flopska.comhttps://flopska.com/2024-03-13T00:00:00+01:00Articles2024-03-13T00:00:00+01:002024-03-13T00:00:00+01:00Florian Kalinketag:flopska.com,2024-03-13:/articles.html<h1>Preprints</h1>
<p>F. Kalinke and <a href="https://zoltansz.github.io/">Z. Szabó</a>. <strong>The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels.</strong> Technical Report, 2024. </br>
<a href="https://arxiv.org/abs/2403.07735">paper</a> (arXiv)</p>
<p><a href="https://tobiasfuchs.de/">T. Fuchs</a>, F. Kalinke and <a href="https://scholar.google.com/citations?user=RzCtTjYAAAAJ">K. Böhm</a>. <strong>Uncertainty-Aware Partial-Label Learning.</strong> Technical Report, 2024. </br>
<a href="https://arxiv.org/abs/2402.00592">paper</a> (arXiv) | <a href="https://github.com/anon1248/uncertainty-aware-pll">code</a></p>
<p>F. Kalinke, <a href="https://scholar.google.com/citations?user=VJeY0WcAAAAJ">M. Heyden</a>, <a href="https://edouardfouche.com/">E. Fouché</a> and <a href="https://scholar.google.com/citations?user=RzCtTjYAAAAJ">K. Böhm</a>. <strong>Maximum Mean Discrepancy on Exponential …</strong></p><h1>Preprints</h1>
<p>F. Kalinke and <a href="https://zoltansz.github.io/">Z. Szabó</a>. <strong>The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels.</strong> Technical Report, 2024. </br>
<a href="https://arxiv.org/abs/2403.07735">paper</a> (arXiv)</p>
<p><a href="https://tobiasfuchs.de/">T. Fuchs</a>, F. Kalinke and <a href="https://scholar.google.com/citations?user=RzCtTjYAAAAJ">K. Böhm</a>. <strong>Uncertainty-Aware Partial-Label Learning.</strong> Technical Report, 2024. </br>
<a href="https://arxiv.org/abs/2402.00592">paper</a> (arXiv) | <a href="https://github.com/anon1248/uncertainty-aware-pll">code</a></p>
<p>F. Kalinke, <a href="https://scholar.google.com/citations?user=VJeY0WcAAAAJ">M. Heyden</a>, <a href="https://edouardfouche.com/">E. Fouché</a> and <a href="https://scholar.google.com/citations?user=RzCtTjYAAAAJ">K. Böhm</a>. <strong>Maximum Mean Discrepancy on Exponential Windows for Online Change Detection.</strong> Technical Report, 2023. </br>
<a href="https://arxiv.org/abs/2205.12706">paper</a> (arXiv) | <a href="https://github.com/FlopsKa/mmdew-change-detector">code</a></p>
<h1>2024</h1>
<p><a href="https://scholar.google.com/citations?user=VJeY0WcAAAAJ">M. Heyden</a>, <a href="https://edouardfouche.com/">E. Fouché</a>, V. Arzamasov, T. Fenn, F. Kalinke and <a href="https://scholar.google.com/citations?user=RzCtTjYAAAAJ">K. Böhm</a>. <strong>Adaptive Bernstein Change Detector for High-Dimensional Data Streams.</strong> Data Mining and Knowledge Discovery (2024): 1-30. </br>
<a href="publications/heyden24-article.pdf">paper</a> (author version) | <a href="https://doi.org/10.1007/s10618-023-00999-5">paper</a> (Springer) | <a href="https://arxiv.org/abs/2306.12974">paper</a> (arXiv) | <a href="https://github.com/heymarco/AdaptiveBernsteinChangeDetector">code</a></p>
<h1>2023</h1>
<p>F. Kalinke and <a href="https://zoltansz.github.io/">Z. Szabó</a>. <strong>Nyström M-Hilbert-Schmidt Independence Criterion.</strong> Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 216:1005-1015, 2023. </br>
<a href="https://proceedings.mlr.press/v216/kalinke23a/kalinke23a.pdf">paper</a> (UAI) | <a href="https://proceedings.mlr.press/v216/kalinke23a/kalinke23a-supp.pdf">supplement</a> (UAI) | <a href="publications/kalinke23-poster.pdf">poster</a> | <a href="publications/kalinke23-spotlight.pdf">slides</a> | <a href="https://arxiv.org/abs/2302.09930">paper</a> (arXiv) | <a href="https://github.com/FlopsKa/nystroem-mhsic">code</a></p>
<p>F. Kalinke, <a href="https://edouardfouche.com/">E. Fouché</a>, H. Thiessen and <a href="https://scholar.google.com/citations?user=RzCtTjYAAAAJ">K. Böhm</a>. <strong>Multi-kernel Times Series Outlier Detection.</strong> Proceedings of the Twenty-Sixth International Conference on Discovery Science (DS 2023), LNAI 14276:688-702, 2023.<br/>
<a href="publications/kalinke23b-article.pdf">paper</a> (author version) | <a href="https://doi.org/10.1007/978-3-031-45275-8_46">paper</a> (LNAI) | <a href="publications/kalinke23b-slides.pdf">slides</a> | <a href="https://github.com/FlopsKa/mk-tsod/">code</a></p>
<h1>2021</h1>
<p>F. Kalinke, P. Bielski, <a href="https://scholar.google.com/citations?user=AUW65_oAAAAJ">S. Singh</a>, <a href="https://edouardfouche.com/">E. Fouché</a> and <a href="https://scholar.google.com/citations?user=RzCtTjYAAAAJ">K. Böhm</a>. <strong>An Evaluation of NILM Approaches on Industrial Energy-Consumption Data.</strong> e-Energy 2021: 239-243.</br>
<a href="https://doi.org/10.1145/3447555.3464863">paper</a> | <a href="https://github.com/nilmtk/nilmtk/tree/master/nilmtk/dataset_converters/hipe">data</a></p>
<p><a href="https://edouardfouche.com/">E. Fouché</a>, F. Kalinke and <a href="https://scholar.google.com/citations?user=RzCtTjYAAAAJ">K. Böhm</a>. <strong>Efficient subspace search in data streams.</strong> Information Systems 97 (2021).</br> <a href="https://doi.org/10.1016/j.is.2020.101705">paper</a> | <a href="https://github.com/edouardfouche/SGMRD">code</a></p>
<h1>2020</h1>
<p><a href="https://edouardfouche.com/">E. Fouché</a>, A. Mazankiewicz, F. Kalinke and <a href="https://scholar.google.com/citations?user=RzCtTjYAAAAJ">K. Böhm</a>. <strong>A framework for dependency estimation in heterogeneous data streams.</strong> Distributed and Parallel Databases (2020).</br> <a href="https://link.springer.com/article/10.1007%2Fs10619-020-07295-x">paper</a> | <a href="https://github.com/edouardfouche/MCDE-extended">code</a></p>