1 citations · 2 across the 5 of their papers we have counts for
7 papers
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…