How does the core sit inside the mantle? - Amin Coja-Oghlan; Oliver Cooley; Mihyun Kang; Kathrin Skubch (Electron. Notes Discret. Math., 2015)

Core forging and local limit theorems for the k-core of random graphs. - Amin Coja-Oghlan; Oliver Cooley; Mihyun Kang; Kathrin Skubch (CoRR, 2017)

Limits of discrete distributions and Gibbs measures on random graphs. - Amin Coja-Oghlan; Will Perkins 001; Kathrin Skubch (Eur. J. Comb., 2017)

The Minimum Bisection in the Planted Bisection Model. - Amin Coja-Oghlan; Oliver Cooley; Mihyun Kang; Kathrin Skubch (Theory Comput., 2017)

Message passing algorithms for investigating invariants of sparse random graphs. - Kathrin Skubch (2018)

Core forging and local limit theorems for the k-core of random graphs. - Amin Coja-Oghlan; Oliver Cooley; Mihyun Kang; Kathrin Skubch (J. Comb. Theory B, 2019)

Multi-Class Multi-Instance Count Conditioned Adversarial Image Generation. - Amrutha Saseendran; Kathrin Skubch; Margret Keuper (ICCV, 2021)

Shape your Space: A Gaussian Mixture Regularization Approach to Deterministic Autoencoders. - Amrutha Saseendran; Kathrin Skubch; Stefan Falkner; Margret Keuper (NeurIPS, 2021)

Transfer Learning with Gaussian Processes for Bayesian Optimization. - Petru Tighineanu; Kathrin Skubch; Paul Baireuther; Attila Reiss; Felix Berkenkamp; Julia Vinogradska (AISTATS, 2022)

Trading off Image Quality for Robustness is not Necessary with Regularized Deterministic Autoencoders. - Amrutha Saseendran; Kathrin Skubch; Stefan Falkner; Margret Keuper (NeurIPS, 2022)

Scalable Meta-Learning with Gaussian Processes. - Petru Tighineanu; Lukas Grossberger; Paul Baireuther; Kathrin Skubch; Stefan Falkner; Julia Vinogradska; Felix Berkenkamp (AISTATS, 2024)