Publications

Interpolation and Regularization for Causal Learning

We study the problem of learning causal models from observational data through the lens of interpolation and its …

Causal Forecasting - Generalization Bounds for Autoregressive Models

Despite the increasing relevance of forecasting methods, the causal implications of these algorithms remain largely unexplored. This is …

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

In recent years, several results in the supervised learning setting suggested that classical statistical learning-theoretic measures, …

Graphon based Clustering and Testing of Networks - Algorithms and Theory

Network-valued data are encountered in a wide range of applications and pose challenges in learning due to their complex structure and …

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

Despite the ubiquity of kernel-based clustering, surprisingly few statistical guarantees exist beyond settings that consider strong …

Insights into Ordinal Embedding Algorithms - A Systematic Evaluation

The objective of ordinal embedding is to find a Euclidean representation of a set of abstract items, using only answers to triplet …

On the optimality of kernels for high-dimensional clustering

This paper studies the optimality of kernel methods in high-dimensional data clustering. Recent works have studied the large sample …

Metric embeddings for Machine learning

In this thesis, we initiate and perform an extensive study of the theory of metric embeddings in the context of Machine Learning. We …

Measures of distortion for machine learning

Given data from a general metric space, one of the standard machine learning pipelines is to first embed the data into a Euclidean …

A Robotic Home Assistant with Memory Aid Functionality

Created an object detection module for the system.