Constant Curvature Graph Convolutional Networks. - Gregor Bachmann; Gary Bécigneul; Octavian Ganea (ICML, 2020)

Uniform Convergence, Adversarial Spheres and a Simple Remedy. - Gregor Bachmann; Seyed-Mohsen Moosavi-Dezfooli; Thomas Hofmann (ICML, 2021)

Precise characterization of the prior predictive distribution of deep ReLU networks. - Lorenzo Noci; Gregor Bachmann; Kevin Roth; Sebastian Nowozin; Thomas Hofmann (NeurIPS, 2021)

Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect. - Lorenzo Noci; Kevin Roth; Gregor Bachmann; Sebastian Nowozin; Thomas Hofmann (NeurIPS, 2021)

Analytic Insights into Structure and Rank of Neural Network Hessian Maps. - Sidak Pal Singh; Gregor Bachmann; Thomas Hofmann (NeurIPS, 2021)

Generalization Through the Lens of Leave-One-Out Error. - Gregor Bachmann; Thomas Hofmann; Aurélien Lucchi (ICLR, 2022)

How Tempering Fixes Data Augmentation in Bayesian Neural Networks. - Gregor Bachmann; Lorenzo Noci; Thomas Hofmann (ICML, 2022)

Multi-CLIP: Contrastive Vision-Language Pre-training for Question Answering tasks in 3D Scenes. - Alexandros Delitzas; Maria Parelli; Nikolas Hars; Georgios Vlassis; Sotirios-Konstantinos Anagnostidis; Gregor Bachmann; Thomas Hofmann (BMVC, 2023)

CLIP-Guided Vision-Language Pre-training for Question Answering in 3D Scenes. - Maria Parelli; Alexandros Delitzas; Nikolas Hars; Georgios Vlassis; Sotiris Anagnostidis; Gregor Bachmann; Thomas Hofmann (CVPR Workshops, 2023)

The Curious Case of Benign Memorization. - Sotiris Anagnostidis; Gregor Bachmann; Lorenzo Noci; Thomas Hofmann (ICLR, 2023)

Random Teachers are Good Teachers. - Felix Sarnthein; Gregor Bachmann; Sotiris Anagnostidis; Thomas Hofmann (ICML, 2023)

Scaling MLPs: A Tale of Inductive Bias. - Gregor Bachmann; Sotiris Anagnostidis; Thomas Hofmann (NeurIPS, 2023)

Navigating Scaling Laws: Accelerating Vision Transformer's Training via Adaptive Strategies. - Sotiris Anagnostidis; Gregor Bachmann; Thomas Hofmann (CoRR, 2023)

Disentangling Linear Mode-Connectivity. - Gul Sena Altintas; Gregor Bachmann; Lorenzo Noci; Thomas Hofmann (CoRR, 2023)

How Good is a Single Basin? - Kai Lion; Lorenzo Noci; Thomas Hofmann; Gregor Bachmann (AISTATS, 2024)

Navigating Scaling Laws: Compute Optimality in Adaptive Model Training. - Sotiris Anagnostidis; Gregor Bachmann; Imanol Schlag; Thomas Hofmann (ICML, 2024)

The Pitfalls of Next-Token Prediction. - Gregor Bachmann; Vaishnavh Nagarajan (ICML, 2024)

A Language Model's Guide Through Latent Space. - Dimitri von Rütte; Sotiris Anagnostidis; Gregor Bachmann; Thomas Hofmann (ICML, 2024)

Interpolated-MLPs: Controllable Inductive Bias. - Sean Wu; Jordan Hong; Keyu Bai; Gregor Bachmann (CoRR, 2024)

Tokenisation is NP-Complete. - Philip Whittington; Gregor Bachmann; Tiago Pimentel (ACL (1), 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)

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)