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ORCIDDebarghya Ghoshdastidar
Person information
- affiliation: Technical University of Munich, Germany
- affiliation: Eberhard Karls Universität of Tübingen, Germany
- affiliation: Indian Institute of Science, Bangalore, India
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2020 – today
- 2024
[j7]Pascal Mattia Esser, Satyaki Mukherjee, Debarghya Ghoshdastidar:
Representation Learning Dynamics of Self-Supervised Models. Trans. Mach. Learn. Res. 2024 (2024)- [c23]Pascal Mattia Esser, Maximilian Fleissner, Debarghya Ghoshdastidar:
Non-parametric Representation Learning with Kernels. AAAI 2024: 11910-11918 - [c22]Maximilian Fleissner, Leena Chennuru Vankadara, Debarghya Ghoshdastidar:
Explaining Kernel Clustering via Decision Trees. ICLR 2024 - [i30]Maximilian Fleissner, Leena Chennuru Vankadara, Debarghya Ghoshdastidar:
Explaining Kernel Clustering via Decision Trees. CoRR abs/2402.09881 (2024) - [i29]Alexandru Craciun, Debarghya Ghoshdastidar:
On the Stability of Gradient Descent for Large Learning Rate. CoRR abs/2402.13108 (2024) - [i28]Gautham Govind Anil, Pascal Mattia Esser, Debarghya Ghoshdastidar:
When can we Approximate Wide Contrastive Models with Neural Tangent Kernels and Principal Component Analysis? CoRR abs/2403.08673 (2024) - [i27]Lukas Gosch, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar, Stephan Günnemann:
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks. CoRR abs/2407.10867 (2024) - 2023
- [j6]Aishik Mandal, Michaël Perrot, Debarghya Ghoshdastidar:
A Revenue Function for Comparison-Based Hierarchical Clustering. Trans. Mach. Learn. Res. 2023 (2023) - [j5]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) - [c21]Pascal Mattia Esser, Satyaki Mukherjee, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar:
Improved Representation Learning Through Tensorized Autoencoders. AISTATS 2023: 8294-8307 - [i26]Satyaki Mukherjee, Soumendu Sundar Mukherjee
, Debarghya Ghoshdastidar:
Wasserstein Projection Pursuit of Non-Gaussian Signals. CoRR abs/2302.12693 (2023) - [i25]Anurag Singh, Mahalakshmi Sabanayagam, Krikamol Muandet, Debarghya Ghoshdastidar:
Fast Adaptive Test-Time Defense with Robust Features. CoRR abs/2307.11672 (2023) - [i24]Pascal Mattia Esser, Satyaki Mukherjee, Debarghya Ghoshdastidar:
Representation Learning Dynamics of Self-Supervised Models. CoRR abs/2309.02011 (2023) - [i23]Pascal Mattia Esser, Maximilian Fleissner, Debarghya Ghoshdastidar:
Non-Parametric Representation Learning with Kernels. CoRR abs/2309.02028 (2023) - 2022
- [c20]Mahalakshmi Sabanayagam, Leena Chennuru Vankadara, Debarghya Ghoshdastidar:
Graphon based Clustering and Testing of Networks: Algorithms and Theory. ICLR 2022 - [c19]Leena Chennuru Vankadara, Luca Rendsburg, Ulrike von Luxburg, Debarghya Ghoshdastidar:
Interpolation and Regularization for Causal Learning. NeurIPS 2022 - [c18]Leena Chennuru Vankadara, Philipp Michael Faller, Michaela Hardt, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing:
Causal forecasting: generalization bounds for autoregressive models. UAI 2022: 2002-2012 - [i22]Leena Chennuru Vankadara, Luca Rendsburg, Ulrike von Luxburg, Debarghya Ghoshdastidar:
Interpolation and Regularization for Causal Learning. CoRR abs/2202.09054 (2022) - [i21]Mahalakshmi Sabanayagam, Pascal Mattia Esser, Debarghya Ghoshdastidar:
Representation Power of Graph Convolutions : Neural Tangent Kernel Analysis. CoRR abs/2210.09809 (2022) - [i20]Luca Rendsburg, Leena Chennuru Vankadara, Debarghya Ghoshdastidar, Ulrike von Luxburg:
A Consistent Estimator for Confounding Strength. CoRR abs/2211.01903 (2022) - [i19]Aishik Mandal, Michaël Perrot, Debarghya Ghoshdastidar:
A Revenue Function for Comparison-Based Hierarchical Clustering. CoRR abs/2211.16459 (2022) - [i18]Pascal Mattia Esser, Satyaki Mukherjee, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar:
Improved Representation Learning Through Tensorized Autoencoders. CoRR abs/2212.01046 (2022) - 2021
- [c17]Leena C. Vankadara, Sebastian Bordt, Ulrike von Luxburg, Debarghya Ghoshdastidar:
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models. AISTATS 2021: 3817-3825 - [c16]Nil Ayday, Debarghya Ghoshdastidar:
Improvement on Incremental Spectral Clustering. LWDA 2021: 78-85 - [c15]Pascal Mattia Esser, Leena C. Vankadara, Debarghya Ghoshdastidar:
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks. NeurIPS 2021: 27043-27056 - [i17]Mahalakshmi Sabanayagam, Leena Chennuru Vankadara, Debarghya Ghoshdastidar:
Graphon based Clustering and Testing of Networks: Algorithms and Theory. CoRR abs/2110.02722 (2021) - [i16]Mahalakshmi Sabanayagam, Pascal Mattia Esser, Debarghya Ghoshdastidar:
New Insights into Graph Convolutional Networks using Neural Tangent Kernels. CoRR abs/2110.04060 (2021) - [i15]Leena C. Vankadara, Sebastian Bordt, Ulrike von Luxburg, Debarghya Ghoshdastidar:
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models. CoRR abs/2110.09476 (2021) - [i14]Leena C. Vankadara, Philipp Michael Faller, Lenon Minorics, Debarghya Ghoshdastidar, Dominik Janzing:
Causal Forecasting: Generalization Bounds for Autoregressive Models. CoRR abs/2111.09831 (2021) - [i13]Pascal Mattia Esser, Leena C. Vankadara, Debarghya Ghoshdastidar:
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks. CoRR abs/2112.03968 (2021) - 2020
- [c14]Leena Chennuru Vankadara, Debarghya Ghoshdastidar:
On the optimality of kernels for high-dimensional clustering. AISTATS 2020: 2185-2195 - [c13]Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar:
Near-Optimal Comparison Based Clustering. NeurIPS 2020 - [i12]Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar:
Near-Optimal Comparison Based Clustering. CoRR abs/2010.03918 (2020)
2010 – 2019
- 2019
- [c12]Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg:
Foundations of Comparison-Based Hierarchical Clustering. NeurIPS 2019: 7454-7464 - [i11]Leena Chennuru Vankadara, Debarghya Ghoshdastidar:
On the optimality of kernels for high-dimensional clustering. CoRR abs/1912.00458 (2019) - 2018
- [c11]Debarghya Ghoshdastidar, Ulrike von Luxburg:
Practical Methods for Graph Two-Sample Testing. NeurIPS 2018: 3019-3028 - [i10]Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg:
Foundations of Comparison-Based Hierarchical Clustering. CoRR abs/1811.00928 (2018) - [i9]Debarghya Ghoshdastidar, Ulrike von Luxburg:
Practical methods for graph two-sample testing. CoRR abs/1811.12752 (2018) - 2017
- [j4]Debarghya Ghoshdastidar, Ambedkar Dukkipati:
Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques. J. Mach. Learn. Res. 18: 50:1-50:41 (2017) - [c10]Siavash Haghiri, Debarghya Ghoshdastidar, Ulrike von Luxburg:
Comparison-Based Nearest Neighbor Search. AISTATS 2017: 851-859 - [c9]Debarghya Ghoshdastidar, Maurilio Gutzeit, Alexandra Carpentier, Ulrike von Luxburg:
Two-Sample Tests for Large Random Graphs Using Network Statistics. COLT 2017: 954-977 - [i8]Siavash Haghiri, Debarghya Ghoshdastidar, Ulrike von Luxburg:
Comparison Based Nearest Neighbor Search. CoRR abs/1704.01460 (2017) - 2016
- [j3]Debarghya Ghoshdastidar, Ajay P. Adsul, Ambedkar Dukkipati:
Learning With Jensen-Tsallis Kernels. IEEE Trans. Neural Networks Learn. Syst. 27(10): 2108-2119 (2016) - [c8]Ambedkar Dukkipati, Debarghya Ghoshdastidar, Jinu Krishnan:
Mixture modeling with compact support distributions for unsupervised learning. IJCNN 2016: 2706-2713 - [i7]Debarghya Ghoshdastidar, Ambedkar Dukkipati:
Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques. CoRR abs/1602.06516 (2016) - 2015
- [c7]Debarghya Ghoshdastidar, Ambedkar Dukkipati:
Spectral Clustering Using Multilinear SVD: Analysis, Approximations and Applications. AAAI 2015: 2610-2616 - [c6]Debarghya Ghoshdastidar, Ambedkar Dukkipati:
A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning. ICML 2015: 400-409 - 2014
- [j2]Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar:
Newton-based stochastic optimization using q-Gaussian smoothed functional algorithms. Autom. 50(10): 2606-2614 (2014) - [j1]Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar:
Smoothed Functional Algorithms for Stochastic Optimization Using q-Gaussian Distributions. ACM Trans. Model. Comput. Simul. 24(3): 17:1-17:26 (2014) - [c5]Debarghya Ghoshdastidar, Ambedkar Dukkipati, Ajay P. Adsul, Aparna S. Vijayan:
Spectral Clustering with Jensen-Type Kernels and Their Multi-point Extensions. CVPR 2014: 1472-1477 - [c4]Debarghya Ghoshdastidar, Ambedkar Dukkipati:
Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model. NIPS 2014: 397-405 - [i6]Debarghya Ghoshdastidar, Ambedkar Dukkipati, Ajay P. Adsul, Aparna S. Vijayan:
Spectral Clustering with Jensen-type kernels and their multi-point extensions. CoRR abs/1403.4378 (2014) - 2013
- [c3]Debarghya Ghoshdastidar, Ambedkar Dukkipati:
On Power-Law Kernels, Corresponding Reproducing Kernel Hilbert Space and Applications. AAAI 2013: 365-371 - [c2]Ambedkar Dukkipati, Gaurav Pandey, Debarghya Ghoshdastidar, Paramita Koley, D. M. V. Satya Sriram:
Generative Maximum Entropy Learning for Multiclass Classification. ICDM 2013: 141-150 - [i5]Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar:
Newton based Stochastic Optimization using q-Gaussian Smoothed Functional Algorithms. CoRR abs/1311.2296 (2013) - 2012
- [c1]Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar:
q-Gaussian based Smoothed Functional algorithms for stochastic optimization. ISIT 2012: 1059-1063 - [i4]Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar:
q-Gaussian based Smoothed Functional Algorithm for Stochastic Optimization. CoRR abs/1202.5665 (2012) - [i3]Debarghya Ghoshdastidar, Ambedkar Dukkipati:
On q-Gaussian kernel and its Reproducing Kernel Hilbert Space. CoRR abs/1204.1800 (2012) - [i2]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 abs/1205.0651 (2012) - [i1]Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar:
Smoothed Functional Algorithms for Stochastic Optimization using q-Gaussian Distributions. CoRR abs/1206.4832 (2012)
Coauthor Index
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last updated on 2024-10-07 21:19 CEST by the dblp team
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