6 papers
It's all In the (Exponential) Family: An Equivalence between Maximum Likelihood Estimation and Control Variates for Sketching Algorithms
Keegan Kang, Kerong Wang, Ding Zhang +3
Maximum likelihood estimators (MLE) and control variate estimators (CVE) have been used in conjunction with known information across sketching algorithms and applications in machin…
Improved Analysis of Khatri-Rao Random Projections and Applications
Arvind K. Saibaba, Bhisham Dev Verma, Grey Ballard
Randomization has emerged as a powerful set of tools for large-scale matrix and tensor decompositions. Randomized algorithms involve computing sketches with random matrices. A prev…
Adaptive Randomized Tensor Train Rounding using Khatri-Rao Products
Hussam Al Daas, Grey Ballard, Laura Grigori +3
Approximating a tensor in the tensor train (TT) format has many important applications in scientific computing. Rounding a TT tensor involves further compressing a tensor that is a…
Stochastic Trace and Diagonal Estimator for Tensors
Bhisham Dev Verma, Rameshwar Pratap, Keegan Kang
We consider the problem of estimating the trace and diagonal entries of an N-order tensor (where ) under the framework where the tensor can only be accessed through tenso…
Faster and Space Efficient Indexing for Locality Sensitive Hashing
Bhisham Dev Verma, Rameshwar Pratap
This work suggests faster and space-efficient index construction algorithms for LSH for Euclidean distance (\textit{a.k.a.}~\ELSH) and cosine similarity (\textit{a.k.a.}~\SRP). The…
Improving LSH via Tensorized Random Projection
Bhisham Dev Verma, Rameshwar Pratap
Locality sensitive hashing (LSH) is a fundamental algorithmic toolkit used by data scientists for approximate nearest neighbour search problems that have been used extensively in m…