Sub-sampled Cubic Regularization for Non-convex Optimization. - Jonas Moritz Kohler; Aurélien Lucchi (ICML, 2017)

Escaping Saddles with Stochastic Gradients. - Hadi Daneshmand; Jonas Moritz Kohler; Aurélien Lucchi; Thomas Hofmann 001 (ICML, 2018)

Towards a Theoretical Understanding of Batch Normalization. - Jonas Moritz Kohler; Hadi Daneshmand; Aurélien Lucchi; Ming Zhou 007; Klaus Neymeyr; Thomas Hofmann 001 (CoRR, 2018)

Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization. - Jonas Moritz Kohler; Hadi Daneshmand; Aurélien Lucchi; Thomas Hofmann 001; Ming Zhou 007; Klaus Neymeyr (AISTATS, 2019)

The Role of Memory in Stochastic Optimization. - Antonio Orvieto; Jonas Kohler; Aurélien Lucchi (UAI, 2019)

Ellipsoidal Trust Region Methods and the Marginal Value of Hessian Information for Neural Network Training. - Leonard Adolphs; Jonas Kohler; Aurélien Lucchi (CoRR, 2019)

A Stochastic Tensor Method for Non-convex Optimization. - Aurélien Lucchi; Jonas Kohler (CoRR, 2019)

Batch normalization provably avoids ranks collapse for randomly initialised deep networks. - Hadi Daneshmand; Jonas Moritz Kohler; Francis R. Bach; Thomas Hofmann 001; Aurélien Lucchi (NeurIPS, 2020)

Theoretical Understanding of Batch-normalization: A Markov Chain Perspective. - Hadi Daneshmand; Jonas Moritz Kohler; Francis R. Bach; Thomas Hofmann 001; Aurélien Lucchi (CoRR, 2020)

Two-Level K-FAC Preconditioning for Deep Learning. - Nikolaos Tselepidis; Jonas Kohler; Antonio Orvieto (CoRR, 2020)

Learning Generative Models of Textured 3D Meshes from Real-World Images. - Dario Pavllo; Jonas Kohler; Thomas Hofmann 001; Aurélien Lucchi (ICCV, 2021)

This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks. - Adrian Hoffmann; Claudio Fanconi; Rahul Rade; Jonas Kohler (CoRR, 2021)

Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks. - Antonio Orvieto; Jonas Kohler; Dario Pavllo; Thomas Hofmann 001; Aurélien Lucchi (CoRR, 2021)

Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces. - Ziyad Sheebaelhamd; Konstantinos Zisis; Athina Nisioti; Dimitris Gkouletsos; Dario Pavllo; Jonas Kohler (CoRR, 2021)

Synthesizing Speech from Intracranial Depth Electrodes using an Encoder-Decoder Framework. - Jonas Kohler; Maarten C. Ottenhoff; Sophocles Goulis; Miguel Angrick; Albert J. Colon; G. Louis Wagner; Simon Tousseyn; Pieter Leonard Kubben; Christian Herff (CoRR, 2021)

Vanishing Curvature in Randomly Initialized Deep ReLU Networks. - Antonio Orvieto; Jonas Kohler; Dario Pavllo; Thomas Hofmann 001; Aurélien Lucchi (AISTATS, 2022)

Insights on the interplay of network architectures and optimization algorithms in deep learning. - Jonas Kohler (2022)

fMPI: Fast Novel View Synthesis in the Wild with Layered Scene Representations. - Jonas Kohler; Nicolas Griffiths Sanchez; Luca Cavalli; Catherine Herold; Albert Pumarola; Alberto Garcia Garcia; Ali K. Thabet (CoRR, 2023)

Cache Me if You Can: Accelerating Diffusion Models through Block Caching. - Felix Wimbauer; Bichen Wu; Edgar Schönfeld; Xiaoliang Dai; Ji Hou; Zijian He; Artsiom Sanakoyeu; Peizhao Zhang; Sam S. Tsai; Jonas Kohler; Christian Rupprecht 001; Daniel Cremers; et al. (CVPR, 2024)

Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation. - Jonas Kohler; Albert Pumarola; Edgar Schönfeld; Artsiom Sanakoyeu; Roshan Sumbaly; Peter Vajda; Ali K. Thabet (CoRR, 2024)

Movie Gen: A Cast of Media Foundation Models. - Adam Polyak; Amit Zohar; Andrew Brown; Andros Tjandra; Animesh Sinha; Ann Lee 001; Apoorv Vyas; Bowen Shi; Chih-Yao Ma; Ching-Yao Chuang; David Yan; Dhruv Choudhary; et al. (CoRR, 2024)

Deep learning applied to the segmentation of rodent brain MRI data outperforms noisy ground truth on full-fledged brain atlases. - Jonas Kohler; Thomas Bielser; Stanislaw Adaszewski; Basil Künnecke; Andreas Bruns (NeuroImage, 2024)

Adaptive Guidance: Training-free Acceleration of Conditional Diffusion Models. - Angela Castillo; Jonas Kohler; Juan C. Pérez; Juan Pablo Pérez; Albert Pumarola; Bernard Ghanem; Pablo Arbeláez; Ali K. Thabet (AAAI, 2025)

FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality Samples with Less Compute. - Sotiris Anagnostidis; Gregor Bachmann; Yeongmin Kim; Jonas Kohler; Markos Georgopoulos; Artsiom Sanakoyeu; Yuming Du; Albert Pumarola; Ali K. Thabet; Edgar Schönfeld (CVPR, 2025)

Autoregressive Distillation of Diffusion Transformers. - Yeongmin Kim; Sotiris Anagnostidis; Yuming Du; Edgar Schönfeld; Jonas Kohler; Markos Georgopoulos; Albert Pumarola; Ali K. Thabet; Artsiom Sanakoyeu (CVPR, 2025)

Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment. - Gregor Bachmann; Sotiris Anagnostidis; Albert Pumarola; Markos Georgopoulos; Artsiom Sanakoyeu; Yuming Du; Edgar Schönfeld; Ali K. Thabet; Jonas Kohler (ICLR, 2025)

Storybooth: Training-Free Multi-Subject Consistency for Improved Visual Storytelling. - Jaskirat Singh; Junshen K. Chen; Jonas Kohler; Michael F. Cohen (ICLR, 2025)