17,959 research outputs found
Uplift Modeling with Multiple Treatments and General Response Types
Randomized experiments have been used to assist decision-making in many
areas. They help people select the optimal treatment for the test population
with certain statistical guarantee. However, subjects can show significant
heterogeneity in response to treatments. The problem of customizing treatment
assignment based on subject characteristics is known as uplift modeling,
differential response analysis, or personalized treatment learning in
literature. A key feature for uplift modeling is that the data is unlabeled. It
is impossible to know whether the chosen treatment is optimal for an individual
subject because response under alternative treatments is unobserved. This
presents a challenge to both the training and the evaluation of uplift models.
In this paper we describe how to obtain an unbiased estimate of the key
performance metric of an uplift model, the expected response. We present a new
uplift algorithm which creates a forest of randomized trees. The trees are
built with a splitting criterion designed to directly optimize their uplift
performance based on the proposed evaluation method. Both the evaluation method
and the algorithm apply to arbitrary number of treatments and general response
types. Experimental results on synthetic data and industry-provided data show
that our algorithm leads to significant performance improvement over other
applicable methods
A Practically Competitive and Provably Consistent Algorithm for Uplift Modeling
Randomized experiments have been critical tools of decision making for
decades. However, subjects can show significant heterogeneity in response to
treatments in many important applications. Therefore it is not enough to simply
know which treatment is optimal for the entire population. What we need is a
model that correctly customize treatment assignment base on subject
characteristics. The problem of constructing such models from randomized
experiments data is known as Uplift Modeling in the literature. Many algorithms
have been proposed for uplift modeling and some have generated promising
results on various data sets. Yet little is known about the theoretical
properties of these algorithms. In this paper, we propose a new tree-based
ensemble algorithm for uplift modeling. Experiments show that our algorithm can
achieve competitive results on both synthetic and industry-provided data. In
addition, by properly tuning the "node size" parameter, our algorithm is proved
to be consistent under mild regularity conditions. This is the first consistent
algorithm for uplift modeling that we are aware of.Comment: Accepted by 2017 IEEE International Conference on Data Minin
On q-deformed infinite-dimensional n-algebra
The -deformation of the infinite-dimensional -algebra is investigated.
Based on the structure of the -deformed Virasoro-Witt algebra, we derive a
nontrivial -deformed Virasoro-Witt -algebra which is nothing but a
sh--Lie algebra. Furthermore in terms of the pseud-differential operators on
the quantum plane, we construct the (co)sine -algebra and the -deformed
-algebra. We prove that they are the sh--Lie algebras for
the case of even . An explicit physical realization of the (co)sine
-algebra is given.Comment: 22 page
Integrated Face Analytics Networks through Cross-Dataset Hybrid Training
Face analytics benefits many multimedia applications. It consists of a number
of tasks, such as facial emotion recognition and face parsing, and most
existing approaches generally treat these tasks independently, which limits
their deployment in real scenarios. In this paper we propose an integrated Face
Analytics Network (iFAN), which is able to perform multiple tasks jointly for
face analytics with a novel carefully designed network architecture to fully
facilitate the informative interaction among different tasks. The proposed
integrated network explicitly models the interactions between tasks so that the
correlations between tasks can be fully exploited for performance boost. In
addition, to solve the bottleneck of the absence of datasets with comprehensive
training data for various tasks, we propose a novel cross-dataset hybrid
training strategy. It allows "plug-in and play" of multiple datasets annotated
for different tasks without the requirement of a fully labeled common dataset
for all the tasks. We experimentally show that the proposed iFAN achieves
state-of-the-art performance on multiple face analytics tasks using a single
integrated model. Specifically, iFAN achieves an overall F-score of 91.15% on
the Helen dataset for face parsing, a normalized mean error of 5.81% on the
MTFL dataset for facial landmark localization and an accuracy of 45.73% on the
BNU dataset for emotion recognition with a single model.Comment: 10 page
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