Feature Cross Search via Submodular Optimization

no code implementations • 5 Jul 2021 • Lin Chen, Hossein Esfandiari, Gang Fu, Vahab S. Mirrokni, Qian Yu

First, we show that it is not possible to provide an $n^{1/\log\log n}$-approximation algorithm for this problem unless the exponential time hypothesis fails.

Feature Engineering

Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond

no code implementations • NeurIPS 2019 • Lin Chen, Hossein Esfandiari, Thomas Fu, Vahab S. Mirrokni

In this paper, we aim to develop LSH schemes for distance functions that measure the distance between two probability distributions, particularly for f-divergences as well as a generalization to capture mutual information loss.

Model Compression

Categorical Feature Compression via Submodular Optimization

no code implementations • 30 Apr 2019 • Mohammadhossein Bateni, Lin Chen, Hossein Esfandiari, Thomas Fu, Vahab S. Mirrokni, Afshin Rostamizadeh

To achieve this, we introduce a novel re-parametrization of the mutual information objective, which we prove is submodular, and design a data structure to query the submodular function in amortized $O(\log n )$ time (where $n$ is the input vocabulary size).

Feature Compression