Intriguing Properties of Input-Dependent Randomized Smoothing. - Peter Súkeník; Aleksei Kuvshinov; Stephan Günnemann (ICML, 2022)

The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes. - Peter Kocsis 001; Peter Súkeník; Guillem Brasó; Matthias Nießner; Laura Leal-Taixé; Ismail Elezi (NeurIPS, 2022)

Generalization In Multi-Objective Machine Learning. - Peter Súkeník; Christoph H. Lampert (CoRR, 2022)

Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model. - Peter Súkeník; Marco Mondelli; Christoph H. Lampert (NeurIPS, 2023)

Average gradient outer product as a mechanism for deep neural collapse. - Daniel Beaglehole; Peter Súkeník; Marco Mondelli; Mikhail Belkin (NeurIPS, 2024)

Neural collapse vs. low-rank bias: Is deep neural collapse really optimal? - Peter Súkeník; Christoph H. Lampert; Marco Mondelli (NeurIPS, 2024)

Neural Collapse versus Low-rank Bias: Is Deep Neural Collapse Really Optimal? - Peter Súkeník; Marco Mondelli; Christoph Lampert (CoRR, 2024)

Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse. - Arthur Jacot; Peter Súkeník; Zihan Wang; Marco Mondelli (ICLR, 2025)

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers. - Peter Súkeník; Christoph H. Lampert; Marco Mondelli (CoRR, 2025)