3 papers
cs.CV2026
ManifoldGD: Training-Free Hierarchical Manifold Guidance for Diffusion-Based Dataset Distillation
Ayush Roy, Wei-Yang Alex Lee, Rudrasis Chakraborty +1
In recent times, large datasets hinder efficient model training while also containing redundant concepts. Dataset distillation aims to synthesize compact datasets that preserve the…
cs.LG2026
Beyond Pooling: Matching for Robust Generalization under Data Heterogeneity
Ayush Roy, Rudrasis Chakraborty, Lav Varshney +1
Pooling heterogeneous datasets across domains is a common strategy in representation learning, but naive pooling can amplify distributional asymmetries and yield biased estimators,…
cs.LG2025
FoGE: Fock Space inspired encoding for graph prompting
Sotirios Panagiotis Chytas, Rudrasis Chakraborty, Vikas Singh
Recent results show that modern Large Language Models (LLM) are indeed capable of understanding and answering questions about structured data such as graphs. This new paradigm can…