Publications

@inproceedings{adilova2024layerwise,

title = {Layer-wise Linear Mode Connectivity},

author = {Linara Adilova and Maksym Andriushchenko and Michael Kamp Asja Fischer and Martin Jaggi},

url = {https://openreview.net/pdf?id=LfmZh91tDI},

year  = {2024},

date = {2024-05-07},

urldate = {2024-05-07},

booktitle = {International Conference on Learning Representations (ICLR)},

publisher = {Curran Associates, Inc},

abstract = {Averaging neural network parameters is an intuitive method for fusing the knowledge of two independent models. It is most prominently used in federated learning. If models are averaged at the end of training, this can only lead to a good performing model if the loss surface of interest is very particular, i.e., the loss in the exact middle between the two models needs to be sufficiently low. This is impossible to guarantee for the non-convex losses of state-of-the-art networks. For averaging models trained on vastly different datasets, it was proposed to average only the parameters of particular layers or combinations of layers, resulting in better performing models. To get a better understanding of the effect of layer-wise averaging, we analyse the performance of the models that result from averaging single layers, or groups of layers. Based on our empirical and theoretical investigation, we introduce a novel notion of the layer-wise linear connectivity, and show that deep networks do not have layer-wise barriers between them. We analyze additionally the layer-wise personalization averaging and conjecture that in particular problem setup all the partial aggregations result in the approximately same performance.},

keywords = {deep learning, layer-wise, linear mode connectivity},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{yang2024orthogonal,

title = {Orthogonal Gradient Boosting for Interpretable Additive Rule Ensembles},

author = {Fan Yang and Pierre Le Bodic and Michael Kamp and Mario Boley},

year  = {2024},

date = {2024-05-02},

urldate = {2024-05-02},

booktitle = {Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS)},

abstract = {Gradient boosting of prediction rules is an efficient approach to learn potentially interpretable yet accurate probabilistic models. However, actual interpretability requires to limit the number and size of the generated rules, and existing boosting variants are not designed for this purpose. Though corrective boosting refits all rule weights in each iteration to minimise prediction risk, the included rule conditions tend to be sub-optimal, because commonly used objective functions fail to anticipate this refitting. Here, we address this issue by a new objective function that measures the angle between the risk gradient vector and the projection of the condition output vector onto the orthogonal complement of the already selected conditions. This approach correctly approximates the ideal update of adding the risk gradient itself to the model and favours the inclusion of more general and thus shorter rules. As we demonstrate using a wide range of prediction tasks, this significantly improves the comprehensibility/accuracy trade-off of the fitted ensemble. Additionally, we show how objective values for related rule conditions can be computed incrementally to avoid any substantial computational overhead of the new method.},

keywords = {complexity, explainability, interpretability, interpretable, machine learning, rule ensemble, rule mining, XAI},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{adilova2023fam,

title = {FAM: Relative Flatness Aware Minimization},

author = {Linara Adilova and Amr Abourayya and Jianning Li and Amin Dada and Henning Petzka and Jan Egger and Jens Kleesiek and Michael Kamp},

url = {https://michaelkamp.org/wp-content/uploads/2023/06/fam_regularization.pdf},

year  = {2023},

date = {2023-07-22},

urldate = {2023-07-22},

booktitle = {Proceedings of the ICML Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML)},

keywords = {deep learning, flatness, generalization, machine learning, relative flatness, theory of deep learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kamp2023federated,

title = {Federated Learning from Small Datasets},

author = {Michael Kamp and Jonas Fischer and Jilles Vreeken},

url = {https://michaelkamp.org/wp-content/uploads/2022/08/FederatedLearingSmallDatasets.pdf},

year  = {2023},

date = {2023-05-01},

urldate = {2023-05-01},

booktitle = {International Conference on Learning Representations (ICLR)},

journal = {arXiv preprint arXiv:2110.03469},

keywords = {black-box, black-box parallelization, daisy, daisy-chaining, FedDC, federated learning, small, small datasets},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mian2022nothing,

title = {Nothing but Regrets - Privacy-Preserving Federated Causal Discovery},

author = {Osman Mian, David Kaltenpoth, Michael Kamp, Jilles Vreeken},

year  = {2023},

date = {2023-04-25},

urldate = {2023-04-25},

booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},

keywords = {causal discovery, causality, explainable, federated, federated causal discovery, federated learning, interpretable},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mian2023informationb,

title = {Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments},

author = {Osman Mian and Michael Kamp and Jilles Vreeken},

year  = {2023},

date = {2023-02-07},

urldate = {2023-02-07},

booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},

keywords = {causal discovery, causality, federated, federated causal discovery, federated learning, intervention},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{adilova2023informed,

title = {Informed Novelty Detection in Sequential Data by Per-Cluster Modeling},

author = {Linara Adilova and Siming Chen and Michael Kamp},

url = {https://michaelkamp.org/wp-content/uploads/2023/09/Informed_Novelty_Detection_in_Sequential_Data_by_Per_Cluster_Modeling.pdf},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {ICML workshop on Artificial Intelligence & Human Computer Interaction},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mian2022regret,

title = {Regret-based Federated Causal Discovery},

author = {Osman Mian and David Kaltenpoth and Michael Kamp},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {The KDD'22 Workshop on Causal Discovery},

pages = {61--69},

organization = {PMLR},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{petzka2021relative,

title = {Relative Flatness and Generalization},

author = {Henning Petzka and Michael Kamp and Linara Adilova and Cristian Sminchisescu and Mario Boley},

year  = {2021},

date = {2021-12-07},

urldate = {2021-12-07},

booktitle = {Advances in Neural Information Processing Systems},

publisher = {Curran Associates, Inc.},

abstract = {Flatness of the loss curve is conjectured to be connected to the generalization ability of machine learning models, in particular neural networks. While it has been empirically observed that flatness measures consistently correlate strongly with generalization, it is still an open theoretical problem why and under which circumstances flatness is connected to generalization, in particular in light of reparameterizations that change certain flatness measures but leave generalization unchanged. We investigate the connection between flatness and generalization by relating it to the interpolation from representative data, deriving notions of representativeness, and feature robustness. The notions allow us to rigorously connect flatness and generalization and to identify conditions under which the connection holds. Moreover, they give rise to a novel, but natural relative flatness measure that correlates strongly with generalization, simplifies to ridge regression for ordinary least squares, and solves the reparameterization issue.},

keywords = {deep learning, flatness, generalization, Hessian, learning theory, relative flatness, theory of deep learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{li2021fedbn,

title = {FedBN: Federated Learning on Non-IID Features via Local Batch Normalization},

author = {Xiaoxiao Li and Meirui Jiang and Xiaofei Zhang and Michael Kamp and Qi Dou},

url = {https://michaelkamp.org/wp-content/uploads/2021/05/fedbn_federated_learning_on_non_iid_features_via_local_batch_normalization.pdf

https://michaelkamp.org/wp-content/uploads/2021/05/FedBN_appendix.pdf},

year  = {2021},

date = {2021-05-03},

urldate = {2021-05-03},

booktitle = {Proceedings of the 9th International Conference on Learning Representations (ICLR)},

abstract = {The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improving data privacy. In most cases, the assumption of independent and identically distributed samples across local clients does not hold for federated learning setups. Under this setting, neural network training performance may vary significantly according to the data distribution and even hurt training convergence. Most of the previous work has focused on a difference in the distribution of labels or client shifts. Unlike those settings, we address an important problem of FL, e.g., different scanners/sensors in medical imaging, different scenery distribution in autonomous driving (highway vs. city), where local clients store examples with different distributions compared to other clients, which we denote as feature shift non-iid. In this work, we propose an effective method that uses local batch normalization to alleviate the feature shift before averaging models. The resulting scheme, called FedBN, outperforms both classical FedAvg, as well as the state-of-the-art for non-iid data (FedProx) on our extensive experiments. These empirical results are supported by a convergence analysis that shows in a simplified setting that FedBN has a faster convergence rate than FedAvg. Code is available at https://github.com/med-air/FedBN.},

keywords = {batch normalization, black-box parallelization, deep learning, federated learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3394486.3403180,

title = {HOPS: Probabilistic Subtree Mining for Small and Large Graphs},

author = {Pascal Welke and Florian Seiffarth and Michael Kamp and Stefan Wrobel},

url = {https://doi.org/10.1145/3394486.3403180},

doi = {10.1145/3394486.3403180},

isbn = {9781450379984},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

booktitle = {Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},

pages = {1275–1284},

publisher = {Association for Computing Machinery},

address = {Virtual Event, CA, USA},

series = {KDD '20},

abstract = {Frequent subgraph mining, i.e., the identification of relevant patterns in graph databases, is a well-known data mining problem with high practical relevance, since next to summarizing the data, the resulting patterns can also be used to define powerful domain-specific similarity functions for prediction. In recent years, significant progress has been made towards subgraph mining algorithms that scale to complex graphs by focusing on tree patterns and probabilistically allowing a small amount of incompleteness in the result. Nonetheless, the complexity of the pattern matching component used for deciding subtree isomorphism on arbitrary graphs has significantly limited the scalability of existing approaches. In this paper, we adapt sampling techniques from mathematical combinatorics to the problem of probabilistic subtree mining in arbitrary databases of many small to medium-size graphs or a single large graph. By restricting on tree patterns, we provide an algorithm that approximately counts or decides subtree isomorphism for arbitrary transaction graphs in sub-linear time with one-sided error. Our empirical evaluation on a range of benchmark graph datasets shows that the novel algorithm substantially outperforms state-of-the-art approaches both in the task of approximate counting of embeddings in single large graphs and in probabilistic frequent subtree mining in large databases of small to medium sized graphs.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kamp2018efficient,

title = {Efficient Decentralized Deep Learning by Dynamic Model Averaging},

author = {Michael Kamp and Linara Adilova and Joachim Sicking and Fabian Hüger and Peter Schlicht and Tim Wirtz and Stefan Wrobel},

url = {http://michaelkamp.org/wp-content/uploads/2018/07/commEffDeepLearning_extended.pdf},

year  = {2018},

date = {2018-09-14},

urldate = {2018-09-14},

booktitle = {Machine Learning and Knowledge Discovery in Databases},

publisher = {Springer},

abstract = {We propose an efficient protocol for decentralized training of deep neural networks from distributed data sources. The proposed protocol allows to handle different phases of model training equally well and to quickly adapt to concept drifts. This leads to a reduction of communication by an order of magnitude compared to periodically communicating state-of-the-art approaches. Moreover, we derive a communication bound that scales well with the hardness of the serialized learning problem. The reduction in communication comes at almost no cost, as the predictive performance remains virtually unchanged. Indeed, the proposed protocol retains loss bounds of periodically averaging schemes. An extensive empirical evaluation validates major improvement of the trade-off between model performance and communication which could be beneficial for numerous decentralized learning applications, such as autonomous driving, or voice recognition and image classification on mobile phones.},

keywords = {decentralized, deep learning, federated learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kamp2017effective,

title = {Effective Parallelisation for Machine Learning},

author = {Michael Kamp and Mario Boley and Olana Missura and Thomas Gärtner},

url = {http://papers.nips.cc/paper/7226-effective-parallelisation-for-machine-learning.pdf},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

booktitle = {Advances in Neural Information Processing Systems},

pages = {6480--6491},

abstract = {We present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to growing amounts of data as well as growing needs for accurate and confident predictions in critical applications. In contrast to other parallelisation techniques, it can be applied to a broad class of learning algorithms without further mathematical derivations and without writing dedicated code, while at the same time maintaining theoretical performance guarantees. Moreover, our parallelisation scheme is able to reduce the runtime of many learning algorithms to polylogarithmic time on quasi-polynomially many processing units. This is a significant step towards a general answer to an open question on efficient parallelisation of machine learning algorithms in the sense of Nick's Class (NC). The cost of this parallelisation is in the form of a larger sample complexity. Our empirical study confirms the potential of our parallelisation scheme with fixed numbers of processors and instances in realistic application scenarios.},

keywords = {decentralized, distributed, machine learning, parallelization, radon},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ullrich2017co,

title = {Co-regularised support vector regression},

author = {Katrin Ullrich and Michael Kamp and Thomas Gärtner and Martin Vogt and Stefan Wrobel},

url = {http://michaelkamp.org/mk_v1/wp-content/uploads/2018/05/CoRegSVR.pdf},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},

pages = {338--354},

organization = {Springer},

keywords = {co-regularization, transfer learning, unsupervised},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kamp2016communication,

title = {Communication-Efficient Distributed Online Learning with Kernels},

author = {Michael Kamp and Sebastian Bothe and Mario Boley and Michael Mock},

editor = {Paolo Frasconi and Niels Landwehr and Giuseppe Manco and Jilles Vreeken},

url = {http://michaelkamp.org/wp-content/uploads/2020/03/Paper467.pdf},

year  = {2016},

date = {2016-09-16},

urldate = {2016-09-16},

booktitle = {Machine Learning and Knowledge Discovery in Databases},

pages = {805--819},

publisher = {Springer International Publishing},

abstract = {We propose an efficient distributed online learning protocol for low-latency real-time services. It extends a previously presented protocol to kernelized online learners that represent their models by a support vector expansion. While such learners often achieve higher predictive performance than their linear counterparts, communicating the support vector expansions becomes inefficient for large numbers of support vectors. The proposed extension allows for a larger class of online learning algorithms—including those alleviating the problem above through model compression. In addition, we characterize the quality of the proposed protocol by introducing a novel criterion that requires the communication to be bounded by the loss suffered.},

keywords = {communication-efficient, distributed, dynamic averaging, federated learning, kernel methods, parallelization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ullrich2016ligand,

title = {Ligand-based virtual screening with co-regularised support Vector Regression},

author = {Katrin Ullrich and Michael Kamp and Thomas Gärtner and Martin Vogt and Stefan Wrobel},

url = {http://michaelkamp.org/wp-content/uploads/2020/03/LigandBasedCoSVR.pdf},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

booktitle = {2016 IEEE 16th international conference on data mining workshops (ICDMW)},

pages = {261--268},

organization = {IEEE},

abstract = {We consider the problem of ligand affinity prediction as a regression task, typically with few labelled examples, many unlabelled instances, and multiple views on the data. In chemoinformatics, the prediction of binding affinities for protein ligands is an important but also challenging task. As protein-ligand bonds trigger biochemical reactions, their characterisation is a crucial step in the process of drug discovery and design. However, the practical determination of ligand affinities is very expensive, whereas unlabelled compounds are available in abundance. Additionally, many different vectorial representations for compounds (molecular fingerprints) exist that cover different sets of features. To this task we propose to apply a co-regularisation approach, which extracts information from unlabelled examples by ensuring that individual models trained on different fingerprints make similar predictions. We extend support vector regression similarly to the existing co-regularised least squares regression (CoRLSR) and obtain a co-regularised support vector regression (CoSVR). We empirically evaluate the performance of CoSVR on various protein-ligand datasets. We show that CoSVR outperforms CoRLSR as well as existing state-of-the-art approaches that do not take unlabelled molecules into account. Additionally, we provide a theoretical bound on the Rademacher complexity for CoSVR.},

keywords = {biology, chemistry, corresponding projections, semi-supervised},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{michael2014beating,

title = {Beating Human Analysts in Nowcasting Corporate Earnings by using Publicly Available Stock Price and Correlation Features},

author = {Michael Kamp and Mario Boley and Thomas Gärtner},

url = {http://www.ferari-project.eu/wp-content/uploads/2014/12/earningsPrediction.pdf},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

booktitle = {Proceedings of the SIAM International Conference on Data Mining},

volume = {72},

pages = {641--649},

organization = {SIAM},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kamp2014communication,

title = {Communication-Efficient Distributed Online Prediction by Dynamic Model Synchronization},

author = {Michael Kamp and Mario Boley and Daniel Keren and Assaf Schuster and Izchak Sharfman},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECMLPKDD)},

organization = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kamp2013privacy,

title = {Privacy-preserving mobility monitoring using sketches of stationary sensor readings},

author = {Michael Kamp and Christine Kopp and Michael Mock and Mario Boley and Michael May},

year  = {2013},

date = {2013-01-01},

urldate = {2013-01-01},

booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},

pages = {370--386},

organization = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}