Debarghya Ghoshdastidar

According to our database, Debarghya Ghoshdastidar authored at least 45 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Representation Learning Dynamics of Self-Supervised Models.

Pascal Mattia Esser, Satyaki Mukherjee, Debarghya Ghoshdastidar

Trans. Mach. Learn. Res., 2024

Data Augmentations Go Beyond Encoding Invariances: A Theoretical Study on Self-Supervised Learning.

Shlomo Libo Feigin, Maximilian Fleissner, Debarghya Ghoshdastidar

CoRR, 2024

Decision Trees for Interpretable Clusters in Mixture Models and Deep Representations.

Maximilian Fleissner, Maedeh Zarvandi, Debarghya Ghoshdastidar

CoRR, 2024

Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks.

Lukas Gosch, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar, Stephan Günnemann

CoRR, 2024

When can we Approximate Wide Contrastive Models with Neural Tangent Kernels and Principal Component Analysis?

Gautham Govind Anil, Pascal Mattia Esser, Debarghya Ghoshdastidar

CoRR, 2024

On the Stability of Gradient Descent for Large Learning Rate.

Alexandru Craciun, Debarghya Ghoshdastidar

CoRR, 2024

Explaining Kernel Clustering via Decision Trees.

Maximilian Fleissner, Leena Chennuru Vankadara, Debarghya Ghoshdastidar

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Non-parametric Representation Learning with Kernels.

Pascal Mattia Esser, Maximilian Fleissner, Debarghya Ghoshdastidar

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernel.

Mahalakshmi Sabanayagam, Pascal Mattia Esser, Debarghya Ghoshdastidar

Trans. Mach. Learn. Res., 2023

A Revenue Function for Comparison-Based Hierarchical Clustering.

Aishik Mandal, Michaël Perrot, Debarghya Ghoshdastidar

Trans. Mach. Learn. Res., 2023

Fast Adaptive Test-Time Defense with Robust Features.

Anurag Singh, Mahalakshmi Sabanayagam, Krikamol Muandet, Debarghya Ghoshdastidar

CoRR, 2023

Wasserstein Projection Pursuit of Non-Gaussian Signals.

Satyaki Mukherjee, Soumendu Sundar Mukherjee, Debarghya Ghoshdastidar

CoRR, 2023

Improved Representation Learning Through Tensorized Autoencoders.

Pascal Mattia Esser, Satyaki Mukherjee, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

A Consistent Estimator for Confounding Strength.

Luca Rendsburg, Leena Chennuru Vankadara, Debarghya Ghoshdastidar, Ulrike von Luxburg

CoRR, 2022

Representation Power of Graph Convolutions : Neural Tangent Kernel Analysis.

Mahalakshmi Sabanayagam, Pascal Mattia Esser, Debarghya Ghoshdastidar

CoRR, 2022

Causal forecasting: generalization bounds for autoregressive models.

Leena Chennuru Vankadara, Philipp Michael Faller, Michaela Hardt, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Interpolation and Regularization for Causal Learning.

Leena Chennuru Vankadara, Luca Rendsburg, Ulrike von Luxburg, Debarghya Ghoshdastidar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Graphon based Clustering and Testing of Networks: Algorithms and Theory.

Mahalakshmi Sabanayagam, Leena Chennuru Vankadara, Debarghya Ghoshdastidar

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Causal Forecasting: Generalization Bounds for Autoregressive Models.

Leena C. Vankadara, Philipp Michael Faller, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing

CoRR, 2021

New Insights into Graph Convolutional Networks using Neural Tangent Kernels.

Mahalakshmi Sabanayagam, Pascal Mattia Esser, Debarghya Ghoshdastidar

CoRR, 2021

Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks.

Pascal Mattia Esser, Leena C. Vankadara, Debarghya Ghoshdastidar

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Improvement on Incremental Spectral Clustering.

Nil Ayday, Debarghya Ghoshdastidar

Proceedings of the LWDA 2021 Workshops: FGWM, 2021

Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models.

Leena C. Vankadara, Sebastian Bordt, Ulrike von Luxburg, Debarghya Ghoshdastidar

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Near-Optimal Comparison Based Clustering.

Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On the optimality of kernels for high-dimensional clustering.

Leena Chennuru Vankadara, Debarghya Ghoshdastidar

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Foundations of Comparison-Based Hierarchical Clustering.

Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Practical Methods for Graph Two-Sample Testing.

Debarghya Ghoshdastidar, Ulrike von Luxburg

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques.

Debarghya Ghoshdastidar, Ambedkar Dukkipati

J. Mach. Learn. Res., 2017

Two-Sample Tests for Large Random Graphs Using Network Statistics.

Debarghya Ghoshdastidar, Maurilio Gutzeit, Alexandra Carpentier, Ulrike von Luxburg

Proceedings of the 30th Conference on Learning Theory, 2017

Comparison-Based Nearest Neighbor Search.

Siavash Haghiri, Debarghya Ghoshdastidar, Ulrike von Luxburg

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Learning With Jensen-Tsallis Kernels.

Debarghya Ghoshdastidar, Ajay P. Adsul, Ambedkar Dukkipati

IEEE Trans. Neural Networks Learn. Syst., 2016

Mixture modeling with compact support distributions for unsupervised learning.

Ambedkar Dukkipati, Debarghya Ghoshdastidar, Jinu Krishnan

Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

2015

A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning.

Debarghya Ghoshdastidar, Ambedkar Dukkipati

Proceedings of the 32nd International Conference on Machine Learning, 2015

Spectral Clustering Using Multilinear SVD: Analysis, Approximations and Applications.

Debarghya Ghoshdastidar, Ambedkar Dukkipati

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Smoothed Functional Algorithms for Stochastic Optimization Using <i>q</i>-Gaussian Distributions.

Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar

ACM Trans. Model. Comput. Simul., 2014

Newton-based stochastic optimization using q-Gaussian smoothed functional algorithms.

Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar

Autom., 2014

Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model.

Debarghya Ghoshdastidar, Ambedkar Dukkipati

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Spectral Clustering with Jensen-Type Kernels and Their Multi-point Extensions.

Debarghya Ghoshdastidar, Ambedkar Dukkipati, Ajay P. Adsul, Aparna S. Vijayan

Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

2013

Generative Maximum Entropy Learning for Multiclass Classification.

Ambedkar Dukkipati, Gaurav Pandey, Debarghya Ghoshdastidar, Paramita Koley, D. M. V. Satya Sriram

Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

On Power-Law Kernels, Corresponding Reproducing Kernel Hilbert Space and Applications.

Debarghya Ghoshdastidar, Ambedkar Dukkipati

Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013

2012

Smoothed Functional Algorithms for Stochastic Optimization using q-Gaussian Distributions.

Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar

CoRR, 2012

Maximum Entropy with Maximum J-Divergence Discrimination for Text Classification

D. M. V. Satya Sriram, Gaurav Pandey, Debarghya Ghoshdastidar, Ambedkar Dukkipati, M. Narasimha Murty

CoRR, 2012

On q-Gaussian kernel and its Reproducing Kernel Hilbert Space

Debarghya Ghoshdastidar, Ambedkar Dukkipati

CoRR, 2012

q-Gaussian based Smoothed Functional Algorithm for Stochastic Optimization

Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar

CoRR, 2012

q-Gaussian based Smoothed Functional algorithms for stochastic optimization.

Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar

Proceedings of the 2012 IEEE International Symposium on Information Theory, 2012