57 citations · 82 across the 13 of their papers we have counts for
11 papers · 1 filter
Learning Neural PDE Solvers with Parameter-Guided Channel Attention
Makoto Takamoto, Francesco Alesiani, Mathias Niepert
Scientific Machine Learning (SciML) is concerned with the development of learned emulators of physical systems governed by partial differential equations (PDE). In application doma…
Implicit Bilevel Optimization: Differentiating through Bilevel Optimization Programming
Francesco Alesiani
Bilevel Optimization Programming is used to model complex and conflicting interactions between agents, for example in Robust AI or Privacy-preserving AI. Integrating bilevel mathem…
PDEBENCH: An Extensive Benchmark for Scientific Machine Learning
Makoto Takamoto, Timothy Praditia, Raphael Leiteritz +4
Machine learning-based modeling of physical systems has experienced increased interest in recent years. Despite some impressive progress, there is still a lack of benchmarks for Sc…
Principle of Relevant Information for Graph Sparsification
Shujian Yu, Francesco Alesiani, Wenzhe Yin +2
Graph sparsification aims to reduce the number of edges of a graph while maintaining its structural properties. In this paper, we propose the first general and effective informatio…
Gated Information Bottleneck for Generalization in Sequential Environments
Francesco Alesiani, Shujian Yu, Xi Yu
Deep neural networks suffer from poor generalization to unseen environments when the underlying data distribution is different from that in the training set. By learning minimum su…
Measuring Dependence with Matrix-based Entropy Functional
Shujian Yu, Francesco Alesiani, Xi Yu +2
Measuring the dependence of data plays a central role in statistics and machine learning. In this work, we summarize and generalize the main idea of existing information-theoretic…
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…
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…