3 papers
cs.DS2026
AutoCSF: Provably Space-Efficient Indexing of Skewed Key-Value Workloads via Filter-Augmented Compressed Static Functions
David Torres Ramos, Vihan Lakshman, Chen Luo +2
We study the problem of building space-efficient, in-memory indexes for massive key-value datasets with highly skewed value distributions. This challenge arises in many data-intens…
cs.IR2026
Breaking the Curse of Dimensionality: On the Stability of Modern Vector Retrieval
Vihan Lakshman, Blaise Munyampirwa, Julian Shun +1
Modern vector databases enable efficient retrieval over high-dimensional neural embeddings, powering applications from web search to retrieval-augmented generation. However, classi…
cs.LG2025
Down with the Hierarchy: The 'H' in HNSW Stands for "Hubs"
Blaise Munyampirwa, Vihan Lakshman, Benjamin Coleman
Driven by recent breakthrough advances in neural representation learning, approximate near-neighbor (ANN) search over vector embeddings has emerged as a critical computational work…