5 citations · 21 across the 13 of their papers we have counts for
13 papers
Uncertainty Propagation in Node Classification
Zhao Xu, Carolin Lawrence, Ammar Shaker +1
Quantifying predictive uncertainty of neural networks has recently attracted increasing attention. In this work, we focus on measuring uncertainty of graph neural networks (GNNs) f…
Multi-Source Survival Domain Adaptation
Ammar Shaker, Carolin Lawrence
Survival analysis is the branch of statistics that studies the relation between the characteristics of living entities and their respective survival times, taking into account the…
Human-Centric Research for NLP: Towards a Definition and Guiding Questions
Bhushan Kotnis, Kiril Gashteovski, Julia Gastinger +7
With Human-Centric Research (HCR) we can steer research activities so that the research outcome is beneficial for human stakeholders, such as end users. But what exactly makes rese…
A Human-Centric Assessment Framework for AI
Sascha Saralajew, Ammar Shaker, Zhao Xu +5
With the rise of AI systems in real-world applications comes the need for reliable and trustworthy AI. An essential aspect of this are explainable AI systems. However, there is no…
milIE: Modular & Iterative Multilingual Open Information Extraction
Bhushan Kotnis, Kiril Gashteovski, Daniel Oñoro Rubio +5
Open Information Extraction (OpenIE) is the task of extracting (subject, predicate, object) triples from natural language sentences. Current OpenIE systems extract all triple slots…
Learning to Transfer with von Neumann Conditional Divergence
Ammar Shaker, Shujian Yu, Daniel Oñoro-Rubio
The similarity of feature representations plays a pivotal role in the success of problems related to domain adaptation. Feature similarity includes both the invariance of marginal…
Bilevel Continual Learning
Ammar Shaker, Francesco Alesiani, Shujian Yu +1
Continual learning (CL) studies the problem of learning a sequence of tasks, one at a time, such that the learning of each new task does not lead to the deterioration in performanc…
Modular-Relatedness for Continual Learning
Ammar Shaker, Shujian Yu, Francesco Alesiani
In this paper, we propose a continual learning (CL) technique that is beneficial to sequential task learners by improving their retained accuracy and reducing catastrophic forgetti…
Learning an Interpretable Graph Structure in Multi-Task Learning
Shujian Yu, Francesco Alesiani, Ammar Shaker +1
We present a novel methodology to jointly perform multi-task learning and infer intrinsic relationship among tasks by an interpretable and sparse graph. Unlike existing multi-task…
Towards Interpretable Multi-Task Learning Using Bilevel Programming
Francesco Alesiani, Shujian Yu, Ammar Shaker +1
Interpretable Multi-Task Learning can be expressed as learning a sparse graph of the task relationship based on the prediction performance of the learned models. Since many natural…
TSK-Streams: Learning TSK Fuzzy Systems on Data Streams
Ammar Shaker, Eyke Hüllermeier
The problem of adaptive learning from evolving and possibly non-stationary data streams has attracted a lot of interest in machine learning in the recent past, and also stimulated…
Efficient and Scalable Multi-task Regression on Massive Number of Tasks
Xiao He, Francesco Alesiani, Ammar Shaker
Many real-world large-scale regression problems can be formulated as Multi-task Learning (MTL) problems with a massive number of tasks, as in retail and transportation domains. How…
MetaBags: Bagged Meta-Decision Trees for Regression
Jihed Khiari, Luis Moreira-Matias, Ammar Shaker +2
Ensembles are popular methods for solving practical supervised learning problems. They reduce the risk of having underperforming models in production-grade software. Although criti…