Information Science and Engineering Lab

We perform teaching and research in machine learning strategies for the pattern analysis of various kinds of data. This comprises statistical models for clustering, graphical models for network inference and algorithmic methods to efficiently find these structures in the data.

Contact Info
CAB F 61.1
Universitaetstrasse 6,
8092 Zurich
Schweiz

+41 44 632 64 96

Follow Us

Information Science and Engineering Lab

We perform teaching and research in machine learning strategies for the pattern analysis of various kinds of data. This comprises statistical models for clustering, graphical models for network inference and algorithmic methods to efficiently find these structures in the data.

Contact Info
CAB F 61.1
Universitaetstrasse 6,
8092 Zurich
Schweiz

+41 44 632 64 96

Follow Us

Working Paper

Statistical and computational thresholds for the planted k-densest sub-hypergraph problem

Luca Corinzia, Paolo Penna, Wojciech Szpankowski, Joachim M. Buhmann,

arXiv,

DOI: 10.3929/ethz-b-000456981      Research Collection

Recovery a planted signal perturbed by noise is a fundamental problem in machine learning. In this work, we consider the problem of recovery a planted k-densest sub-hypergraph on h-uniform hypergraphs over n nodes. This fundamental problem appears in different contexts, e.g., community detection, average case complexity, and neuroscience applications. We first observe that it can be viewed as a structural variant of tensor PCA in which the hypergraph parameters k and h determine the structure of the signal to be...

Continuous submodular function maximization

Yatao Bian, Joachim M. Buhmann, Andreas Krause,

arXiv,

DOI: 10.3929/ethz-b-000466476      Research Collection

Continuous submodular functions are a category of generally non-convex/non-concave functions with a wide spectrum of applications. The celebrated property of this class of functions - continuous submodularity - enables both exact minimization and approximate maximization in poly. time. Continuous submodularity is obtained by generalizing the notion of submodularity from discrete domains to continuous domains. It intuitively captures a repulsive effect amongst different dimensions of the defined multivariate function. In this paper, we systematically study continuous submodularity and a class of...

Learning Counterfactual Representations for Estimating Individual Dose-Response Curves-2019

Patrick Schwab, Lorenz Linhardt, Stefan Bauer, Joachim M. Buhmann, Walter Karlen,

arXiv,

Research Collection

Optimal DR-Submodular Maximization and Applications to Provable Mean Field Inference

An Bian, Joachim M. Buhmann, Andreas Krause,

arXiv,

Research Collection