Publications

Recent preprints / Papers in submission

1. A. van Elst, D. Ghoshdastidar. Tight PAC-Bayesian risk certificates for contrastive learning. [Preprint]

2. S. Mukherjee, S. S. Mukherjee, D. Ghoshdastidar. Wasserstein projection pursuit of non-Gaussian signals. Under review at Journal of Multivariate Analysis [Preprint] [PDF]

3. M. Eppert, S. Mukherjee, D. Ghoshdastidar. Recovering imbalanced clusters via gradient based projection pursuit. Under review at Journal of Multivariate Analysis [PDF]

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

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

Peer reviewed publications

1. L. Rendsburg, L. C. Vankadara, U. von Luxburg, D. Ghoshdastidar. A consistent estimator for confounding strength. Mathematical Statistics and Learning, 7 (3/4): 189-220, 2024. [Paper] [Preprint]

2. A. Singh, M. Sabanayagam, K. Muandet, D. Ghoshdastidar. Fast adaptive test-time defense with robust features. Transactions of Machine Learning, 2024. [Paper] [Preprint]

3. P. Esser, S. Mukherjee, D. Ghoshdastidar. Representation learning dynamics of self-supervised models. Transactions of Machine Learning, 2024. [Paper] [Preprint]

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

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

6. 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]

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

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

9. 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]

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

11. 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]

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

13. 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]

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

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

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

17. 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] 

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

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

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

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

22. 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]

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

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

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

26. 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]

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

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

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

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

31. 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]

32. 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]

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

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

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

Doctoral dissertations

1. Pascal Mattia Esser. Theoretical Foundations for Exploiting Unlabelled Data in Machine Learning. Technical University of Munich, 2024. [PDF]

2. Leena Chennuru Vankadara. Towards a theory of learning under extreme non-identifiability: Through the lens of causal learning and kernel clustering. University of Tübingen, 2023. [PDF]

Workshop papers / Preprints

1. L. Gosch, M. Sabanayagam, D. Ghoshdastidar, S. Günnemann. Provable robustness of (graph) neural networks against data poisoning and backdoor attacks. Neurips 2024 Workshop on New Frontiers in Adversarial Machine Learning (AdvML-Frontiers) [Preprint]

2. V. Kaufmann, P. M. Esser, B. Zhu, D. Ghoshdatidar. Self-supervised learning approaches to improve residential housing energy prediction accuracy. GNI Symposium & Expo on Artificial Intelligence for the Built World 2024.

3. O. Bouattour, O. Kanjilal, D. Ghoshdastidar. Active learning surrogates for enhanced reliability assessment of engineering systems. GNI Symposium & Expo on Artificial Intelligence for the Built World 2024.

4. M. Eppert, S. Mukherjee, D. Ghoshdastidar. Recovering imbalanced clusters via gradient based projection pursuit. LinStat 2024

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

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

7. 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]

8. 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.

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