10 citations · 24 across the 6 of their papers we have counts for
8 papers
Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote
Yi-Shan Wu, Andrés R. Masegosa, Stephan S. Lorenzen +2
We present a new second-order oracle bound for the expected risk of a weighted majority vote. The bound is based on a novel parametric form of the Chebyshev- Cantelli inequality (a…
Information Bottleneck: Exact Analysis of (Quantized) Neural Networks
Stephan Sloth Lorenzen, Christian Igel, Mads Nielsen
The information bottleneck (IB) principle has been suggested as a way to analyze deep neural networks. The learning dynamics are studied by inspecting the mutual information (MI) b…
Second Order PAC-Bayesian Bounds for the Weighted Majority Vote
Andrés R. Masegosa, Stephan S. Lorenzen, Christian Igel +1
We present a novel analysis of the expected risk of weighted majority vote in multiclass classification. The analysis takes correlation of predictions by ensemble members into acco…
Tracking Behavioral Patterns among Students in an Online Educational System
Stephan Lorenzen, Niklas Hjuler, Stephen Alstrup
Analysis of log data generated by online educational systems is an essential task to better the educational systems and increase our understanding of how students learn. In this st…
Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search
Stephan S. Lorenzen, Ninh Pham
Top-k maximum inner product search (MIPS) is a central task in many machine learning applications. This paper extends top-k MIPS with a budgeted setting, that asks for the best app…
Investigating Writing Style Development in High School
Stephan Lorenzen, Niklas Hjuler, Stephen Alstrup
In this paper we do the first large scale analysis of writing style development among Danish high school students. More than 10K students with more than 100K essays are analyzed. W…
Detecting Ghostwriters in High Schools
Magnus Stavngaard, August Sørensen, Stephan Lorenzen +2
Students hiring ghostwriters to write their assignments is an increasing problem in educational institutions all over the world, with companies selling these services as a product.…
On PAC-Bayesian Bounds for Random Forests
Stephan Sloth Lorenzen, Christian Igel, Yevgeny Seldin
Existing guarantees in terms of rigorous upper bounds on the generalization error for the original random forest algorithm, one of the most frequently used machine learning methods…