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Found 9 papers, 1 papers with code
Nonlinear Kalman Filtering with Reparametrization Gradients
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.
An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions
Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions.
Bid Shading by Win-Rate Estimation and Surplus Maximization
We found that bid shading, in general, can deliver significant value to advertisers, reducing price per impression to about 55% of the unshaded cost.
Risk Bounds for Low Cost Bipartite Ranking
Bipartite ranking is an important supervised learning problem; however, unlike regression or classification, it has a quadratic dependence on the number of samples.
MBA: Mini-Batch AUC Optimization
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.
Online Forecasting Matrix Factorization
In this paper the problem of forecasting high dimensional time series is considered.
Nonlinear Kalman Filtering with Divergence Minimization
We consider the nonlinear Kalman filtering problem using Kullback-Leibler (KL) and $\alpha$-divergence measures as optimization criteria.
Stochastic Annealing for Variational Inference
We empirically evaluate a stochastic annealing strategy for Bayesian posterior optimization with variational inference.
A Collaborative Kalman Filter for Time-Evolving Dyadic Processes
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.
