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09/05/2023
Non-Parametric Representation Learning with Kernels
Unsupervised and self-supervised representation learning has become popu...
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09/05/2023
Representation Learning Dynamics of Self-Supervised Models
Self-Supervised Learning (SSL) is an important paradigm for learning rep...
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07/21/2023
Fast Adaptive Test-Time Defense with Robust Features
Adaptive test-time defenses are used to improve the robustness of deep n...
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02/24/2023
Wasserstein Projection Pursuit of Non-Gaussian Signals
We consider the general dimensionality reduction problem of locating in ...
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12/02/2022
Improved Representation Learning Through Tensorized Autoencoders
The central question in representation learning is what constitutes a go...
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11/29/2022
A Revenue Function for Comparison-Based Hierarchical Clustering
Comparison-based learning addresses the problem of learning when, instea...
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11/03/2022
A Consistent Estimator for Confounding Strength
Regression on observational data can fail to capture a causal relationsh...
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10/18/2022
Representation Power of Graph Convolutions : Neural Tangent Kernel Analysis
The fundamental principle of Graph Neural Networks (GNNs) is to exploit ...
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02/18/2022
Interpolation and Regularization for Causal Learning
We study the problem of learning causal models from observational data t...
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12/07/2021
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks
In recent years, several results in the supervised learning setting sugg...
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11/18/2021
Causal Forecasting:Generalization Bounds for Autoregressive Models
Despite the increasing relevance of forecasting methods, the causal impl...
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10/18/2021
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models
Despite the ubiquity of kernel-based clustering, surprisingly few statis...
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10/08/2021
New Insights into Graph Convolutional Networks using Neural Tangent Kernels
Graph Convolutional Networks (GCNs) have emerged as powerful tools for l...
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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 ...
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10/08/2020
Near-Optimal Comparison Based Clustering
The goal of clustering is to group similar objects into meaningful parti...
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12/01/2019
On the optimality of kernels for high-dimensional clustering
This paper studies the optimality of kernel methods in high-dimensional ...
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11/30/2018
Practical methods for graph two-sample testing
Hypothesis testing for graphs has been an important tool in applied rese...
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11/02/2018
Foundations of Comparison-Based Hierarchical Clustering
We address the classical problem of hierarchical clustering, but in a fr...
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07/04/2017
Two-sample Hypothesis Testing for Inhomogeneous Random Graphs
The study of networks leads to a wide range of high dimensional inferenc...
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05/17/2017
Two-Sample Tests for Large Random Graphs Using Network Statistics
We consider a two-sample hypothesis testing problem, where the distribut...
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04/05/2017
Comparison Based Nearest Neighbor Search
We consider machine learning in a comparison-based setting where we are ...
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02/21/2016
Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques
In a series of recent works, we have generalised the consistency results...
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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...
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04/09/2012
