3,338 research outputs found
ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Models
We study the domain adaptation problem with label shift in this work. Under
the label shift context, the marginal distribution of the label varies across
the training and testing datasets, while the conditional distribution of
features given the label is the same. Traditional label shift adaptation
methods either suffer from large estimation errors or require cumbersome
post-prediction calibrations. To address these issues, we first propose a
moment-matching framework for adapting the label shift based on the geometry of
the influence function. Under such a framework, we propose a novel method named
\underline{E}fficient \underline{L}abel \underline{S}hift
\underline{A}daptation (ELSA), in which the adaptation weights can be estimated
by solving linear systems. Theoretically, the ELSA estimator is
-consistent ( is the sample size of the source data) and
asymptotically normal. Empirically, we show that ELSA can achieve
state-of-the-art estimation performances without post-prediction calibrations,
thus, gaining computational efficiency
UFIN: Universal Feature Interaction Network for Multi-Domain Click-Through Rate Prediction
Click-Through Rate (CTR) prediction, which aims to estimate the probability
of a user clicking on an item, is a key task in online advertising. Numerous
existing CTR models concentrate on modeling the feature interactions within a
solitary domain, thereby rendering them inadequate for fulfilling the
requisites of multi-domain recommendations in real industrial scenarios. Some
recent approaches propose intricate architectures to enhance knowledge sharing
and augment model training across multiple domains. However, these approaches
encounter difficulties when being transferred to new recommendation domains,
owing to their reliance on the modeling of ID features (e.g., item id). To
address the above issue, we propose the Universal Feature Interaction Network
(UFIN) approach for CTR prediction. UFIN exploits textual data to learn
universal feature interactions that can be effectively transferred across
diverse domains. For learning universal feature representations, we regard the
text and feature as two different modalities and propose an encoder-decoder
network founded on a Large Language Model (LLM) to enforce the transfer of data
from the text modality to the feature modality. Building upon the above
foundation, we further develop a mixtureof-experts (MoE) enhanced adaptive
feature interaction model to learn transferable collaborative patterns across
multiple domains. Furthermore, we propose a multi-domain knowledge distillation
framework to enhance feature interaction learning. Based on the above methods,
UFIN can effectively bridge the semantic gap to learn common knowledge across
various domains, surpassing the constraints of ID-based models. Extensive
experiments conducted on eight datasets show the effectiveness of UFIN, in both
multidomain and cross-platform settings. Our code is available at
https://github.com/RUCAIBox/UFIN
STEADY-STATE ANALYSIS OF THE GI/M/1 QUEUE WITH MULTIPLE VACATIONS AND SET-UP TIME
In this paper, we consider a GI/M/1 queueing model with multiple vacations and set-up time. We derive the distribution and the generating function and the stochastic decomposition of the steady-state queue length, meanwhile, we get the waiting time distributions. Key words: multiple vacations, set-up time, stochastic decompositio
A Second-Order Sliding Mode Controller Design for Spacecraft Tracking Control
For spacecraft attitude tracking system, there exists the chattering phenomenon. In this paper, the spacecraft motion is decomposed into three-channel subsystems, and a second-order sliding mode control is proposed. This method has been proved to have good convergence and robustness. Combined with the proposed sliding surface, the three-channel controllers are designed. The control performance is confirmed by the simulation results, the approaching process is improved effectively, and a smooth transition is achieved without overshoot and buffeting
Supervised Hamiltonian learning via efficient and robust quantum descent
Given the recent developments in quantum techniques, modeling the physical
Hamiltonian of a target quantum many-body system is becoming an increasingly
practical and vital research direction. Here, we propose an efficient quantum
strategy that mingles maximum-likelihood-estimate state and supervised machine
learning. Given the measurement outcomes, we optimize the target model
Hamiltonian and density operator via a series of quantum descent, which we
prove is negative semi-definite with respect to the negative-log-likelihood
function. In addition to such optimization efficiency, supervised Hamiltonian
learning respects the locality of a given quantum system, therefore, extends
readily to larger systems. Compared with previous approaches, it also exhibits
better accuracy and overall stability toward noises, fluctuations, and
temperature ranges, which we demonstrate with various examples.Comment: 12 pages, 8figure
BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading
Diabetic retinopathy (DR) is a common retinal disease that leads to
blindness. For diagnosis purposes, DR image grading aims to provide automatic
DR grade classification, which is not addressed in conventional research
methods of binary DR image classification. Small objects in the eye images,
like lesions and microaneurysms, are essential to DR grading in medical
imaging, but they could easily be influenced by other objects. To address these
challenges, we propose a new deep learning architecture, called BiRA-Net, which
combines the attention model for feature extraction and bilinear model for
fine-grained classification. Furthermore, in considering the distance between
different grades of different DR categories, we propose a new loss function,
called grading loss, which leads to improved training convergence of the
proposed approach. Experimental results are provided to demonstrate the
superior performance of the proposed approach.Comment: Accepted at ICIP 201
Constraining neutrino mass in dynamical dark energy cosmologies with the logarithm parametrization and the oscillating parametrization
We constrain two dynamical dark energy models that are parametrized by the
logarithm form of
and the oscillating form of
. Comparing with
the Chevallier-Polarski-Linder (CPL) model, the two parametrizations for dark
energy can explore the whole evolution history of the universe properly. Using
the current mainstream observational data including the cosmic microwave
background data and the baryon acoustic oscillation data as well as the type Ia
supernovae data, we perform the statistic analysis to global fit these
models, finding that the logarithm parametrization and the oscillating
parametrization are almost as well as the CPL scenario in fitting these data.
We make a comparison for the impacts of the dynamical dark energy on the
cosmological constraints on the total mass of active neutrinos. We find that
the dark energy properties could significantly change the fitting results of
neutrino mass. Looser constraints on are obtained in the
logarithm and oscillating models than those derived in the CPL model.
Consideration of the possible mass ordering of neutrinos reveals that the most
stringent constraint on appears in the degenerate hierarchy
case
EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction
Learning effective high-order feature interactions is very crucial in the CTR
prediction task. However, it is very time-consuming to calculate high-order
feature interactions with massive features in online e-commerce platforms. Most
existing methods manually design a maximal order and further filter out the
useless interactions from them. Although they reduce the high computational
costs caused by the exponential growth of high-order feature combinations, they
still suffer from the degradation of model capability due to the suboptimal
learning of the restricted feature orders. The solution to maintain the model
capability and meanwhile keep it efficient is a technical challenge, which has
not been adequately addressed. To address this issue, we propose an adaptive
feature interaction learning model, named as EulerNet, in which the feature
interactions are learned in a complex vector space by conducting space mapping
according to Euler's formula. EulerNet converts the exponential powers of
feature interactions into simple linear combinations of the modulus and phase
of the complex features, making it possible to adaptively learn the high-order
feature interactions in an efficient way. Furthermore, EulerNet incorporates
the implicit and explicit feature interactions into a unified architecture,
which achieves the mutual enhancement and largely boosts the model
capabilities. Such a network can be fully learned from data, with no need of
pre-designed form or order for feature interactions. Extensive experiments
conducted on three public datasets have demonstrated the effectiveness and
efficiency of our approach. Our code is available at:
https://github.com/RUCAIBox/EulerNet.Comment: 10 pages, 7 figures, accepted for publication in SIGIR'2
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