- Date: September 18, 2015
Where: IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2015
Research Area: Machine Learning
Brief - MERL researchers A. Knyazev and A. Malyshev gave a talk at the IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2015. The paper was published at the IEEE Xplore conference proceedings.
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- Date: July 13, 2015
Where: SIAM Conference on Control and Its Applications 2015
Research Area: Control
Brief - MERL and Mitsubishi Electric researchers presented talks at the SIAM Conference on Control and Its Applications 2015. The papers were published by SIAM in the conference proceedings.
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- Date: June 1, 2015
Where: International Conference on Computational Science (ICCS) Brief - Andrew Knyazev gave a talk at the International Conference on Computational Science (ICCS), 2015 on Nonsymmetric preconditioning for conjugate gradient and steepest descent methods. The paper was published by Elsevier B.V. in the conference proceedings.
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- Date & Time: Thursday, May 2, 2013; 12:00 PM
Speaker: Ben Miller, MIT Abstract 
Graph theory provides an intuitive mathematical foundation for dealing with relational data, but there are numerous computational challenges in the detection of interesting behavior within small subsets of vertices, especially as the graphs grow larger and the behavior becomes more subtle. This presentation discusses computational considerations of a residuals-based subgraph detection framework, including the implications on inference with recent statistical models. We also present scaling properties, demonstrating analysis of a billion-vertex graph using commodity hardware.
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- Date: March 14, 2013
Where: SIAM Journal of Scientific Computing Brief - The article "Absolute Value Preconditioning for Symmetric Indefinite Linear Systems" by Vecharynski, E. and Knyazev, A.V. was published in SIAM Journal of Scientific Computing.
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- Date & Time: Friday, March 8, 2013; 12:00 PM
Speaker: Prof. Hiroshi Mamitsuka, Kyoto University Abstract 
Semi-structured data, particularly graphs, are now abundant in molecular biology. Typical examples are protein-protein interactions, gene regulatory networks, metabolic pathways, etc. To understand cellular mechanisms from this type of data, I've been working on semi-structured data, covering a wide variety of general topics in machine learning or data mining, such as link prediction, graph clustering, frequent subgraph mining, and label propagation over graphs and so on. In this talk I will focus on label propagation, in which nodes are partially labeled and the objective is to predict unknown labels using labels and links. I'll present two approaches under two different inputs in sequence: 1) only single graph and 2) multiple graphs sharing a common node set.
1) Existing methods extract features, considering either of graph smoothness or discrimination. The proposed method extracts features, considering the both two aspects, as spectral transforms. The obtained features or eigenvectors can be used to generate kernels, leading to multiple kernel learning to solve the label propagation problem efficiently.
2) Existing methods estimate weights over given graphs, like selecting the most reliable graph. This framework is however unable to consider densely connected subgraphs, which we call locally informative graphs (LIGs). The proposed method first runs spectral graph partitioning over each graph to capture LIGs in eigenvectors and then an existing method of label propagation for multiple graphs is run over the entire eigenvectors.
I will show empirical advantages of the two proposed methods by using both synthetic and real, biological networks.
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- Date & Time: Wednesday, November 7, 2012; 12:00 PM
Speaker: Prof. Marcus Sarkis, Worcester Polytechnic Institute Abstract - We discuss the following problem: Given a target function on a domain, what is the Neumann data on the boundary so that its harmonic extension into the domain is the closest function to the target function in the L2 norm? For convex polygonal domains, we show that regularization is not needed in case the space for the Neumann data is chosen properly. In the second part of the talk we discuss solvers for the associated discrete Hessian which are robust with respect to regularization parameters and mesh sizes.
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