162 citations · 243 across the 10 of their papers we have counts for
10 papers
A Martingale Kernel Independence Test
Felix Laumann, Zhaolu Liu, Mauricio Barahona
The Hilbert-Schmidt Independence Criterion (HSIC) and its joint-independence extension are degenerate -statistics whose data-dependent weighted- null limit…
Kernel-based independence tests for causal structure learning on functional data
Felix Laumann, Julius von Kügelgen, Junhyung Park +2
Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics…
A continuous Structural Intervention Distance to compare Causal Graphs
Mihir Dhanakshirur, Felix Laumann, Junhyung Park +1
Understanding and adequately assessing the difference between a true and a learnt causal graphs is crucial for causal inference under interventions. As an extension to the graph-ba…
Kernel-based Joint Independence Tests for Multivariate Stationary and Non-stationary Time Series
Zhaolu Liu, Robert L. Peach, Felix Laumann +2
Multivariate time series data that capture the temporal evolution of interconnected systems are ubiquitous in diverse areas. Understanding the complex relationships and potential d…
One to rule them all: Towards Joint Indic Language Hate Speech Detection
Mehar Bhatia, Tenzin Singhay Bhotia, Akshat Agarwal +5
This paper is a contribution to the Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC) 2021 shared task. Social media today is a hotbed of toxic an…
Indic-Transformers: An Analysis of Transformer Language Models for Indian Languages
Kushal Jain, Adwait Deshpande, Kumar Shridhar +2
Language models based on the Transformer architecture have achieved state-of-the-art performance on a wide range of NLP tasks such as text classification, question-answering, and t…
Kernel Two-Sample and Independence Tests for Non-Stationary Random Processes
Felix Laumann, Julius von Kügelgen, Mauricio Barahona
Two-sample and independence tests with the kernel-based MMD and HSIC have shown remarkable results on i.i.d. data and stationary random processes. However, these statistics are not…
Non-linear interlinkages and key objectives amongst the Paris Agreement and the Sustainable Development Goals
Felix Laumann, Julius von Kügelgen, Mauricio Barahona
The United Nations' ambitions to combat climate change and prosper human development are manifested in the Paris Agreement and the Sustainable Development Goals (SDGs), respectivel…
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
Kumar Shridhar, Felix Laumann, Marcus Liwicki
Artificial Neural Networks are connectionist systems that perform a given task by learning on examples without having prior knowledge about the task. This is done by finding an opt…
Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference
Kumar Shridhar, Felix Laumann, Marcus Liwicki
We introduce a novel uncertainty estimation for classification tasks for Bayesian convolutional neural networks with variational inference. By normalizing the output of a Softplus…