198 citations · 450 across the 17 of their papers we have counts for
6 papers · 1 filter
A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks
Farshad Harirchi, Omar A. Khalil, Sijia Liu +3
In this paper, we propose an optimization-based sparse learning approach to identify the set of most influential reactions in a chemical reaction network. This reduced set of react…
A New Data-Driven Sparse-Learning Approach to Study Chemical Reaction Networks
Farshad Harirchi, Doohyun Kim, Omar A. Khalil +4
Chemical kinetic mechanisms can be represented by sets of elementary reactions that are easily translated into mathematical terms using physicochemical relationships. The schematic…
Semiblind subgraph reconstruction in Gaussian graphical models
Tianpei Xie, Sijia Liu, Alfred O. Hero
Consider a social network where only a few nodes (agents) have meaningful interactions in the sense that the conditional dependency graph over node attribute variables (behaviors)…
A Memristor-Based Optimization Framework for AI Applications
Sijia Liu, Yanzhi Wang, Makan Fardad +1
Memristors have recently received significant attention as ubiquitous device-level components for building a novel generation of computing systems. These devices have many promisin…
Bias-Variance Tradeoff of Graph Laplacian Regularizer
Pin-Yu Chen, Sijia Liu
This paper presents a bias-variance tradeoff of graph Laplacian regularizer, which is widely used in graph signal processing and semi-supervised learning tasks. The scaling law of…
Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity
Sijia Liu, Pin-Yu Chen, Alfred O. Hero
We consider the problem of accelerating distributed optimization in multi-agent networks by sequentially adding edges. Specifically, we extend the distributed dual averaging (DDA)…