Kobak, Dmitry

13 publications

TMLR 2026 DREAMS: Preserving Both Local and Global Structure in Dimensionality Reduction Noël Kury, Dmitry Kobak, Sebastian Damrich

TMLR 2025 Node Embeddings via Neighbor Embeddings Jan Niklas Böhm, Marius Keute, Alica Guzmán, Sebastian Damrich, Andrew Draganov, Dmitry Kobak

ICML 2025 On the Importance of Embedding Norms in Self-Supervised Learning Andrew Draganov, Sharvaree Vadgama, Sebastian Damrich, Jan Niklas Böhm, Lucas Maes, Dmitry Kobak, Erik J Bekkers

NeurIPS 2025 TRACE: Contrastive Learning for Multi-Trial Time Series Data in Neuroscience Lisa Schmors, Dominic Gonschorek, Jan Niklas Böhm, Yongrong Qiu, Na Zhou, Dmitry Kobak, Andreas S. Tolias, Fabian H. Sinz, Jacob Reimer, Katrin Franke, Sebastian Damrich, Philipp Berens

NeurIPS 2024 Persistent Homology for High-Dimensional Data Based on Spectral Methods Sebastian Damrich, Philipp Berens, Dmitry Kobak

ICML 2024 Scaling Down Deep Learning with MNIST-1D Samuel James Greydanus, Dmitry Kobak

ICLR 2023 From $t$-SNE to UMAP with Contrastive Learning Sebastian Damrich, Niklas Böhm, Fred A Hamprecht, Dmitry Kobak

ICLR 2023 Unsupervised Visualization of Image Datasets Using Contrastive Learning Niklas Böhm, Philipp Berens, Dmitry Kobak

JMLR 2022 Attraction-Repulsion Spectrum in Neighbor Embeddings Jan Niklas Böhm, Philipp Berens, Dmitry Kobak

ICLRW 2022 Two-Dimensional Visualization of Large Document Libraries Using T-SNE Rita González-Márquez, Philipp Berens, Dmitry Kobak

ECML-PKDD 2022 Wasserstein T-SNE Fynn Bachmann, Philipp Hennig, Dmitry Kobak

JMLR 2020 The Optimal Ridge Penalty for Real-World High-Dimensional Data Can Be Zero or Negative Due to the Implicit Ridge Regularization Dmitry Kobak, Jonathan Lomond, Benoit Sanchez

ECML-PKDD 2019 Heavy-Tailed Kernels Reveal a Finer Cluster Structure in T-SNE Visualisations Dmitry Kobak, George C. Linderman, Stefan Steinerberger, Yuval Kluger, Philipp Berens