Debarghya Ghoshdastidar | DeepAI

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∙ 09/05/2023

Non-Parametric Representation Learning with Kernels

Unsupervised and self-supervised representation learning has become popu...

0 Pascal Esser, et al. ∙

∙ 09/05/2023

Representation Learning Dynamics of Self-Supervised Models

Self-Supervised Learning (SSL) is an important paradigm for learning rep...

0 Pascal Esser, et al. ∙

∙ 07/21/2023

Fast Adaptive Test-Time Defense with Robust Features

Adaptive test-time defenses are used to improve the robustness of deep n...

0 Anurag Singh, et al. ∙

∙ 02/24/2023

Wasserstein Projection Pursuit of Non-Gaussian Signals

We consider the general dimensionality reduction problem of locating in ...

0 Satyaki Mukherjee, et al. ∙

∙ 12/02/2022

Improved Representation Learning Through Tensorized Autoencoders

The central question in representation learning is what constitutes a go...

0 Pascal Mattia Esser, et al. ∙

∙ 11/29/2022

A Revenue Function for Comparison-Based Hierarchical Clustering

Comparison-based learning addresses the problem of learning when, instea...

0 Aishik Mandal, et al. ∙

∙ 11/03/2022

A Consistent Estimator for Confounding Strength

Regression on observational data can fail to capture a causal relationsh...

0 Luca Rendsburg, et al. ∙

∙ 10/18/2022

Representation Power of Graph Convolutions : Neural Tangent Kernel Analysis

The fundamental principle of Graph Neural Networks (GNNs) is to exploit ...

0 Mahalakshmi Sabanayagam, et al. ∙

∙ 02/18/2022

Interpolation and Regularization for Causal Learning

We study the problem of learning causal models from observational data t...

0 Leena Chennuru Vankadara, et al. ∙

∙ 12/07/2021

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

In recent years, several results in the supervised learning setting sugg...

0 Pascal Mattia Esser, et al. ∙

∙ 11/18/2021

Causal Forecasting:Generalization Bounds for Autoregressive Models

Despite the increasing relevance of forecasting methods, the causal impl...

0 Leena Chennuru Vankadara, et al. ∙

∙ 10/18/2021

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

Despite the ubiquity of kernel-based clustering, surprisingly few statis...

0 Leena Chennuru Vankadara, et al. ∙

∙ 10/08/2021

New Insights into Graph Convolutional Networks using Neural Tangent Kernels

Graph Convolutional Networks (GCNs) have emerged as powerful tools for l...

0 Mahalakshmi Sabanayagam, et al. ∙

∙ 10/06/2021

Graphon based Clustering and Testing of Networks: Algorithms and Theory

Network-valued data are encountered in a wide range of applications and ...

0 Mahalakshmi Sabanayagam, et al. ∙

∙ 10/08/2020

Near-Optimal Comparison Based Clustering

The goal of clustering is to group similar objects into meaningful parti...

0 Michaël Perrot, et al. ∙

∙ 12/01/2019

On the optimality of kernels for high-dimensional clustering

This paper studies the optimality of kernel methods in high-dimensional ...

0 Leena Chennuru Vankadara, et al. ∙

∙ 11/30/2018

Practical methods for graph two-sample testing

Hypothesis testing for graphs has been an important tool in applied rese...

0 Debarghya Ghoshdastidar, et al. ∙

∙ 11/02/2018

Foundations of Comparison-Based Hierarchical Clustering

We address the classical problem of hierarchical clustering, but in a fr...

0 Debarghya Ghoshdastidar, et al. ∙

∙ 07/04/2017

Two-sample Hypothesis Testing for Inhomogeneous Random Graphs

The study of networks leads to a wide range of high dimensional inferenc...

0 Debarghya Ghoshdastidar, et al. ∙

∙ 05/17/2017

Two-Sample Tests for Large Random Graphs Using Network Statistics

We consider a two-sample hypothesis testing problem, where the distribut...

0 Debarghya Ghoshdastidar, et al. ∙

∙ 04/05/2017

Comparison Based Nearest Neighbor Search

We consider machine learning in a comparison-based setting where we are ...

0 Siavash Haghiri, et al. ∙

∙ 02/21/2016

Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques

In a series of recent works, we have generalised the consistency results...

0 Debarghya Ghoshdastidar, et al. ∙

∙ 05/07/2015

Consistency of Spectral Hypergraph Partitioning under Planted Partition Model

Hypergraph partitioning lies at the heart of a number of problems in mac...

0 Debarghya Ghoshdastidar, et al. ∙

∙ 04/09/2012

On Power-law Kernels, corresponding Reproducing Kernel Hilbert Space and Applications

The role of kernels is central to machine learning. Motivated by the imp...

0 Debarghya Ghoshdastidar, et al. ∙

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