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Found 13 papers, 3 papers with code
Multi-Source Survival Domain Adaptation
With the rise of machine learning, survival analysis can be modeled as learning a function that maps studied patients to their survival times.
Human-Centric Research for NLP: Towards a Definition and Guiding Questions
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
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
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.
Learning to Transfer with von Neumann Conditional Divergence
Feature similarity includes both the invariance of marginal distributions and the closeness of conditional distributions given the desired response $y$ (e. g., class labels).
Modular-Relatedness for Continual Learning
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.
Bilevel Continual Learning
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.
Learning an Interpretable Graph Structure in Multi-Task Learning
We present a novel methodology to jointly perform multi-task learning and infer intrinsic relationship among tasks by an interpretable and sparse graph.
Towards Interpretable Multi-Task Learning Using Bilevel Programming
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.
Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications
We propose a simple yet powerful test statistic to quantify the discrepancy between two conditional distributions.
TSK-Streams: Learning TSK Fuzzy Systems on Data Streams
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.
Efficient and Scalable Multi-task Regression on Massive Number of Tasks
Scaling up MTL methods to problems with a tremendous number of tasks is a big challenge.
MetaBags: Bagged Meta-Decision Trees for Regression
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.
