12 citations · 18 across the 13 of their papers we have counts for
14 papers
Geometric Kernel Diversity: An Axiomatic Diversity Measure in Euclidean Space
Ditbul Ban, Younghun Roh, Julian Shun +1
Quantifying the diversity of a set of objects is a fundamental problem in machine learning and statistics. Mironov and Prokhorenkova (2025) recently showed that three axioms-monoto…
PROTOCOL: Late Interaction Retrieval for Protein Homolog Search
Gabrielle Cohn, Rohan Gumaste, Minh Hoang +1
Protein homology search underlies function annotation, structure prediction, and evolutionary analysis, but remains challenging in the "twilight zone," where global sequence simila…
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…
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…
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…
On the Diminishing Returns of Width for Continual Learning
Etash Guha, Vihan Lakshman
While deep neural networks have demonstrated groundbreaking performance in various settings, these models often suffer from \emph{catastrophic forgetting} when trained on new tasks…
CAPS: A Practical Partition Index for Filtered Similarity Search
Gaurav Gupta, Jonah Yi, Benjamin Coleman +3
With the surging popularity of approximate near-neighbor search (ANNS), driven by advances in neural representation learning, the ability to serve queries accompanied by a set of c…
CARAMEL: A Succinct Read-Only Lookup Table via Compressed Static Functions
Benjamin Coleman, David Torres Ramos, Vihan Lakshman +2
Lookup tables are a fundamental structure in many data processing and systems applications. Examples include tokenized text in NLP, quantized embedding collections in recommendatio…
BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Search and Recommendation Models on Commodity CPU Hardware
Nicholas Meisburger, Vihan Lakshman, Benito Geordie +9
Efficient large-scale neural network training and inference on commodity CPU hardware is of immense practical significance in democratizing deep learning (DL) capabilities. Present…
DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries
Joshua Engels, Benjamin Coleman, Vihan Lakshman +1
We study the problem of with . This task is analogous to traditional near-neighbor search, with the exception that both th…
Low-Precision Quantization for Efficient Nearest Neighbor Search
Anthony Ko, Iman Keivanloo, Vihan Lakshman +1
Fast k-Nearest Neighbor search over real-valued vector spaces (KNN) is an important algorithmic task for information retrieval and recommendation systems. We present a method for u…
Embracing Structure in Data for Billion-Scale Semantic Product Search
Vihan Lakshman, Choon Hui Teo, Xiaowen Chu +4
We present principled approaches to train and deploy dyadic neural embedding models at the billion scale, focusing our investigation on the application of semantic product search.…
A Fast Randomized Algorithm for Massive Text Normalization
Nan Jiang, Chen Luo, Vihan Lakshman +2
Many popular machine learning techniques in natural language processing and data mining rely heavily on high-quality text sources. However real-world text datasets contain a signif…
Semantic Product Search
Priyanka Nigam, Yiwei Song, Vijai Mohan +7
We study the problem of semantic matching in product search, that is, given a customer query, retrieve all semantically related products from the catalog. Pure lexical matching via…