L. Theis. What makes an image realistic? Proceedings of the 41st International Conference on Machine Learning, 2024.

H. Kim, M. Bauer, L. Theis, J. R. Schwarz, and E. Dupont. C3: High-performance and low-complexity neural compression from a single image or video. Computer Vision and Pattern Recognition, 2024.

D. Severo, L. Theis, and J. Ballé. The Unreasonable Effectiveness of Linear Prediction as a Perceptual Metric. International Conference on Learning Representations, 2024.

E. Hoogeboom, E. Agustsson, F. Mentzer, L. Versari, G. Toderici, and L. Theis. High-Fidelity Image Compression with Score-based Generative Models. 2023.

G. Flamich and L. Theis. Adaptive Greedy Rejection Sampling. IEEE International Symposium on Information Theory, 2023.

Y. Yang, S. Mandt, and L. Theis. An Introduction to Neural Data Compression. Foundations and Trends in Computer Graphics and Vision, volume 15, issue 2, pages 113-200, 2023.

L. Theis, T. Salimans, M. D. Hoffman, and F. Mentzer. Lossy Compression with Gaussian Diffusion. 2022.

A. Shah, W.-N. Chen, J. Balle, P. Kairouz, and L. Theis. Optimal Compression of Locally Differentially Private Mechanisms. Artificial Intelligence and Statistics, 2022.

L. Theis and N. Yosri. Algorithms for the Communication of Samples. Proceedings of the 39th International Conference on Machine Learning, 2022.

L. Theis and J. Ho. Importance weighted compression. Neural Compression Workshop at ICLR, 2021.

L. Theis and A. B. Wagner. A coding theorem for the rate-distortion-perception function. Neural Compression Workshop at ICLR, 2021.

L. Theis and E. Agustsson. On the advantages of stochastic encoders. Neural Compression Workshop at ICLR, 2021.

E. Agustsson and L. Theis. Universally Quantized Neural Compression. Advances in Neural Information Processing Systems 33, 2020.

I. Korshunova, H. Xiong, M. Fedoryszak, and L. Theis. Discriminative Topic Modeling with Logistic LDA. Advances in Neural Information Processing Systems 33, 2019.

T. Nguyen-Phuoc, C. Li, L. Theis, C. Richardt, and Y.-L. Yang. HoloGAN: Unsupervised learning of 3D representations from natural images. International Conference on Computer Vision, 2019.

K. Storrs, S. V. Leuven, S. Kojder, L. Theis, and F. Huszár. Adaptive Paired-Comparison Method for Subjective Video Quality Assessment on Mobile Devices. Picture Coding Symposium, 2018.

L. Theis, I. Korshunova, A. Tejani, and F. Huszár. Faster gaze prediction with dense networks and Fisher pruning. 2018.

I. Korshunova, W. Shi, J. Dambre, and L. Theis. Fast Face-swap Using Convolutional Neural Networks. International Conference on Computer Vision, October 2017.

C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. Computer Vision and Pattern Recognition, July 2017.

L. Theis, W. Shi, A. Cunningham, and F. Huszár. Lossy Image Compression with Compressive Autoencoders. International Conference on Learning Representations, 2017.

C. Sønderby, J. Caballero, L. Theis, W. Shi, and F. Huszár. Amortised MAP Inference for Image Super-resolution. International Conference on Learning Representations, 2017.

L. Theis, P. Berens, E. Froudarakis, J. Reimer, M. Roman-Roson, T. Baden, T. Euler, A. S. Tolias, and M. Bethge. Benchmarking spike rate inference in population calcium imaging. Neuron, volume 90, issue 3, pages 471-482, May 2016.

L. Theis, A. van den Oord, and M. Bethge. A note on the evaluation of generative models. International Conference on Learning Representations, April 2016.

L. Theis. Advances in Probabilistic Modeling of Natural Images. 2016.

L. Theis and M. Bethge. Generative Image Modeling Using Spatial LSTMs. Advances in Neural Information Processing Systems 28, December 2015.

L. Theis and M. D. Hoffman. A trust-region method for stochastic variational inference with applications to streaming data. Proceedings of the 32nd International Conference on Machine Learning, July 2015.

M. Kümmerer, L. Theis, and M. Bethge. Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet. ICLR Workshop, June 2015.

H. E. Gerhard, L. Theis, and M. Bethge. Modeling Natural Image Statistics. Biologically-inspired Computer Vision—Fundamentals and Applications, Wiley VCH, 2015.

S. Sra, R. Hosseini, L. Theis, and M. Bethge. Data modeling with the elliptical gamma distribution. Artificial Intelligence and Statistics, volume 18, 2015.

A. M. Chagas, L. Theis, B. Sengupta, M. Stüttgen, M. Bethge, and C. Schwarz. Functional analysis of ultra high information rates conveyed by rat vibrissal primary afferents. Frontiers in Neural Circuits, volume 7, issue 190, December 2013.

L. Theis, A. M. Chagas, D. Arnstein, C. Schwarz, and M. Bethge. Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification. PLoS Computational Biology, volume 9, issue 11, November 2013.

L. Theis, J. Sohl-Dickstein, and M. Bethge. Training sparse natural image models with a fast Gibbs sampler of an extended state space. Advances in Neural Information Processing Systems 25, November 2012.

L. Theis, R. Hosseini, and M. Bethge. Mixtures of Conditional Gaussian Scale Mixtures Applied to Multiscale Image Representations. PLoS ONE, Public Library of Science, volume 7, issue 7, July 2012.

L. Theis, S. Gerwinn, F. Sinz, and M. Bethge. In All Likelihood, Deep Belief Is Not Enough. Journal of Machine Learning Research, volume 12, November 2011.