8,631 research outputs found
A Simple Prediction Model for PCC Voltage Variation Due to Active Power Fluctuation for a Grid Connected Wind Turbine
RTB Formulation Using Point Process
We propose a general stochastic framework for modelling repeated auctions in
the Real Time Bidding (RTB) ecosystem using point processes. The flexibility of
the framework allows a variety of auction scenarios including configuration of
information provided to player, determination of auction winner and
quantification of utility gained from each auctions. We propose theoretical
results on how this formulation of process can be approximated to a Poisson
point process, which enables the analyzer to take advantage of well-established
properties. Under this framework, we specify the player's optimal strategy
under various scenarios. We also emphasize that it is critical to consider the
joint distribution of utility and market condition instead of estimating the
marginal distributions independently
Addressing Distribution Shift in RTB Markets via Exponential Tilting
Distribution shift in machine learning models can be a primary cause of
performance degradation. This paper delves into the characteristics of these
shifts, primarily motivated by Real-Time Bidding (RTB) market models. We
emphasize the challenges posed by class imbalance and sample selection bias,
both potent instigators of distribution shifts. This paper introduces the
Exponential Tilt Reweighting Alignment (ExTRA) algorithm, as proposed by Marty
et al. (2023), to address distribution shifts in data. The ExTRA method is
designed to determine the importance weights on the source data, aiming to
minimize the KL divergence between the weighted source and target datasets. A
notable advantage of this method is its ability to operate using labeled source
data and unlabeled target data. Through simulated real-world data, we
investigate the nature of distribution shift and evaluate the applicacy of the
proposed model
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