Debarghya Ghoshdastidar

Orcid: 0000-0003-0202-7007

Affiliations:
  • Technical University of Munich, Germany
  • Eberhard Karls Universität of Tübingen, Germany
  • Indian Institute of Science, Bangalore, India


According to our database1, Debarghya Ghoshdastidar authored at least 40 papers between 2012 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
On the Stability of Gradient Descent for Large Learning Rate.
CoRR, 2024

Explaining Kernel Clustering via Decision Trees.
CoRR, 2024

Non-parametric Representation Learning with Kernels.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
A Revenue Function for Comparison-Based Hierarchical Clustering.
Trans. Mach. Learn. Res., 2023

Representation Learning Dynamics of Self-Supervised Models.
CoRR, 2023

Fast Adaptive Test-Time Defense with Robust Features.
CoRR, 2023

Wasserstein Projection Pursuit of Non-Gaussian Signals.
CoRR, 2023

Improved Representation Learning Through Tensorized Autoencoders.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
A Consistent Estimator for Confounding Strength.
CoRR, 2022

Representation Power of Graph Convolutions : Neural Tangent Kernel Analysis.
CoRR, 2022

Causal forecasting: generalization bounds for autoregressive models.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Interpolation and Regularization for Causal Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Graphon based Clustering and Testing of Networks: Algorithms and Theory.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Causal Forecasting: Generalization Bounds for Autoregressive Models.
CoRR, 2021

New Insights into Graph Convolutional Networks using Neural Tangent Kernels.
CoRR, 2021

Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Improvement on Incremental Spectral Clustering.
Proceedings of the LWDA 2021 Workshops: FGWM, 2021

Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Near-Optimal Comparison Based Clustering.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On the optimality of kernels for high-dimensional clustering.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Foundations of Comparison-Based Hierarchical Clustering.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018
Practical Methods for Graph Two-Sample Testing.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017
Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques.
J. Mach. Learn. Res., 2017

Two-Sample Tests for Large Random Graphs Using Network Statistics.
Proceedings of the 30th Conference on Learning Theory, 2017

Comparison-Based Nearest Neighbor Search.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
Learning With Jensen-Tsallis Kernels.
IEEE Trans. Neural Networks Learn. Syst., 2016

Mixture modeling with compact support distributions for unsupervised learning.
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

2015
A Provable Generalized Tensor Spectral Method for Uniform Hypergraph Partitioning.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Spectral Clustering Using Multilinear SVD: Analysis, Approximations and Applications.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014
Smoothed Functional Algorithms for Stochastic Optimization Using <i>q</i>-Gaussian Distributions.
ACM Trans. Model. Comput. Simul., 2014

Newton-based stochastic optimization using q-Gaussian smoothed functional algorithms.
Autom., 2014

Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Spectral Clustering with Jensen-Type Kernels and Their Multi-point Extensions.
Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

2013
Generative Maximum Entropy Learning for Multiclass Classification.
Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

On Power-Law Kernels, Corresponding Reproducing Kernel Hilbert Space and Applications.
Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013

2012
Smoothed Functional Algorithms for Stochastic Optimization using q-Gaussian Distributions.
CoRR, 2012

Maximum Entropy with Maximum J-Divergence Discrimination for Text Classification
CoRR, 2012

On q-Gaussian kernel and its Reproducing Kernel Hilbert Space
CoRR, 2012

q-Gaussian based Smoothed Functional Algorithm for Stochastic Optimization
CoRR, 2012

q-Gaussian based Smoothed Functional algorithms for stochastic optimization.
Proceedings of the 2012 IEEE International Symposium on Information Theory, 2012


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