24 citations · 85 across the 22 of their papers we have counts for
22 papers
GreenFlow: A Computation Allocation Framework for Building Environmentally Sound Recommendation System
Xingyu Lu, Zhining Liu, Yanchu Guan +6
Given the enormous number of users and items, industrial cascade recommendation systems (RS) are continuously expanded in size and complexity to deliver relevant items, such as new…
Multiple Instance Learning for Uplift Modeling
Yao Zhao, Haipeng Zhang, Shiwei Lyu +3
Uplift modeling is widely used in performance marketing to estimate effects of promotion campaigns (e.g., increase of customer retention rate). Since it is impossible to observe ou…
An Unified Search and Recommendation Foundation Model for Cold-Start Scenario
Yuqi Gong, Xichen Ding, Yehui Su +3
In modern commercial search engines and recommendation systems, data from multiple domains is available to jointly train the multi-domain model. Traditional methods train multi-dom…
Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning
Tianchi Cai, Jiyan Jiang, Wenpeng Zhang +7
We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We first discuss the long-term effect of optimizing marketing b…
An Ensemble Score Filter for Tracking High-Dimensional Nonlinear Dynamical Systems
Feng Bao, Zezhong Zhang, Guannan Zhang
We propose an ensemble score filter (EnSF) for solving high-dimensional nonlinear filtering problems with superior accuracy. A major drawback of existing filtering methods, e.g., p…
AntMC: A Large Scale Dataset For Multi-Scenario Multi-Modal CTR Prediction
Zhaoxin Huan, Ke Ding, Ang Li +10
Click-through rate (CTR) prediction is a crucial issue in recommendation systems. There has been an emergence of various public CTR datasets. However, existing datasets primarily s…
Model-free Reinforcement Learning with Stochastic Reward Stabilization for Recommender Systems
Tianchi Cai, Shenliao Bao, Jiyan Jiang +5
Model-free RL-based recommender systems have recently received increasing research attention due to their capability to handle partial feedback and long-term rewards. However, most…
Harnessing the Power of David against Goliath: Exploring Instruction Data Generation without Using Closed-Source Models
Yue Wang, Xinrui Wang, Juntao Li +5
Instruction tuning is instrumental in enabling Large Language Models~(LLMs) to follow user instructions to complete various open-domain tasks. The success of instruction tuning dep…
Who Would be Interested in Services? An Entity Graph Learning System for User Targeting
Dan Yang, Binbin Hu, Xiaoyan Yang +4
With the growing popularity of various mobile devices, user targeting has received a growing amount of attention, which aims at effectively and efficiently locating target users th…
GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning
Weifan Wang, Binbin Hu, Zhicheng Peng +5
Recently, the growth of service platforms brings great convenience to both users and merchants, where the service search engine plays a vital role in improving the user experience…
Edge-cloud Collaborative Learning with Federated and Centralized Features
Zexi Li, Qunwei Li, Yi Zhou +3
Federated learning (FL) is a popular way of edge computing that doesn't compromise users' privacy. Current FL paradigms assume that data only resides on the edge, while cloud serve…
Compressive-sensing-assisted mixed integer optimization for dynamical system discovery with highly noisy data
Zhongshun Shi, Hang Ma, Hoang Tran +1
The identification of governing equations for dynamical systems is everlasting challenges for the fundamental research in science and engineering. Machine learning has exhibited gr…
A Manifold-based Airfoil Geometric-feature Extraction and Discrepant Data Fusion Learning Method
Yu Xiang, Guangbo Zhang, Liwei Hu +2
Geometrical shape of airfoils, together with the corresponding flight conditions, are crucial factors for aerodynamic performances prediction. The obtained airfoils geometrical fea…
A Multi-Characteristic Learning Method with Micro-Doppler Signatures for Pedestrian Identification
Yu Xiang, Yu Huang, Haodong Xu +2
The identification of pedestrians using radar micro-Doppler signatures has become a hot topic in recent years. In this paper, we propose a multi-characteristic learning (MCL) model…
Model Calibration of the Liquid Mercury Spallation Target using Evolutionary Neural Networks and Sparse Polynomial Expansions
Majdi I. Radaideh, Hoang Tran, Lianshan Lin +6
The mercury constitutive model predicting the strain and stress in the target vessel plays a central role in improving the lifetime prediction and future target designs of the merc…
Level set learning with pseudo-reversible neural networks for nonlinear dimension reduction in function approximation
Yuankai Teng, Zhu Wang, Lili Ju +2
Due to the curse of dimensionality and the limitation on training data, approximating high-dimensional functions is a very challenging task even for powerful deep neural networks.…
PI3NN: Out-of-distribution-aware prediction intervals from three neural networks
Siyan Liu, Pei Zhang, Dan Lu +1
We propose a novel prediction interval (PI) method for uncertainty quantification, which addresses three major issues with the state-of-the-art PI methods. First, existing PI metho…
Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials
Sirui Bi, Jiaxin Zhang, Guannan Zhang
Topology optimization (TO) is a popular and powerful computational approach for designing novel structures, materials, and devices. Two computational challenges have limited the ap…
AdaDGS: An adaptive black-box optimization method with a nonlocal directional Gaussian smoothing gradient
Hoang Tran, Guannan Zhang
The local gradient points to the direction of the steepest slope in an infinitesimal neighborhood. An optimizer guided by the local gradient is often trapped in local optima when t…
Accelerating Reinforcement Learning with a Directional-Gaussian-Smoothing Evolution Strategy
Jiaxing Zhang, Hoang Tran, Guannan Zhang
Evolution strategy (ES) has been shown great promise in many challenging reinforcement learning (RL) tasks, rivaling other state-of-the-art deep RL methods. Yet, there are two limi…
A Novel Evolution Strategy with Directional Gaussian Smoothing for Blackbox Optimization
Jiaxin Zhang, Hoang Tran, Dan Lu +1
We propose an improved evolution strategy (ES) using a novel nonlocal gradient operator for high-dimensional black-box optimization. Standard ES methods with -dimensional Gaussi…
Learning nonlinear level sets for dimensionality reduction in function approximation
Guannan Zhang, Jiaxin Zhang, Jacob Hinkle
We developed a Nonlinear Level-set Learning (NLL) method for dimensionality reduction in high-dimensional function approximation with small data. This work is motivated by a variet…