Search Results for author: Ammar Shaker

Found 14 papers, 3 papers with code

Uncertainty Propagation in Node Classification

no code implementations3 Apr 2023 Zhao Xu, Carolin Lawrence, Ammar Shaker, Raman Siarheyeu

To address these issues, we propose a Bayesian uncertainty propagation (BUP) method, which embeds GNNs in a Bayesian modeling framework, and models predictive uncertainty of node classification with Bayesian confidence of predictive probability and uncertainty of messages.

Classification Node Classification

Multi-Source Survival Domain Adaptation

1 code implementation1 Dec 2022 Ammar Shaker, Carolin Lawrence

With the rise of machine learning, survival analysis can be modeled as learning a function that maps studied patients to their survival times.

Domain Adaptation Survival Analysis

Human-Centric Research for NLP: Towards a Definition and Guiding Questions

no code implementations10 Jul 2022 Bhushan Kotnis, Kiril Gashteovski, Julia Gastinger, Giuseppe Serra, Francesco Alesiani, Timo Sztyler, Ammar Shaker, Na Gong, Carolin Lawrence, Zhao Xu

With Human-Centric Research (HCR) we can steer research activities so that the research outcome is beneficial for human stakeholders, such as end users.

A Human-Centric Assessment Framework for AI

no code implementations25 May 2022 Sascha Saralajew, Ammar Shaker, Zhao Xu, Kiril Gashteovski, Bhushan Kotnis, Wiem Ben Rim, Jürgen Quittek, Carolin Lawrence

Inspired by the Turing test, we introduce a human-centric assessment framework where a leading domain expert accepts or rejects the solutions of an AI system and another domain expert.

milIE: Modular & Iterative Multilingual Open Information Extraction

no code implementations ACL 2022 Bhushan Kotnis, Kiril Gashteovski, Daniel Oñoro Rubio, Vanesa Rodriguez-Tembras, Ammar Shaker, Makoto Takamoto, Mathias Niepert, Carolin Lawrence

In contrast, we explore the hypothesis that it may be beneficial to extract triple slots iteratively: first extract easy slots, followed by the difficult ones by conditioning on the easy slots, and therefore achieve a better overall extraction.

Open Information Extraction

Learning to Transfer with von Neumann Conditional Divergence

no code implementations7 Aug 2021 Ammar Shaker, Shujian Yu, Daniel Oñoro-Rubio

Feature similarity includes both the invariance of marginal distributions and the closeness of conditional distributions given the desired response $y$ (e. g., class labels).

Domain Adaptation

Modular-Relatedness for Continual Learning

no code implementations2 Nov 2020 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 forgetting.

Continual Learning

Bilevel Continual Learning

no code implementations2 Nov 2020 Ammar Shaker, Francesco Alesiani, Shujian Yu, Wenzhe Yin

This paper presents Bilevel Continual Learning (BiCL), a general framework for continual learning that fuses bilevel optimization and recent advances in meta-learning for deep neural networks.

Bilevel Optimization Continual Learning +1

Towards Interpretable Multi-Task Learning Using Bilevel Programming

no code implementations11 Sep 2020 Francesco Alesiani, Shujian Yu, Ammar Shaker, Wenzhe Yin

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.

Multi-Task Learning

Learning an Interpretable Graph Structure in Multi-Task Learning

no code implementations11 Sep 2020 Shujian Yu, Francesco Alesiani, Ammar Shaker, Wenzhe Yin

We present a novel methodology to jointly perform multi-task learning and infer intrinsic relationship among tasks by an interpretable and sparse graph.

Multi-Task Learning

TSK-Streams: Learning TSK Fuzzy Systems on Data Streams

1 code implementation10 Nov 2019 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 research in related fields, such as computational intelligence and fuzzy systems.


MetaBags: Bagged Meta-Decision Trees for Regression

no code implementations17 Apr 2018 Jihed Khiari, Luis Moreira-Matias, Ammar Shaker, Bernard Zenko, Saso Dzeroski

The proposed method and meta-features are designed in such a way that they enable good predictive performance even in subregions of space which are not adequately represented in the available training data.

Inductive Bias Meta-Learning +1

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