sequence Models for Continuous-Time Event Data</div>
<div class="paper_authors">A. Boyd, R. Bamler, S. Mandt, P. Smyth</div>
<div class="venue">NeurIPS 2020</div>
<a class="icon pdf" href="files/boyd-user-dependent-npps-neurips-2020.pdf">PDF</a>
</li>
<li>
<div class="item_title">
Hybridizing physical and data-driven prediction methods for physicochemical properties</div>
<div class="paper_authors">F. Jirasek, R. Bamler, S. Mandt</div>
<div class="venue">Chemical Communications, 2020</div>
<a class="icon pdf" href="files/jirasek-hybrid-ml-in-td-2020.pdf">PDF</a>
<a class="icon external" target="_blank"
href="https://pubs.rsc.org/en/content/articlelanding/2020/cc/d0cc05258b/unauth">Journal Website</a>
</li>
<li>
<div class="item_title">Variational Bayesian Quantization</div>
<div class="comment">(former title: Variable-Bitrate Neural Compression via Bayesian Arithmetic Coding)</div>
<div class="paper_authors">Y. Yang, R. Bamler, and S. Mandt</div>
<div class="venue">ICML 2020</div>
<a class="icon pdf" href="files/yang-vbq-icml-2020.pdf">PDF</a>
<a class="icon video" target="_blank" href="https://icml.cc/virtual/2020/poster/6764">Vide abstract by Yibo
Yang (15 min)</a>
</li>
<li>
<div class="item_title">Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix
Completion</div>
<div class="paper_authors">F. Jirasek, R. A. S. Alves, J. Damay,
R. A. Vandermeulen, R. Bamler, M. Bortz, S. Mandt, M. Kloft,
H. Hasse</div>
<div class="venue">The Journal of Physical Chemistry Letters, 2020(11) 981-985</div>
<a class="icon pdf" href="files/jirasek-ml-in-td-2020.pdf">PDF</a>
<a class="icon external" target="_blank"
href="https://pubs.acs.org/doi/full/10.1021/acs.jpclett.9b03657">Journal website</a>
</li>
<li>
<div class="item_title">Extreme Classification via Adversarial Softmax Approximation</div>
<div class="paper_authors">R. Bamler and S. Mandt</div>
<div class="venue">ICLR 2020</div>
<a class="icon pdf" href="files/bamler-mandt-adversarial-neg-sampling-iclr2020.pdf">PDF</a>
<a class="icon video" href="#" onclick="toggle_video(event)">Video abstract (5 min.)</a>
<a class="icon slides" target="_blank"
href="https://docs.google.com/presentation/d/1-Hfu8JXGgVtVrfEl3t992divXAn0sr4BjwViF5ZwUWQ/edit?usp=sharing">Slides</a>
<div class="videocontainer">
<video controls poster="videos/bamler-mandt-adversarial-neg-sampling-iclr2020.png">
<source src="videos/bamler-mandt-adversarial-neg-sampling-iclr2020.webm" type="video/webm">
<source src="videos/bamler-mandt-adversarial-neg-sampling-iclr2020.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
<a class="icon videolink" href="videos/bamler-mandt-adversarial-neg-sampling-iclr2020.mp4">Link to
video</a>
</div>
</li>
<li>
<div class="item_title">Tightening Bounds for Variational Inference by Revisiting Perturbation
Theory</div>
<div class="paper_authors">R. Bamler, C. Zhang, M. Opper, and S. Mandt</div>
<div class="venue">J. Stat. Mech. (2019) 124004</div>
<a class="icon pdf" href="files/bamler-vi-by-revisiting-perturbation-theory-jstat2019.pdf">PDF</a>
<a class="icon external" target="_blank"
href="https://iopscience.iop.org/article/10.1088/1742-5468/ab43d3">Journal website</a>
</li>
<li>
<div class="item_title">Augmenting and Tuning Knowledge Graph Embeddings</div>
<div class="paper_authors">R. Bamler, F. Salehi, and S. Mandt</div>
<div class="venue">UAI 2019</div>
<a class="icon pdf" href="files/bamler-salehi-mandt-tuning-kges-uai2019.pdf">PDF</a>
<a class="icon video" href="#" onclick="toggle_video(event)">Video abstract (1 min.)</a>
<a class="icon slides" target="_blank"
href="https://docs.google.com/presentation/d/1VvYSv9UjuWCWaav9BWg7SfQPrSxtBAkD5LysKa1thjI/edit?usp=sharing">Slides</a>
<div class="videocontainer">
<video controls poster="videos/bamler-salehi-mandt-tuning-kges-uai2019.png">
<source src="videos/bamler-salehi-mandt-tuning-kges-uai2019.webm" type="video/webm">
<source src="videos/bamler-salehi-mandt-tuning-kges-uai2019.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
<a class="icon videolink" href="videos/bamler-salehi-mandt-tuning-kges-uai2019.mp4">Link to video</a>
</div>
</li>
<li>
<div class="item_title">Improving Optimization for Models With Continuous Symmetry Breaking</div>
<div class="paper_authors">R. Bamler and S. Mandt</div>
<div class="venue">ICML 2018</div>
<a class="icon pdf" href="files/bamler-goldstone-gd-icml-2018.pdf">PDF</a>
<a class="icon video" href="#" onclick="toggle_video(event)">Related video abstract (4 min.)</a>
<a class="icon slides" target="_blank"
href="https://docs.google.com/presentation/d/1dg5P97Yo1VHygL1Rgw62aowF4_lTNDY7eGeatIjfO2Y/edit?usp=sharing">Slides</a>
<div class="videocontainer">
<video controls poster="videos/bamler-mandt-qft-of-representation-learning.png">
<source src="videos/bamler-mandt-qft-of-representation-learning.webm" type="video/webm">
<source src="videos/bamler-mandt-qft-of-representation-learning.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
<a class="icon videolink" href="videos/bamler-mandt-qft-of-representation-learning.mp4">Link to
video</a>
</div>
</li>
<li>
<div class="item_title">Perturbative Black Box Variational Inference</div>
<div class="paper_authors">R. Bamler, C. Zhang, M. Opper, and S. Mandt</div>
<div class="venue">NeurIPS 2017</div>
<a class="icon pdf" href="files/bamler-pbbvi-nips-2017.pdf">PDF</a>
</li>
<li>
<div class="item_title">Bayesian Paragraph Vectors</div>
<div class="paper_authors">G. Ji, R. Bamler, E. B. Sudderth, S. Mandt</div>
<div class="venue">NeurIPS 2017 workshop on Advances in Approximate Bayesian Inference</div>
<a class="icon pdf" href="files/ji-bayesian-paragraph-vectors-aabi-2017.pdf">PDF</a>
</li>
<li>
<div class="item_title">Dynamic Word Embeddings</div>
<div class="paper_authors">R. Bamler and S. Mandt</div>
<div class="venue">ICML 2017</div>
<a class="icon pdf" href="files/bamler-dynamic-word-embeddings-icml-2017.pdf">PDF</a>
<a class="icon video" target="_blank" href="https://youtu.be/2uQ6bgemuLw">Promotional video
(3 min.)</a>
<a class="icon video" target="_blank" href="https://vimeo.com/240776794">Conference talk
(17 min.)</a>
</li>
</ul>
<h2 id="physics-publications">Physics Publications</h2>
<ul>
<li>
<div class="item_title">Phase-Space Berry Phases in Chiral Magnets: Skyrmion Charge, Hall Effect, and
Dynamics of Magnetic Skyrmions</div>
<div class="paper_authors">R. Bamler</div>
<div class="venue">Ph.D. thesis, University of Cologne (2016)</div>
<a class="icon pdf" href="files/thesis-robert-bamler.pdf">PDF</a>
</li>
<li>
<div class="item_title">Equilibration and approximate conservation laws: Dipole oscillations and perfect
drag
of ultracold atoms in a harmonic trap</div>
<div class="paper_authors">R. Bamler and A. Rosch</div>
<div class="venue">Phys. Rev. A 91, 063604 (2015)</div>
<a class="icon pdf" href="files/bamler-equilibration-pra-2015.pdf">PDF</a>
</li>
<li>
<div class="item_title">Phase-space Berry phases in chiral magnets: Dzyaloshinskii-Moriya interaction and
the
charge of skyrmions</div>
<div class="paper_authors">F. Freimuth, R. Bamler, Y. Mokrousov, and A. Rosch</div>
<div class="venue">Phys. Rev. B 88, 214409 (2013)</div>
<a class="icon pdf" href="files/freimuth-skyrmion-charge-prb-2013.pdf">PDF</a>
</li>
<li>
<div class="item_title">Giant generic topological Hall resistivity of MnSi under pressure</div>
<div class="paper_authors">R. Ritz, M. Halder, C. Franz, A. Bauer, M. Wagner,
R. Bamler, A. Rosch, and C. Pfleiderer</div>
<div class="venue">Phys. Rev. B 87, 134424 (2013)</div>
<a class="icon pdf" href="files/ritz-giant-prb-2013.pdf">PDF</a>
</li>
<li>
<div class="item_title">Hydrodynamic Object Recognition: When Multipoles Count</div>
<div class="paper_authors">A. B. Sichert, R. Bamler, and J. L. van Hemmen</div>
<div class="venue">Phys. Rev. Lett. 102, 058104 (2009)</div>
<a class="icon pdf" href="files/sichert-multipoles-prl-2009.pdf">PDF</a>
</li>
</ul>
<h2 id="talks">Talks & Video Abstracts</h2>
<p>
Below is a selection of talks and video abstracts where either recordings or slides are available.
</p>
<ul>
<li>
<div class="item_title">Scalable Bayesian Inference: New Tools for New Challenges</div>
<div class="venue">ml4science Cluster Colloquium, University of Tübingen, Germany, 2/2021</div>
<a class="icon video" target="_blank" href="https://www.youtube.com/watch?v=1y7DdVSoO8E">Video recording
(50 min.)</a>
<a class="icon slides" target="_blank"
href="https://docs.google.com/presentation/d/1mYX8zc7W-pYhhQAUYLyplzBU7aF0rf5R1yRKMoFuy0w/edit">Slides</a>
</li>
<li>
<div class="item_title">Extreme Classification via Adversarial Softmax Approximation</div>
<div class="paper_authors">R. Bamler and S. Mandt</div>
<div class="venue">ICLR 2020</div>
<a class="icon video" href="#" onclick="toggle_video(event)">Video abstract (5 min.)</a>
<a class="icon slides" target="_blank"
href="https://docs.google.com/presentation/d/1-Hfu8JXGgVtVrfEl3t992divXAn0sr4BjwViF5ZwUWQ/edit?usp=sharing">Slides</a>
<a class="icon pdf" href="files/bamler-mandt-adversarial-neg-sampling-iclr2020.pdf">Paper (PDF)</a>
<div class="videocontainer">
<video controls poster="videos/bamler-mandt-adversarial-neg-sampling-iclr2020.png">
<source src="videos/bamler-mandt-adversarial-neg-sampling-iclr2020.webm" type="video/webm">
<source src="videos/bamler-mandt-adversarial-neg-sampling-iclr2020.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
<a class="icon videolink" href="videos/bamler-mandt-adversarial-neg-sampling-iclr2020.mp4">Link to
video</a>
</div>
</li>
<li>
<div class="item_title">Revisiting Variational Expectation Maximization</div>
<div class="venue">AI/ML Seminar @ UC Irvine, 2019</div>
<a class="icon slides" target="_blank"
href="https://docs.google.com/presentation/d/1boT0tGSoWIAVu4_YW2Dg9R7hT_FEJmNJofIRyn5QaFM/edit?usp=sharing">Slides</a>
<a class="icon pdf" href="files/bamler-salehi-mandt-tuning-kges-uai2019.pdf">Related paper 1 (PDF)</a>