q-Gaussian based Smoothed Functional algorithms for stochastic optimization. - Debarghya Ghoshdastidar; Ambedkar Dukkipati; Shalabh Bhatnagar (ISIT, 2012)

q-Gaussian based Smoothed Functional Algorithm for Stochastic Optimization - Debarghya Ghoshdastidar; Ambedkar Dukkipati; Shalabh Bhatnagar (CoRR, 2012)

On q-Gaussian kernel and its Reproducing Kernel Hilbert Space - Debarghya Ghoshdastidar; Ambedkar Dukkipati (CoRR, 2012)

Maximum Entropy with Maximum J-Divergence Discrimination for Text Classification - D. M. V. Satya Sriram; Gaurav Pandey 001; Debarghya Ghoshdastidar; Ambedkar Dukkipati; M. Narasimha Murty (CoRR, 2012)

Smoothed Functional Algorithms for Stochastic Optimization using q-Gaussian Distributions. - Debarghya Ghoshdastidar; Ambedkar Dukkipati; Shalabh Bhatnagar (CoRR, 2012)

On Power-Law Kernels, Corresponding Reproducing Kernel Hilbert Space and Applications. - Debarghya Ghoshdastidar; Ambedkar Dukkipati (AAAI, 2013)

Generative Maximum Entropy Learning for Multiclass Classification. - Ambedkar Dukkipati; Gaurav Pandey 001; Debarghya Ghoshdastidar; Paramita Koley; D. M. V. Satya Sriram (ICDM, 2013)

Spectral Clustering with Jensen-Type Kernels and Their Multi-point Extensions. - Debarghya Ghoshdastidar; Ambedkar Dukkipati; Ajay P. Adsul; Aparna S. Vijayan (CVPR, 2014)

Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model. - Debarghya Ghoshdastidar; Ambedkar Dukkipati (NIPS, 2014)

Newton-based stochastic optimization using q-Gaussian smoothed functional algorithms. - Debarghya Ghoshdastidar; Ambedkar Dukkipati; Shalabh Bhatnagar (Autom., 2014)

Smoothed Functional Algorithms for Stochastic Optimization Using q-Gaussian Distributions. - Debarghya Ghoshdastidar; Ambedkar Dukkipati; Shalabh Bhatnagar (ACM Trans. Model. Comput. Simul., 2014)

Spectral Clustering Using Multilinear SVD: Analysis, Approximations and Applications. - Debarghya Ghoshdastidar; Ambedkar Dukkipati (AAAI, 2015)

A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning. - Debarghya Ghoshdastidar; Ambedkar Dukkipati (ICML, 2015)

Mixture modeling with compact support distributions for unsupervised learning. - Ambedkar Dukkipati; Debarghya Ghoshdastidar; Jinu Krishnan (IJCNN, 2016)

Learning With Jensen-Tsallis Kernels. - Debarghya Ghoshdastidar; Ajay P. Adsul; Ambedkar Dukkipati (IEEE Trans. Neural Networks Learn. Syst., 2016)

Comparison-Based Nearest Neighbor Search. - Siavash Haghiri; Debarghya Ghoshdastidar; Ulrike von Luxburg (AISTATS, 2017)

Two-Sample Tests for Large Random Graphs Using Network Statistics. - Debarghya Ghoshdastidar; Maurilio Gutzeit; Alexandra Carpentier; Ulrike von Luxburg (COLT, 2017)

Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques. - Debarghya Ghoshdastidar; Ambedkar Dukkipati (J. Mach. Learn. Res., 2017)

Practical Methods for Graph Two-Sample Testing. - Debarghya Ghoshdastidar; Ulrike von Luxburg (NeurIPS, 2018)

Foundations of Comparison-Based Hierarchical Clustering. - Debarghya Ghoshdastidar; Michaël Perrot; Ulrike von Luxburg (NeurIPS, 2019)

On the optimality of kernels for high-dimensional clustering. - Leena Chennuru Vankadara; Debarghya Ghoshdastidar (AISTATS, 2020)

Near-Optimal Comparison Based Clustering. - Michaël Perrot; Pascal Mattia Esser; Debarghya Ghoshdastidar (NeurIPS, 2020)

Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models. - Leena C. Vankadara; Sebastian Bordt; Ulrike von Luxburg; Debarghya Ghoshdastidar (AISTATS, 2021)

Improvement on Incremental Spectral Clustering. - Nil Ayday; Debarghya Ghoshdastidar (LWDA, 2021)

Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks. - Pascal Mattia Esser; Leena C. Vankadara; Debarghya Ghoshdastidar (NeurIPS, 2021)

New Insights into Graph Convolutional Networks using Neural Tangent Kernels. - Mahalakshmi Sabanayagam; Pascal Mattia Esser; Debarghya Ghoshdastidar (CoRR, 2021)

Causal Forecasting: Generalization Bounds for Autoregressive Models. - Leena C. Vankadara; Philipp Michael Faller; Lenon Minorics; Debarghya Ghoshdastidar; Dominik Janzing (CoRR, 2021)

Graphon based Clustering and Testing of Networks: Algorithms and Theory. - Mahalakshmi Sabanayagam; Leena Chennuru Vankadara; Debarghya Ghoshdastidar (ICLR, 2022)

Interpolation and Regularization for Causal Learning. - Leena Chennuru Vankadara; Luca Rendsburg; Ulrike von Luxburg; Debarghya Ghoshdastidar (NeurIPS, 2022)

Causal forecasting: generalization bounds for autoregressive models. - Leena Chennuru Vankadara; Philipp Michael Faller; Michaela Hardt; Lenon Minorics; Debarghya Ghoshdastidar; Dominik Janzing (UAI, 2022)

Representation Power of Graph Convolutions : Neural Tangent Kernel Analysis. - Mahalakshmi Sabanayagam; Pascal Mattia Esser; Debarghya Ghoshdastidar (CoRR, 2022)

A Consistent Estimator for Confounding Strength. - Luca Rendsburg; Leena Chennuru Vankadara; Debarghya Ghoshdastidar; Ulrike von Luxburg (CoRR, 2022)

Improved Representation Learning Through Tensorized Autoencoders. - Pascal Mattia Esser; Satyaki Mukherjee; Mahalakshmi Sabanayagam; Debarghya Ghoshdastidar (AISTATS, 2023)

Wasserstein Projection Pursuit of Non-Gaussian Signals. - Satyaki Mukherjee; Soumendu Sundar Mukherjee; Debarghya Ghoshdastidar (CoRR, 2023)

Fast Adaptive Test-Time Defense with Robust Features. - Anurag Singh 006; Mahalakshmi Sabanayagam; Krikamol Muandet; Debarghya Ghoshdastidar (CoRR, 2023)

A Revenue Function for Comparison-Based Hierarchical Clustering. - Aishik Mandal; Michaël Perrot; Debarghya Ghoshdastidar (Trans. Mach. Learn. Res., 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)

Non-parametric Representation Learning with Kernels. - Pascal Mattia Esser; Maximilian Fleissner; Debarghya Ghoshdastidar (AAAI, 2024)

Explaining Kernel Clustering via Decision Trees. - Maximilian Fleissner; Leena Chennuru Vankadara; Debarghya Ghoshdastidar (ICLR, 2024)

On the Stability of Gradient Descent for Large Learning Rate. - Alexandru Craciun 001; Debarghya Ghoshdastidar (CoRR, 2024)

Decision Trees for Interpretable Clusters in Mixture Models and Deep Representations. - Maximilian Fleissner; Maedeh Zarvandi; Debarghya Ghoshdastidar (CoRR, 2024)

Data Augmentations Go Beyond Encoding Invariances: A Theoretical Study on Self-Supervised Learning. - Shlomo Libo Feigin; Maximilian Fleissner; Debarghya Ghoshdastidar (CoRR, 2024)

Tight PAC-Bayesian Risk Certificates for Contrastive Learning. - Anna Van Elst; Debarghya Ghoshdastidar (CoRR, 2024)

Robust Feature Inference: A Test-time Defense Strategy using Spectral Projections. - Anurag Singh 006; Mahalakshmi Sabanayagam; Krikamol Muandet; Debarghya Ghoshdastidar (Trans. Mach. Learn. Res., 2024)

Representation Learning Dynamics of Self-Supervised Models. - Pascal Mattia Esser; Satyaki Mukherjee; Debarghya Ghoshdastidar (Trans. Mach. Learn. Res., 2024)

When Can We Approximate Wide Contrastive Models with Neural Tangent Kernels and Principal Component Analysis? - Gautham Govind Anil; Pascal Mattia Esser; Debarghya Ghoshdastidar (AAAI, 2025)

Infinite Width Limits of Self Supervised Neural Networks. - Maximilian Fleissner; Gautham Govind Anil; Debarghya Ghoshdastidar (AISTATS, 2025)

Exact Certification of (Graph) Neural Networks Against Label Poisoning. - Mahalakshmi Sabanayagam; Lukas Gosch; Stephan Günnemann; Debarghya Ghoshdastidar (ICLR, 2025)

Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks. - Lukas Gosch; Mahalakshmi Sabanayagam; Debarghya Ghoshdastidar; Stephan Günnemann (Trans. Mach. Learn. Res., 2025)

A Probabilistic Model for Self-Supervised Learning. - Maximilian Fleissner; Pascal Mattia Esser; Debarghya Ghoshdastidar (CoRR, 2025)

Recovering Imbalanced Clusters via Gradient-Based Projection Pursuit. - Martin Eppert; Satyaki Mukherjee; Debarghya Ghoshdastidar (CoRR, 2025)

Attention Learning is Needed to Efficiently Learn Parity Function. - Yaomengxi Han; Debarghya Ghoshdastidar (CoRR, 2025)

Generalization Certificates for Adversarially Robust Bayesian Linear Regression. - Mahalakshmi Sabanayagam; Russell Tsuchida; Cheng Soon Ong; Debarghya Ghoshdastidar (CoRR, 2025)

Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders. - Jonghyun Ham; Maximilian Fleissner; Debarghya Ghoshdastidar (CoRR, 2025)