8,501 research outputs found
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
Generalized gravity model for human migration
The gravity model (GM) analogous to Newton's law of universal gravitation has
successfully described the flow between different spatial regions, such as
human migration, traffic flows, international economic trades, etc. This simple
but powerful approach relies only on the 'mass' factor represented by the scale
of the regions and the 'geometrical' factor represented by the geographical
distance. However, when the population has a subpopulation structure
distinguished by different attributes, the estimation of the flow solely from
the coarse-grained geographical factors in the GM causes the loss of
differential geographical information for each attribute. To exploit the full
information contained in the geographical information of subpopulation
structure, we generalize the GM for population flow by explicitly harnessing
the subpopulation properties characterized by both attributes and geography. As
a concrete example, we examine the marriage patterns between the bride and the
groom clans of Korea in the past. By exploiting more refined geographical and
clan information, our generalized GM properly describes the real data, a part
of which could not be explained by the conventional GM. Therefore, we would
like to emphasize the necessity of using our generalized version of the GM,
when the information on such nongeographical subpopulation structures is
available.Comment: 14 pages, 6 figures, 2 table
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