Debarghya Ghoshdastidar

I joined Theory of Machine Learning group in May 2016 as a post-doctoral researcher. I am part of Elite Program for Postdocs in Baden-Würtemberg, and also a junior fellow in Research Unit 1735 of German Research Foundation.
Broadly, I am interested in (i) developing learning algorithms for the purpose of network analysis, and (ii) understanding and applying results from random matrix theory and random graph theory. My current research revolves around studies related to hypothesis testing and clustering in the context of random graphs and hypergraphs.

Bio

• Ph.D. in Computer Science & Automation from Indian Institute of Science, Bangalore, India
  Thesis: Consistency of spectral algorithms for hypergraphs under planted partition model pdf slides
• Masters (Engineering) in System Science & Automation from Indian Institute of Science, Bangalore, India
  Thesis: Properties of multivariate q-Gaussian distribution and its application to smoothed functional algorithms for stochastic optimization pdf slides
• Bachelors (Engineering) in Electrical Engineering from Jadavpur University, Kolkata, India

Teaching

• Summer 2018: Seminar on Statistical Inference on Networks
• Winter 2016/17: Seminar on Crowdsourcing algorithms

Publications / Preprints

• (Preprint) D. Ghoshdastidar, M. Gutzeit, A. Carpentier and U. von Luxburg. Two-sample hypothesis testing for inhomogeneous random graphs. arXiv preprint: 1707.00833v2. arXiv
• D. Ghoshdastidar and A. Dukkipati. Uniform hypergraph partitioning: Provable tensor methods and sampling techniques. Journal of Machine Learning Research 18(50), pp. 1−41, 2017. link arXiv code
  Shorter version in ICML 2015. link slides
• D. Ghoshdastidar, M. Gutzeit, A. Carpentier and U. von Luxburg. Two-sample tests for large random graphs using network statistics. COLT, 2017. link arXiv slides
• S. Haghiri, D. Ghoshdastidar and U. von Luxburg. Comparison based nearest neighbor search. AISTATS, 2017. link arXiv
• D. Ghoshdastidar and A. Dukkipati. Consistency of spectral hypergraph partitioning under planted partition model. The Annals of Statistics, 45(1), pp. 289-315, 2017. DOI arxiv
• D. Ghoshdastidar, A. P. Adsul and A. Dukkipati. Learning with Jensen-Tsallis kernels. IEEE Transactions on Neural Networks and Learning Systems, 27 (10), pp. 2108-2119, 2016. DOI pdf code
  Shorter version in CVPR 2014. DOI arxiv
• A. Dukkipati, D. Ghoshdastidar, and J. Krishnan. Mixture modelling with compact support distributions for unsupervised learning. IJCNN, 2016. DOI
• (Preprint) D. Ghoshdastidar and A. Dukkipati. Coloring random non-uniform bipartite hypergraphs. arXiv preprint: 1507.00763v2. arXiv
• D. Ghoshdastidar and A. Dukkipati. Spectral clustering using multilinear SVD: Analysis, approximations and applications. AAAI, 2015. link slides
• D. Ghoshdastidar and A. Dukkipati. Consistency of spectral partitioning of uniform hypergraphs under planted partition model. NIPS, 2014. link poster code
• D. Ghoshdastidar, A. Dukkipati, and S. Bhatnagar. Newton based stochastic optimization using q-Gaussian smoothed functional algorithms. Automatica, 50 (10), pp. 2606-2614, 2014. DOI arxiv code
• D. Ghoshdastidar, A. Dukkipati, and S. Bhatnagar. Smoothed functional algorithms for stochastic optimization using q-Gaussian distributions. ACM Transactions on Modeling and Computer Simulation, 24 (3), Article 17, 2014. DOI arxiv code
  Shorter version in ISIT 2012. DOI arxiv
• A. Dukkipati, G. Pandey, D. Ghoshdastidar, P. Koley, and D. M. V. Satya Sriram. Generative Maximum Entropy Learning for Multiclass Classification. ICDM, 2013. DOI arxiv slides
• D. Ghoshdastidar and A. Dukkipati. On power law kernels, corresponding Reproducing Kernel Hilbert Space and applications. AAAI, 2013. link slides