Debarghya Ghoshdastidar
24 papers · 2014–2026 · 10 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (43) π Conference Polyglot Β· Gold π Academic Marathon Β· Half π Interdisciplinary Bridge Β· Gold π£ Hot Topic Early Bird
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Deep Specialist
(11)
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Grand Slam
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Keyword Champion
(2)
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Trend Setter
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The Questioner
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Unstoppable Β· Silver
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Keyword Collector
(102)
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Century Club Β· Bronze
Venues
Top co-authors
Research topics
Machine Learning
(22)
Core Methods
(18)
Optimization & Theory
(11)
Deep Learning
(7)
Mathematics & Optimization
(7)
Artificial Intelligence
(6)
Learning Types
(6)
Architectures
(5)
Mathematics
(5)
Core AI
(4)
Optimization
(4)
Computer Science
(2)
Data Science & Analytics
(2)
Applications Β· Data Science & Analytics
(2)
Applications Β· Computer Science
(2)
Keywords
tensor decomposition
(4)
representation learning
(4)
kernel methods
(4)
neural tangent kernel
(3)
self-supervised learning
(3)
graph neural network
(3)
spectral clustering
(3)
kernel clustering
(3)
node classification
(3)
uniform hypergraph
(2)
comparison-based learning
(2)
kernel k-mean
(2)
principal component analysis
(2)
hypergraph partitioning
(2)
contrastive learning
(2)
generalization bound
(2)
tensor method
(2)
consistency analysis
(1)
transductive learning
(1)
statistical consistency
(1)
Papers
Explainable Clustering of Mixture Models
UAI 2026
Gaussian Process Limit Reveals Structural Benefits of Graph Transformers
UAI 2026
PAC-Bayesian Analysis of the Surrogate Relation between Joint Embedding and Supervised Downstream Losses
ALT 2026
Exact Certification of (Graph) Neural Networks Against Label Poisoning
ICLR 2025
Infinite Width Limits of Self Supervised Neural Networks
AISTATS 2025
When Can We Approximate Wide Contrastive Models with Neural Tangent Kernels and Principal Component Analysis?
AAAI 2025
Explaining Kernel Clustering via Decision Trees
ICLR 2024
Non-parametric Representation Learning with Kernels
AAAI 2024
Improved Representation Learning Through Tensorized Autoencoders
AISTATS 2023
Causal forecasting: generalization bounds for autoregressive models
UAI 2022
Graphon based Clustering and Testing of Networks: Algorithms and Theory
ICLR 2022
Interpolation and Regularization for Causal Learning
NeurIPS 2022
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks
NeurIPS 2021
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models
AISTATS 2021
Near-Optimal Comparison Based Clustering
NeurIPS 2020
On the optimality of kernels for high-dimensional clustering
AISTATS 2020
Foundations of Comparison-Based Hierarchical Clustering
NeurIPS 2019
Practical Methods for Graph Two-Sample Testing
NeurIPS 2018
Comparison-Based Nearest Neighbor Search
AISTATS 2017
Two-Sample Tests for Large Random Graphs Using Network Statistics
COLT 2017
Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques
JMLR 2017
A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning
ICML 2015
Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model
NeurIPS 2014
Spectral Clustering with Jensen-type Kernels and their Multi-point Extensions
CVPR 2014