50 citations · 97 across the 10 of their papers we have counts for
10 papers
Double Distributionally Robust Bid Shading for First Price Auctions
Yanlin Qu, Ravi Kant, Yan Chen +4
Bid shading has become a standard practice in the digital advertising industry, in which most auctions for advertising (ad) opportunities are now of first price type. Given an ad o…
Nonlinear Kalman Filtering with Reparametrization Gradients
San Gultekin, Brendan Kitts, Aaron Flores +1
We introduce a novel nonlinear Kalman filter that utilizes reparametrization gradients. The widely used parametric approximation is based on a jointly Gaussian assumption of the st…
An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions
Tian Zhou, Hao He, Shengjun Pan +9
Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions. Due to the fundamental difference bet…
Bid Shading by Win-Rate Estimation and Surplus Maximization
Shengjun Pan, Brendan Kitts, Tian Zhou +8
This paper describes a new win-rate based bid shading algorithm (WR) that does not rely on the minimum-bid-to-win feedback from a Sell-Side Platform (SSP). The method uses a modifi…
Risk Bounds for Low Cost Bipartite Ranking
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. To circumvent t…
MBA: Mini-Batch AUC Optimization
San Gultekin, Avishek Saha, Adwait Ratnaparkhi +1
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 o…
Online Forecasting Matrix Factorization
San Gultekin, John Paisley
In this paper the problem of forecasting high dimensional time series is considered. Such time series can be modeled as matrices where each column denotes a measurement. In additio…
Nonlinear Kalman Filtering with Divergence Minimization
San Gultekin, John Paisley
We consider the nonlinear Kalman filtering problem using Kullback-Leibler (KL) and -divergence measures as optimization criteria. Unlike linear Kalman filters, nonlinear Kalman…
Stochastic Annealing for Variational Inference
San Gultekin, Aonan Zhang, John Paisley
We empirically evaluate a stochastic annealing strategy for Bayesian posterior optimization with variational inference. Variational inference is a deterministic approach to approxi…
A Collaborative Kalman Filter for Time-Evolving Dyadic Processes
San Gultekin, John Paisley
We present the collaborative Kalman filter (CKF), a dynamic model for collaborative filtering and related factorization models. Using the matrix factorization approach to collabora…