500 citations
- Indian Institute of Technology DelhiIN62 papers
- Adobe Systems (United States)US8 papers
- Centre National de la Recherche ScientifiqueFR6 papers
- Indian Institute of Technology BombayIN5 papers
- Arizona State UniversityUS4 papers
- Jadavpur UniversityIN4 papers
- Rochester Institute of TechnologyUS4 papers
- University of CalgaryCA4 papers
- University of DelhiIN4 papers
- All India Institute of Medical SciencesIN3 papers
- Delhi Technological UniversityIN3 papers
- HUN-REN Alfréd Rényi Institute of MathematicsHU3 papers
211 papers
Median-of-Means as an Extremal Convex Estimator and a Nonconvex Route to the Trimmed Oracle
Angshul Majumdar
We revisit median-of-means estimation from a deterministic optimization viewpoint and develop a family of block-Lp estimators for robust learning with heavy-tailed and adversariall…
Quantum-Stable Robust Principal Component Analysis: Theory and Evidence from NISQ Regimes
Angshul Majumdar
Robust Principal Component Analysis (RPCA) is a fundamental technique for extracting low-rank structures from data corrupted by sparse anomalies and noise. While classical RPCA and…
Clustering as Approximation by Constrained Projectors: Theory and Guarantees
Angshul Majumdar
This paper develops a unified theoretical framework showing that a broad family of clustering methods, including k-means, fuzzy c-means, kernel k-means, kernel FCM, and spectral cl…
Gaussian Volumetric Representation for Efficient Shear-Warp Visualization
Mayuri Mathur, Ojaswa Sharma
Medical image visualization requires volumetric rendering algorithms that preserve anatomical fidelity while maintaining high rendering speeds. To address the high computational co…
Channel-Adaptive Robust Aggregation for Over-the-Air Federated Learning in Heterogeneous Networks
Zubaida Fatima, Zubair Shaban, Yusuf Jamal +3
The growing demand for privacy-preserving, data-intensive applications such as IoT, augmented reality, and autonomous systems positions Federated Learning (FL) as a key enabler in…
Better and Worse with Scale: How Contextual Entrainment Diverges with Model Size
Dikshant Kukreja, Kshitij Sah, Gautam Gupta +5
Larger language models become simultaneously better and worse at handling contextual information -- better at ignoring false claims, worse at ignoring irrelevant tokens. We formali…