Debarghya Ghoshdastidar

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 45

Top Topics

Unsupervised Learning

Graph Partitioning

Top Venues

Trans. Mach. Learn. Res.

Publications

Maximilian Fleissner, Leena Chennuru Vankadara, Debarghya Ghoshdastidar
Explaining Kernel Clustering via Decision Trees. CoRR (2024)
Alexandru Craciun, Debarghya Ghoshdastidar
On the Stability of Gradient Descent for Large Learning Rate. CoRR (2024)
Gautham Govind Anil, Pascal Mattia Esser, Debarghya Ghoshdastidar
When can we Approximate Wide Contrastive Models with Neural Tangent Kernels and Principal Component Analysis? CoRR (2024)
Pascal Mattia Esser, Maximilian Fleissner, Debarghya Ghoshdastidar
Non-parametric Representation Learning with Kernels. AAAI (2024)
Maximilian Fleissner, Leena Chennuru Vankadara, Debarghya Ghoshdastidar
Explaining Kernel Clustering via Decision Trees. ICLR (2024)
Lukas Gosch, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar, Stephan Günnemann
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks. CoRR (2024)
Pascal Mattia Esser, Satyaki Mukherjee, Debarghya Ghoshdastidar
Representation Learning Dynamics of Self-Supervised Models. Trans. Mach. Learn. Res. 2024 (2024)
Aishik Mandal, Michaël Perrot, Debarghya Ghoshdastidar
A Revenue Function for Comparison-Based Hierarchical Clustering. Trans. Mach. Learn. Res. 2023 (2023)
Pascal Mattia Esser, Satyaki Mukherjee, Debarghya Ghoshdastidar
Representation Learning Dynamics of Self-Supervised Models. CoRR (2023)
Pascal Mattia Esser, Maximilian Fleissner, Debarghya Ghoshdastidar
Non-Parametric Representation Learning with Kernels. CoRR (2023)
Anurag Singh, Mahalakshmi Sabanayagam, Krikamol Muandet, Debarghya Ghoshdastidar
Fast Adaptive Test-Time Defense with Robust Features. CoRR (2023)
Pascal Mattia Esser, Satyaki Mukherjee, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar
Improved Representation Learning Through Tensorized Autoencoders. AISTATS (2023)
Mahalakshmi Sabanayagam, Pascal Mattia Esser, Debarghya Ghoshdastidar
Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernel. Trans. Mach. Learn. Res. 2023 (2023)
Satyaki Mukherjee, Soumendu Sundar Mukherjee, Debarghya Ghoshdastidar
Wasserstein Projection Pursuit of Non-Gaussian Signals. CoRR (2023)
Mahalakshmi Sabanayagam, Pascal Mattia Esser, Debarghya Ghoshdastidar
Representation Power of Graph Convolutions : Neural Tangent Kernel Analysis. CoRR (2022)
Pascal Mattia Esser, Satyaki Mukherjee, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar
Improved Representation Learning Through Tensorized Autoencoders. CoRR (2022)
Luca Rendsburg, Leena Chennuru Vankadara, Debarghya Ghoshdastidar, Ulrike von Luxburg
A Consistent Estimator for Confounding Strength. CoRR (2022)
Aishik Mandal, Michaël Perrot, Debarghya Ghoshdastidar
A Revenue Function for Comparison-Based Hierarchical Clustering. CoRR (2022)
Leena Chennuru Vankadara, Luca Rendsburg, Ulrike von Luxburg, Debarghya Ghoshdastidar
Interpolation and Regularization for Causal Learning. NeurIPS (2022)
Leena Chennuru Vankadara, Philipp Michael Faller, Michaela Hardt, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing
Causal forecasting: generalization bounds for autoregressive models. UAI (2022)
Leena Chennuru Vankadara, Luca Rendsburg, Ulrike von Luxburg, Debarghya Ghoshdastidar
Interpolation and Regularization for Causal Learning. CoRR (2022)
Mahalakshmi Sabanayagam, Leena Chennuru Vankadara, Debarghya Ghoshdastidar
Graphon based Clustering and Testing of Networks: Algorithms and Theory. ICLR (2022)
Nil Ayday, Debarghya Ghoshdastidar
Improvement on Incremental Spectral Clustering. LWDA (2021)
Leena Chennuru Vankadara, Sebastian Bordt, Ulrike von Luxburg, Debarghya Ghoshdastidar
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models. AISTATS (2021)
Mahalakshmi Sabanayagam, Leena Chennuru Vankadara, Debarghya Ghoshdastidar
Graphon based Clustering and Testing of Networks: Algorithms and Theory. CoRR (2021)
Pascal Mattia Esser, Leena Chennuru Vankadara, Debarghya Ghoshdastidar
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks. CoRR (2021)
Pascal Mattia Esser, Leena C. Vankadara, Debarghya Ghoshdastidar
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks. NeurIPS (2021)
Mahalakshmi Sabanayagam, Pascal Mattia Esser, Debarghya Ghoshdastidar
New Insights into Graph Convolutional Networks using Neural Tangent Kernels. CoRR (2021)
Leena Chennuru Vankadara, Sebastian Bordt, Ulrike von Luxburg, Debarghya Ghoshdastidar
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models. CoRR (2021)
Leena Chennuru Vankadara, Philipp Michael Faller, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing
Causal Forecasting: Generalization Bounds for Autoregressive Models. CoRR (2021)
Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar
Near-Optimal Comparison Based Clustering. NeurIPS (2020)
Leena Chennuru Vankadara, Debarghya Ghoshdastidar
On the optimality of kernels for high-dimensional clustering. AISTATS (2020)
Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar
Near-Optimal Comparison Based Clustering. CoRR (2020)
Leena Chennuru Vankadara, Debarghya Ghoshdastidar
On the optimality of kernels for high-dimensional clustering. CoRR (2019)
Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg
Foundations of Comparison-Based Hierarchical Clustering. NeurIPS (2019)
Debarghya Ghoshdastidar, Ulrike von Luxburg
Practical Methods for Graph Two-Sample Testing. NeurIPS (2018)
Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg
Foundations of Comparison-Based Hierarchical Clustering. CoRR (2018)
Debarghya Ghoshdastidar, Ulrike von Luxburg
Practical methods for graph two-sample testing. CoRR (2018)
Debarghya Ghoshdastidar, Maurilio Gutzeit, Alexandra Carpentier, Ulrike von Luxburg
Two-Sample Tests for Large Random Graphs Using Network Statistics. COLT (2017)
Siavash Haghiri, Debarghya Ghoshdastidar, Ulrike von Luxburg
Comparison Based Nearest Neighbor Search. CoRR (2017)
Debarghya Ghoshdastidar, Ambedkar Dukkipati
Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques. J. Mach. Learn. Res. 18 (2017)
Siavash Haghiri, Debarghya Ghoshdastidar, Ulrike von Luxburg
Comparison-Based Nearest Neighbor Search. AISTATS (2017)
Debarghya Ghoshdastidar, Ajay P. Adsul, Ambedkar Dukkipati
Learning With Jensen-Tsallis Kernels. IEEE Trans. Neural Networks Learn. Syst. 27 (10) (2016)
Debarghya Ghoshdastidar, Ambedkar Dukkipati
Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques. CoRR (2016)
Ambedkar Dukkipati, Debarghya Ghoshdastidar, Jinu Krishnan
Mixture modeling with compact support distributions for unsupervised learning. IJCNN (2016)
Debarghya Ghoshdastidar, Ambedkar Dukkipati
A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning. ICML (2015)
Debarghya Ghoshdastidar, Ambedkar Dukkipati
Spectral Clustering Using Multilinear SVD: Analysis, Approximations and Applications. AAAI (2015)
Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar
-Gaussian Distributions. ACM Trans. Model. Comput. Simul. 24 (3) (2014)
Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar
Newton-based stochastic optimization using q-Gaussian smoothed functional algorithms. Autom. 50 (10) (2014)
Debarghya Ghoshdastidar, Ambedkar Dukkipati, Ajay P. Adsul, Aparna S. Vijayan
Spectral Clustering with Jensen-type kernels and their multi-point extensions. CoRR (2014)
Debarghya Ghoshdastidar, Ambedkar Dukkipati
Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model. NIPS (2014)
Debarghya Ghoshdastidar, Ambedkar Dukkipati, Ajay P. Adsul, Aparna S. Vijayan
Spectral Clustering with Jensen-Type Kernels and Their Multi-point Extensions. CVPR (2014)
Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar
Newton based Stochastic Optimization using q-Gaussian Smoothed Functional Algorithms. CoRR (2013)
Ambedkar Dukkipati, Gaurav Pandey, Debarghya Ghoshdastidar, Paramita Koley, D. M. V. Satya Sriram
Generative Maximum Entropy Learning for Multiclass Classification. ICDM (2013)
Debarghya Ghoshdastidar, Ambedkar Dukkipati
On Power-Law Kernels, Corresponding Reproducing Kernel Hilbert Space and Applications. AAAI (2013)
Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar
Smoothed Functional Algorithms for Stochastic Optimization using q-Gaussian Distributions. CoRR (2012)
D. M. V. Satya Sriram, Gaurav Pandey, Debarghya Ghoshdastidar, Ambedkar Dukkipati, M. Narasimha Murty
Maximum Entropy with Maximum J-Divergence Discrimination for Text Classification CoRR (2012)
Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar
q-Gaussian based Smoothed Functional algorithms for stochastic optimization. ISIT (2012)
Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar
q-Gaussian based Smoothed Functional Algorithm for Stochastic Optimization CoRR (2012)
Debarghya Ghoshdastidar, Ambedkar Dukkipati
On q-Gaussian kernel and its Reproducing Kernel Hilbert Space CoRR (2012)