6 citations · 6 across the 10 of their papers we have counts for
10 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…
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…
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…
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…
Improving \textit{Tug-of-War} sketch using Control-Variates method
Rameshwar Pratap, Bhisham Dev Verma, Raghav Kulkarni
Computing space-efficient summary, or \textit{a.k.a. sketches}, of large data, is a central problem in the streaming algorithm. Such sketches are used to answer \textit{post-hoc} q…
A revised comparison between FF five-factor model and three-factor model,based on China's A-share market
Zhijing Zhang, Yue Yu, Qinghua Ma +1
In allusion to some contradicting results in existing research, this paper selects China's latest stock data from 2005 to 2020 for empirical analysis. By choosing this periods' dat…
Efficient Binary Embedding of Categorical Data using BinSketch
Bhisham Dev Verma, Rameshwar Pratap, Debajyoti Bera
In this work, we present a dimensionality reduction algorithm, aka. sketching, for categorical datasets. Our proposed sketching algorithm Cabin constructs low-dimensional binary sk…
QUINT: Node embedding using network hashing
Debajyoti Bera, Rameshwar Pratap, Bhisham Dev Verma +2
Representation learning using network embedding has received tremendous attention due to its efficacy to solve downstream tasks. Popular embedding methods (such as deepwalk, node2v…