arXiv subjects

Mehul Smriti Raje

Publications and source records attributed to Mehul Smriti Raje.

2 recordsLinked to original sources

arXiv2026

Sum Estimation via Vector Similarity Search

Semantic embeddings to represent objects such as image, text and audio are widely used in machine learning and have spurred the development of vector similarity search methods for retrieving semantically related objects. In this work, we study the sibling task of estimating a sum over all objects in a set, such as the kernel density estimate (KDE) and the normalizing constant for softmax distributions. While existing solutions provably reduce the sum estimation task to acquiring $\mathcal{O}(\sqrt{n})$ most similar vectors, where $n$ is the number of objects, we introduce a novel algorithm that only requires $\mathcal{O}(\log(n))$ most similar vectors. Our approach randomly assigns objects to levels with exponentially-decaying probabilities and constructs a vector similarity search data structure for each level. With the top-$k$ objects from each level, we propose an unbiased estimate of the sum and prove a high-probability relative error bound. We run experiments on OpenImages and Amazon Reviews with a vector similar search implementation to show that our method can achieve lower error using less computational time than existing reductions. We show results on applications in estimating densities, computing softmax denominators, and counting the number of vectors within a ball.

cs.DS↗

arXiv2023

DataPerf: Benchmarks for Data-Centric AI Development

Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithfulness of the underlying problems. Neglecting the fundamental importance of data has given rise to inaccuracy, bias, and fragility in real-world applications, and research is hindered by saturation across existing dataset benchmarks. In response, we present DataPerf, a community-led benchmark suite for evaluating ML datasets and data-centric algorithms. We aim to foster innovation in data-centric AI through competition, comparability, and reproducibility. We enable the ML community to iterate on datasets, instead of just architectures, and we provide an open, online platform with multiple rounds of challenges to support this iterative development. The first iteration of DataPerf contains five benchmarks covering a wide spectrum of data-centric techniques, tasks, and modalities in vision, speech, acquisition, debugging, and diffusion prompting, and we support hosting new contributed benchmarks from the community. The benchmarks, online evaluation platform, and baseline implementations are open source, and the MLCommons Association will maintain DataPerf to ensure long-term benefits to academia and industry.

cs.LG↗