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Dr. rer. nat. Valeriy Khakhutskyy

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Valeriy Khakhutskyy Photo
Office:
02.05.036
Email:
Khakhutvmail.png
Phone:
(089) 289 18619
Homepage:
valonsoftware.com
Office hours:
by arrangement

Contents

Publications


Talks

  • V. Khakhutskyy: Adaptive sparse grids for high-dimensional machine learning [BibTeX].
    Challenges in statistical inference, Garching, November 2016.
  • V. Khakhutskyy: Greedy sparse grids adaptivity in the context of online learning [BibTeX].
    Sparse Grids and Applications 2014, Stuttgart, September 2014.
  • V. Khakhutskyy: Scalability and Fault Tolerance of the Alternating Direction Method of Multipliers for Sparse Grids [BibTeX].
    ParCo2013, München, September 2013.
  • V. Khakhutskyy: Parallel solution of regression problems for sparse grids with ADMM [BibTeX].
    5th Workshop in High-Dimensional Approximation, Australian National University, Canberra, February 2013.
  • V. Khakhutskyy: Parallel Solution of Regression Problems Using Sparse Grids and Alternating Direction Method of Multipliers [BibTeX].
    ANZIAM 2013, Newcastle, Australia, February 2013.
  • V. Khakhutskyy: Distributed data-mining with sparse grids using alternating direction method of multipliers [BibTeX].
    Sparse Grids and Applications 2012, München, July 2012.


Students

  • J. Maier: Implementation and Evaluation of Online-Classification Methods based on Sparse Grids [pdf] [BibTeX].
    Master's thesis, Fakultät für Informatik, November 2016.
  • I. Shurmin: Efficient Implementation of the Whole-Genome Regression and Prediction Methods for Parallel Environments [BibTeX].
    Master's thesis, Institut für Informatik, Technische Universität München, September 2016.
  • L. Krenz: Integration of Prior Knowledge for Regression and Classification with Sparse Grids [BibTeX].
    Bachelor's thesis, Institut für Informatik, Technische Universität München, August 2016.
  • K. Sampa Subbarao: Large-Scale Elastic Machine Learning with Sparse Grid Combination Technique [pdf] [BibTeX].
    Master's thesis, Institut für Informatik, Technische Universität München, May 2016.
  • K. Strauß: Convergence of the asynchronous and partially-asynchronous ADMM methods for sparse grid model splitting [BibTeX].
    Bachelor's thesis, Fakultät für Mathematik, Technische Universität München, April 2016.
  • F. Klemt: Sparse grids with data-aware hierarchical subspaces for regression [BibTeX].
    Bachelor's thesis, Institut für Informatik, Technische Universität München, April 2016.
  • G. Koenig: Online Classification with Adaptive Sparse Grid Kernels [BibTeX].
    Bachelor's thesis, Institut für Informatik, Technische Universität München, February 2016.
  • C. Dehner: Anwendung des Downhill-Simplex-Algorithmus im MARZIPAN-Rechenkern [BibTeX].
    Bachelor's thesis, Institut für Informatik, Technische Universität München, October 2015.
  • K. S. Subbarao: Elastic Machine Learning with Multivariate Extrapolation [pdf] [BibTeX].
    Studienarbeit/SEP/IDP, Institut für Informatik, Technische Universität München, October 2015. Guided Research.
  • E. Drossos: Bayesian Multi-scale Optimistic Optimization with Adaptive Sparse Grids [BibTeX].
    Studienarbeit/SEP/IDP, Institut für Informatik, Technische Universität München, April 2015. Guided Research.
  • K. Batzner: Subspace projection methods for high-dimensional supervised learning with sparse grids [BibTeX].
    Bachelor's thesis, Institut für Informatik, Technische Universität München, April 2015.
  • C. Uphoff: Parallel fitting of additive models [pdf] [BibTeX].
    Master's thesis, Fakultät für Elektrotechnik und Informationstechnik, Technische Universität München, February 2015.
  • F. Zipperle: Density-based clustering with periodic adaptive sparse grids [pdf] [BibTeX].
    Bachelor's thesis, Institut für Informatik, Technische Universität München, September 2014.
  • K. G. Batbold: Diskrete Fouriertransformation auf dünnen Gittern für temporales Data-Mining [BibTeX].
    Bachelor's thesis, Institut für Informatik, Technische Universität München, July 2014.
  • J. Maier: Online Lernmethoden für Data Mining mit Dünnen Gittern [BibTeX].
    Bachelor's thesis, Institut für Informatik, Technische Universität München, March 2014.
  • M. Lettrich: Dimension- and Local-Adaptive Refinement Strategies for Data Mining Using Sparse Grids [BibTeX].
    Bachelor's thesis, Institut für Informatik, Technische Universität München, February 2014.


Teaching