Thomas D. Ahle

TL;DR: This work is a general method for applying sketching solutions developed in numerical linear algebra over the past decade to a tensoring of data points without forming the tensoring explicitly, and leads to the first oblivious sketch for the polynomial kernel with a target dimension that is only polynomially dependent on the degree of the kernel function.

TL;DR: In this paper, a systematic study of inner product similarity join (IPS join) has been conducted, showing new lower and upper bounds on the hardness of IPS join, including new asymmetric embeddings.

TL;DR: Oblivious sketching has emerged as a powerful approach to speeding up numerical linear algebra over the past decade, but our understanding of oblivious sketching solutions for kernel matrices has remained quite limited, suffering from the aforementioned exponential dependence on input parameters.

TL;DR: In this article, it was shown that approximate similarity search can be solved in high dimensions with performance matching state-of-the-art LSH, but with a guarantee of no false negatives.