Debarghya Ghoshdastidar
Professor
Studies the theory of machine learning, including the statistical understanding of clustering, kernel methods and learning on networks.
Debarghya Ghoshdastidar's lab at TUM works on learning theory & optimization. 26 papers since 2023, 10 at top venues, mostly AAAI and AISTATS. 7 PhD students. 3 alumni.
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 38%
- PhD students
- 7
- MS students
- 0
- alumni listed
- 3
Papers by yearfrom the lab homepage, 2023–2026
- 9 papers2026 · T1 3 · collecting
- 6 papers2025 · T1 4
- 7 papers2024 · T1 2
- 4 papers2023 · T1 1
T1 venuesOtherVenues: AAAI, AISTATS, ICLR, UAI, ALT
Research focuspaper titles that use each topic's words
- Learning theory & optimization9 papers
- clustering2 papers
- kernel2 papers
- kernel methods0 papers
- understanding0 papers
Paper list
From the lab homepage.
20269 papers
- AsiaCCSALPHA: Active learning with PAC-Bayesian theory for android malware detection.
- UAI (T1)Decision trees for interpretable clusters in mixture models and deep representations.
- UAI (T1)Gaussian process limit reveals structural benefits of graph transformers.
- ICML (T1)Interpretable self-supervised learning via representer landmarks and Nyström approximation.
- ALTPAC-Bayesian analysis of the surrogate relation between joint embedding and supervised downstream losses.
- Journal of Multivariate Analysis (special issue on Dimension Reduction in Multivariate Analysis), Article 105530Recovering imbalanced clusters via gradient based projection pursuit.
- IEEE Open Journal of Control Systems, Special Section: Intersection of Machine Learning with ControlRobustness certificates for neural networks against data poisoning and evasion attacks.
- C. Büllte et al. (eds) Theoretical Foundations of Deep Learning, Springer NatureTheoretical foundations of representation learning using unlabeled data: Statistics and optimization.
- Journal of Multivariate Analysis (special issue on Dimension Reduction in Multivariate Analysis), Article 105535Wasserstein projection pursuit of non-Gaussian signals.
20256 papers
- ICLR (T1)Exact certification of (graph) neural networks against label poisoning.
- AISTATS (T1)Infinite width limits of self supervised neural networks.
- NeurIPS (T1)Non-Singularity of the Gradient Descent Map for Neural Networks with Piecewise Analytic Activations.
- Transactions of Machine LearningProvable robustness of (graph) neural networks against data poisoning and backdoor attacks.
- SIAM Journal on Data Science, 7(4)Tight PAC-Bayesian risk certificates for contrastive learning.
- AAAI (T1)When can we approximate wide contrastive models with neural tangent kernels and principal component analysis?
20247 papers
- Mathematical Statistics and Learning, 7 (3/4): 189-220A consistent estimator for confounding strength.
- GNI Symposium & Expo on Artificial Intelligence for the Built WorldActive learning surrogates for enhanced reliability assessment of engineering systems.
- ICLR (T1)Explaining kernel clustering via decision trees.
- Transactions of Machine LearningFast adaptive test-time defense with robust features.
- AAAI (T1)Non-parametric representation learning with kernels.
- Transactions of Machine LearningRepresentation learning dynamics of self-supervised models.
- GNI Symposium & Expo on Artificial Intelligence for the Built WorldSelf-supervised learning approaches to improve residential housing energy prediction accuracy.
20234 papers
- Transactions of Machine Learning ResearchA revenue function for comparison-based hierarchical clustering.
- Transactions of Machine Learning ResearchAnalysis of convolutions, non-linearity and depth in graph neural networks using neural tangent kernel.
- AISTATS (T1)Improved representation learning through tensorized autoencoders.
- Mathematics of Modern Machine Learning Workshop, NeurIPSUnveiling the Hessian’s connection to the decision boundary.
Where alumni went3 listed
Apply to TUMRolling
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Computer Science Doctorate (CIT)
- Round
- Rolling (position-based)
- Opens
- —
- GRE
- See program page
- English
- Very good written and spoken English; no minimum test score
- Fee
- See program page
Confirmed on the program page.
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Data
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- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.