1 code implementation • 8 Mar 2023 • San Gultekin, Brendan Kitts, Aaron Flores, John Paisley
The widely used parametric approximation is based on a jointly Gaussian assumption of the state-space model, which is in turn equivalent to minimizing an approximation to the Kullback-Leibler divergence.
no code implementations • 12 Jul 2021 • Tian Zhou, Hao He, Shengjun Pan, Niklas Karlsson, Bharatbhushan Shetty, Brendan Kitts, Djordje Gligorijevic, San Gultekin, Tingyu Mao, Junwei Pan, Jianlong Zhang, Aaron Flores
Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions.
no code implementations • 19 Sep 2020 • Shengjun Pan, Brendan Kitts, Tian Zhou, Hao He, Bharatbhushan Shetty, Aaron Flores, Djordje Gligorijevic, Junwei Pan, Tingyu Mao, San Gultekin, Jianlong Zhang
We found that bid shading, in general, can deliver significant value to advertisers, reducing price per impression to about 55% of the unshaded cost.
no code implementations • 2 Dec 2019 • San Gultekin, John Paisley
Bipartite ranking is an important supervised learning problem; however, unlike regression or classification, it has a quadratic dependence on the number of samples.
no code implementations • 29 May 2018 • San Gultekin, Avishek Saha, Adwait Ratnaparkhi, John Paisley
Area under the receiver operating characteristics curve (AUC) is an important metric for a wide range of signal processing and machine learning problems, and scalable methods for optimizing AUC have recently been proposed.
no code implementations • 23 Dec 2017 • San Gultekin, John Paisley
In this paper the problem of forecasting high dimensional time series is considered.
no code implementations • 1 May 2017 • San Gultekin, John Paisley
We consider the nonlinear Kalman filtering problem using Kullback-Leibler (KL) and $\alpha$-divergence measures as optimization criteria.
no code implementations • 25 May 2015 • San Gultekin, Aonan Zhang, John Paisley
We empirically evaluate a stochastic annealing strategy for Bayesian posterior optimization with variational inference.
no code implementations • 22 Jan 2015 • San Gultekin, John Paisley
Using the matrix factorization approach to collaborative filtering, the CKF accounts for time evolution by modeling each low-dimensional latent embedding as a multidimensional Brownian motion.