Search Results for author:
Found 7 papers, 1 papers with code
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With Supplement
The mathematical formalization of a neurological mechanism in the olfactory circuit of a fruit-fly as a locality sensitive hash (Flyhash) and bloom filter (FBF) has been recently proposed and "reprogrammed" for various machine learning tasks such as similarity search, outlier detection and text embeddings.
Neural Neighborhood Encoding for Classification
Inspired by the fruit-fly olfactory circuit, the Fly Bloom Filter [Dasgupta et al., 2018] is able to efficiently summarize the data with a single pass and has been used for novelty detection.
K-means clustering using random matrix sparsification
In this paper we show that we can randomly sparsify the original data matrix resulting in a sparse data matrix which can significantly speed up the above mentioned matrix vector multiplication step without significantly affecting cluster quality.
Improved nearest neighbor search using auxiliary information and priority functions
Nearest neighbor search using random projection trees has recently been shown to achieve superior performance, in terms of better accuracy while retrieving less number of data points, compared to locality sensitive hashing based methods.
Near-optimal Differentially Private Principal Components
In this paper we investigate the theory and empirical performance of differentially private approximations to PCA and propose a new method which explicitly optimizes the utility of the output.
Near-Optimal Algorithms for Differentially-Private Principal Components
In this paper we investigate the theory and empirical performance of differentially private approximations to PCA and propose a new method which explicitly optimizes the utility of the output.
Semi-supervised Learning using Sparse Eigenfunction Bases
We present a new framework for semi-supervised learning with sparse eigenfunction bases of kernel matrices.
