36 citations · 116 across the 26 of their papers we have counts for
29 papers
Autoregressive Frontier Expansion: Growing Trees with Graph Machine Learning
Umer Gupta, Saku Peltonen, Martin Ritzert
Tree-like branching structures are common in nature, from botanical trees to neurons, blood vessels and respiratory trees. Their branching shape often reflects function, making str…
Hierarchical clustering with maximum density paths and mixture models
Martin Ritzert, Polina Turishcheva, Laura Hansel +3
Hierarchical clustering is an effective, interpretable method for analyzing structure in data. It reveals insights at multiple scales without requiring a predefined number of clust…
Internal Evaluation of Density-Based Clusterings with Noise
Anna Beer, Lena Krieger, Pascal Weber +3
Being able to evaluate the quality of a clustering result even in the absence of ground truth cluster labels is fundamental for research in data mining. However, most cluster valid…
MNIST-Nd: a set of naturalistic datasets to benchmark clustering across dimensions
Polina Turishcheva, Laura Hansel, Martin Ritzert +2
Driven by advances in recording technology, large-scale high-dimensional datasets have emerged across many scientific disciplines. Especially in biology, clustering is often used t…
Distinguished In Uniform: Self Attention Vs. Virtual Nodes
Eran Rosenbluth, Jan Tönshoff, Martin Ritzert +2
Graph Transformers (GTs) such as SAN and GPS are graph processing models that combine Message-Passing GNNs (MPGNNs) with global Self-Attention. They were shown to be universal func…
Boosting, Voting Classifiers and Randomized Sample Compression Schemes
Arthur da Cunha, Kasper Green Larsen, Martin Ritzert
In boosting, we aim to leverage multiple weak learners to produce a strong learner. At the center of this paradigm lies the concept of building the strong learner as a voting class…
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark
Jan Tönshoff, Martin Ritzert, Eran Rosenbluth +1
The recent Long-Range Graph Benchmark (LRGB, Dwivedi et al. 2022) introduced a set of graph learning tasks strongly dependent on long-range interaction between vertices. Empirical…
AdaBoost is not an Optimal Weak to Strong Learner
Mikael Møller Høgsgaard, Kasper Green Larsen, Martin Ritzert
AdaBoost is a classic boosting algorithm for combining multiple inaccurate classifiers produced by a weak learner, to produce a strong learner with arbitrarily high accuracy when g…
Determination of from untagged decays using 2019-2021 Belle II data
Belle II Collaboration, K. Adamczyk, L. Aggarwal +588
We present an analysis of the charmless semileptonic decay , where , from 198.0 million pairs of mesons recorded by the Belle II…
Angular analysis of decays reconstructed in 2019, 2020, and 2021 Belle II data
Belle II Collaboration, F. Abudinén, I. Adachi +590
We report on a Belle II measurement of the branching fraction (), longitudinal polarization fraction (), and CP asymmetry () of …
Measurement of the branching fraction of the decay using 190 fb of Belle II data
Belle II Collaboration, F. Abudinén, I. Adachi +590
We report the measurement of the branching fraction of the decay in data recorded by the Belle II experiment at the Sup…
Optimal Weak to Strong Learning
Kasper Green Larsen, Martin Ritzert
The classic algorithm AdaBoost allows to convert a weak learner, that is an algorithm that produces a hypothesis which is slightly better than chance, into a strong learner, achiev…
Graph Machine Learning for Design of High-Octane Fuels
Jan G. Rittig, Martin Ritzert, Artur M. Schweidtmann +7
Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO2 emissions. Identification of molecules with desired autoignition p…
Measurements of the branching fractions for decays at Belle II
F. Abudinén, I. Adachi, R. Adak +565
This paper reports a study of decays using fb of data collected during 2019--2020 by the Belle II experiment at the SuperKEKB asymm…
Angular analysis of decays reconstructed in 2019-2020 Belle II data
Belle II collaboration, F. Abudinén, I. Adachi +549
We report on the first Belle II measurement of the branching fraction () and longitudinal polarization fraction () of decays. We reconstruct $B^+\…
Measurements of branching fractions and CP-violating charge asymmetries in multibody charmless decays reconstructed in 2019-2020 Belle II data
Belle II collaboration, F. Abudinén, I. Adachi +549
We report on measurements of branching fractions () and CP-violating charge asymmetries () of multibody charmless decays reconstructed by the…
Measurement of the branching fraction for decays reconstructed in 2019-2020 Belle II data
Belle II Collaboration, F. Abudinén, I. Adachi +551
We report the first reconstruction of the decay mode at Belle II using samples of 2019 and 2020 data that correspond to 62.8 fb of integrated luminos…
Measurements of branching fractions and direct CP asymmetries in , and using 2019 and 2020 data
Belle II Collaboration, F. Abudinén, I. Adachi +549
We report updated measurements of branching fractions () and CP-violating charge asymmetries () for charmless decays at Belle II, which opera…
Measurement of the branching fractions of decays using 2019/2020 Belle II data
Belle II Collaboration, F. Abudinén, I. Adachi +545
This note describes the rediscovery of decays in Belle II data, both in the charged and neutral final state: and . The is searched…
Measurements of branching fractions and direct -violating asymmetries in $B^+ \to K^+ π^0~\mbox{and}~π^+ π^0$ decays using 2019 and 2020 Belle II data
F. Abudinén, I. Adachi, R. Adak +549
We report measurements of branching fractions () and direct -violating asymmetries () for the decays and …
On the Parameterized Complexity of Learning First-Order Logic
Steffen van Bergerem, Martin Grohe, Martin Ritzert
We analyse the complexity of learning first-order queries in a model-theoretic framework for supervised learning introduced by (Grohe and Turán, TOCS 2004). Previous research on th…
Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing
Jan Tönshoff, Martin Ritzert, Hinrikus Wolf +1
We propose CRaWl, a novel neural network architecture for graph learning. Like graph neural networks, CRaWl layers update node features on a graph and thus can freely be combined o…
The Effects of Randomness on the Stability of Node Embeddings
Tobias Schumacher, Hinrikus Wolf, Martin Ritzert +6
We systematically evaluate the (in-)stability of state-of-the-art node embedding algorithms due to randomness, i.e., the random variation of their outcomes given identical algorith…
Measurement of the integrated luminosity of the Phase 2 data of the Belle II experiment
Belle II Collaboration, F. Abudinén, I. Adachi +420
From April to July 2018, a data sample at the peak energy of the resonance was collected with the Belle~II detector at the SuperKEKB electron-positron collider. This is the…