Recent preprints / Papers in submission

  1. A. Craciun, D. Ghoshdastidar. On the stability of gradient descent for large learning rate. [Preprint]

  2. G. G. Anil, P. Esser, D. Ghoshdastidar. When can we approximate wide contrastive models with neural tangent kernels and principal component analysis? [Preprint]

  3. A. Singh, M. Sabanayagam, K. Muandet, D. Ghoshdastidar. Fast adaptive test-time defense with robust features. [Preprint]

  4. P. Esser, S. Mukherjee, D. Ghoshdastidar. Representation learning dynamics of self-supervised models. Under review at Transactions of Machine Learning [Preprint]

  5. L. Rendsburg, L. C. Vankadara, U. von Luxburg, D. Ghoshdastidar. A consistent estimator for confounding strength. Under review at Mathematical Statistics and Learning [Preprint]

Peer reviewed publications

  1. M. Fleissner, L. Vankadara, D. Ghoshdastidar. Explaining kernel clustering via decision trees. ICLR 2024 [Paper

  2. P. Esser, M. Fleissner, D. Ghoshdastidar. Non-parametric representation learning with kernels. AAAI 2024. [Preprint]

  3. M. Sabanayagam, P. Esser, D. Ghoshdastidar. Analysis of convolutions, non-linearity and depth in graph neural networks using neural tangent kernel. Transactions of Machine Learning Research, 2023. [Paper] [Preprint] [Code]

  4. A. Mandal, M. Perrot, D. Ghoshdastidar. A revenue function for comparison-based hierarchical clustering. Transactions of Machine Learning Research, 2023 [Paper] [Preprint]

  5. P. Esser, S. Mukherjee, M. Sabanayagam, D. Ghoshdastidar. Improved representation learning through tensorized autoencoders. AISTATS 2023. [Paper] [Preprint]

  6. M. C. May, Z. Fang, M. Eitel, N. Stricker, D. Ghoshdastidar, G. Lanza. Graph-based prediction of missing KPIs through optimization and random forests for KPI Systems. Production Engineering, 2022. DOI:10.1007/s11740-022-01179-y [Paper]

  7. L. Vankadara, L. Rendsburg, U. von Luxburg, D. Ghoshdastidar. Interpolation and regularization for causal learning. NeurIPS 2022 [Paper] [Preprint]

  8. L. C. Vankadara, P. M. Faller, M. Hardt, L. Minorics, D. Ghoshdastidar, D. Janzing. Causal forecasting: Generalization bounds for autoregressive models. UAI 2022. [Paper] [Preprint] [Code]

  9. M. Sabanayagam, L. C. Vankadara, D. Ghoshdastidar. Graphon based clustering and testing of networks: Algorithms and theory. ICLR 2022. [Paper] [Preprint] [Code]

  10. S. Huber, S. H. Suyu, D. Ghoshdastidar, S. Taubenberger, V. Bonvin, J. H. H. Chan, M. Kromer, U. M. Noebauer, S. A. Sim, L. Leal-Taixé. HOLISMOKES -- VII. Time-delay measurement of strongly lensed Type Ia supernovae using machine learning. Astronomy & Astrophysics 2022. [Paper] [Preprint]

  11. P. Esser, L. C. Vankadara, D. Ghoshdastidar. Learning theory can (sometimes) explain generalisation in graph neural networks. NeurIPS 2021 [Paper] [Preprint]

  12. L. C. Vankadara, S. Bordt, U. von Luxburg, D. Ghoshdastidar. Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models. AISTATS 2021 [Paper]

  13. M. Perrot, P. Esser, D. Ghoshdastidar. Near-optimal comparison based clustering. Neurips 2020 [Paper] [Preprint] [Code]

  14. D. Ghoshdastidar, M. Gutzeit, A. Carpentier, U. Von Luxburg. Two-sample hypothesis testing for inhomogeneous random graphs. The Annals of Statistics, 48 (4): 2208-2229, 2020. [Paper] [Preprint

  15. L. C. Vankadara, D. Ghoshdastidar. On the optimality of kernels for high-dimensional clustering. AISTATS 2020. [Paper] [Preprint] [Video]

  16. D. Ghoshdastidar, M. Perrot, U. Von Luxburg. Foundations of comparison-based hierarchical clustering. NeurIPS, 2019. [Paper] [Preprint] [Code]

  17. D. Ghoshdastidar, U. Von Luxburg. Practical methods for graph two-sample testing. NeurIPS, 2018. [Paper] [Preprint] [Code

  18. D. Ghoshdastidar, M. Gutzeit, A. Carpentier, U. von Luxburg. Two-sample tests for large random graphs using network statistics. COLT, 2017. [Preprint

  19. D. Ghoshdastidar, A. Dukkipati. Uniform hypergraph partitioning: Provable tensor methods and sampling techniques. The Journal of Machine Learning Research, 18(50): 1-41, 2017. [Paper] [Preprint] [Code]

  20. S. Haghiri, D. Ghoshdastidar, U. von Luxburg. Comparison based nearest neighbor search. AISTATS, 2017. [Paper] [Preprint]

  21. D. Ghoshdastidar, A. Dukkipati. Consistency of Spectral Hypergraph Partitioning under Planted Partition Model. The Annals of Statistics, 45 (1): 289-315, 2017. [Paper] [Preprint]

  22. A. Dukkipati, D. Ghoshdastidar, J. Krishnan. Mixture modelling with compact support distributions for unsupervised learning. IJCNN, 2016. [Paper]

  23. D. Ghoshdastidar, A. P. Adsul, A. Dukkipati. Learning with Jensen-Tsallis kernels. IEEE Transactions on Neural Networks and Learning Systems, 27 (10), pp. 2108-2119, 2016. [Paper]

  24. D. Ghoshdastidar, A. Dukkipati. A provable generalized tensor spectral method for uniform hypergraph partitioning. ICML, 2015. [Paper] [Video]

  25. D. Ghoshdastidar, A. Dukkipati. Spectral clustering using multilinear SVD: Analysis, approximations and applications. AAAI, 2015. [Paper]

  26. D. Ghoshdastidar, A. Dukkipati. Consistency of spectral partitioning of uniform hypergraphs under planted partition model. NIPS, 2014. [Paper]

  27. D. Ghoshdastidar, A. Dukkipati, S. Bhatnagar. Newton based stochastic optimization using q-Gaussian smoothed functional algorithms. Automatica, 50(10): 2606-2614, 2014. [Paper] [Preprint]

  28. D. Ghoshdastidar, A. Dukkipati, A. P. Adsul, A. S. Vijayan. Spectral clustering with Jensen-type kernels and their multi-point extensions. CVPR, 2014. [Paper] [Preprint]

  29. D. Ghoshdastidar, A. Dukkipati, S. Bhatnagar. Smoothed functional algorithms for stochastic optimization using q-Gaussian distributions. ACM Transactions on Modeling and Computer Simulation, 24(3):Article 17, 2014. [Paper] [Preprint]

  30. A. Dukkipati, G. Pandey, D. Ghoshdastidar, P. Koley, D. M. V. Satya Sriram. Generative maximum entropy learning for multiclass classification. ICDM, 2013. [Paper] [Preprint]

  31. D. Ghoshdastidar, A. Dukkipati. On power law kernels, corresponding Reproducing Kernel Hilbert Space and applications. AAAI, 2013. [Paper]

  32. D. Ghoshdastidar, A. Dukkipati, S. Bhatnagar. q-Gaussian based Smoothed Functional algorithms for stochastic optimization. ISIT, 2012. [Paper] [Preprint]

Workshop papers / Preprints

  1. S. Mukherjee, S. S. Mukherjee, D. Ghoshdastidar. Wasserstein projection pursuit of non-Gaussian signals. [Preprint]

  2. M. Sabanayagam, P. Esser, D. Ghoshdastidar. New insights into graph convolutional networks using neural tangent kernels. MLG2022@ECMLPKDD  [Preprint]

  3. N. Ayday, D. Ghoshdastidar. Improvement on incremental spectral clustering. LWDA 2021.

  4. V. Starlinger, C. de la Rua Lope and D. Ghoshdastidar. Machine Learning Benchmark to Assess the Environmental Impact of Cars. AAAI 2021 AI for Urban Mobility Workshop. [Paper] [Data/code]

  5. D. Ghoshdastidar and U. von Luxburg. Do nonparametric two-sample tests work for small sample size? A study on random graphs. NIPS-2016 workshop on Adaptive and Scalable Nonparametric Methods in ML.

  6. D. Ghoshdastidar, A. Dukkipati. Coloring random non-uniform bipartite hypergraphs. [Preprint]