Inparticular,ifanalgorithm uses O(n/logck) samples for some constantc > 0 and polynomial space, then the query time of the data structure must be at leastk1 O(1)/loglogk, i.e., close to

In many search applications related to passage retrieval, text entailment, and sub-graph search, the query and each'document' is a set of elements, with a document

However, their data structure requires a significantly increased super-linear storage space, as well as super-linear preprocessing time. These limitations inhibit the practical applicability of their approach on large datasets.

Graph search is one of the most successful algorithmic trends in near neighbor search. Severalofthemostpopular andempirically successful algorithms are,at their core, a greedy walk along a pruned near neighbor graph.

We show that LSH based algorithms can be made fair, without a significant loss in efficiency. Specifically, we show an algorithm that reports a point in the rneighborhood of a query q with almost uniform probability.

Such complexities are prohibitive for scaling to large datasets.

Neural architecture search (NAS) is a popular area of machine learning, which aims to automate the process of developing neural architectures for a given dataset. Since 2017, a wide variety of NAS techniques have been proposed [78, 45, 32, 49].

Clustering is a fundamental problem in unsupervised learning with several practical applications. In clustering, one is interested in partitioning elements into different groups (i.e.