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09/16/2023
An Unified Search and Recommendation Foundation Model for Cold-Start Scenario
In modern commercial search engines and recommendation systems, data fro...
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09/06/2023
Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning
We study the budget allocation problem in online marketing campaigns tha...
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09/02/2023
An Ensemble Score Filter for Tracking High-Dimensional Nonlinear Dynamical Systems
We propose an ensemble score filter (EnSF) for solving high-dimensional ...
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08/31/2023
AntM^2C: A Large Scale Dataset For Multi-Scenario Multi-Modal CTR Prediction
Click-through rate (CTR) prediction is a crucial issue in recommendation...
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08/25/2023
Model-free Reinforcement Learning with Stochastic Reward Stabilization for Recommender Systems
Model-free RL-based recommender systems have recently received increasin...
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08/24/2023
Harnessing the Power of David against Goliath: Exploring Instruction Data Generation without Using Closed-Source Models
Instruction tuning is instrumental in enabling Large Language Models (LL...
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06/08/2023
A pseudo-reversible normalizing flow for stochastic dynamical systems with various initial distributions
We present a pseudo-reversible normalizing flow method for efficiently g...
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05/30/2023
Who Would be Interested in Services? An Entity Graph Learning System for User Targeting
With the growing popularity of various mobile devices, user targeting ha...
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04/25/2023
GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning
Recently, the growth of service platforms brings great convenience to bo...
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02/13/2023
Convergence analysis for a nonlocal gradient descent method via directional Gaussian smoothing
We analyze the convergence of a nonlocal gradient descent method for min...
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01/27/2023
TransNet: Transferable Neural Networks for Partial Differential Equations
Transfer learning for partial differential equations (PDEs) is to develo...
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05/01/2022
A probabilistic scheme for semilinear nonlocal diffusion equations with volume constraints
This work presents a probabilistic scheme for solving semilinear nonloca...
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02/18/2022
Model Calibration of the Liquid Mercury Spallation Target using Evolutionary Neural Networks and Sparse Polynomial Expansions
The mercury constitutive model predicting the strain and stress in the t...
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12/02/2021
Level set learning with pseudo-reversible neural networks for nonlinear dimension reduction in function approximation
Due to the curse of dimensionality and the limitation on training data, ...
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08/05/2021
PI3NN: Prediction intervals from three independently trained neural networks
We propose a novel prediction interval method to learn prediction mean v...
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04/29/2021
A Feynman-Kac based numerical method for the exit time probability of a class of transport problems
The exit time probability, which gives the likelihood that an initial co...
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03/14/2021
A Hybrid Gradient Method to Designing Bayesian Experiments for Implicit Models
Bayesian experimental design (BED) aims at designing an experiment to ma...
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03/14/2021
A Scalable Gradient-Free Method for Bayesian Experimental Design with Implicit Models
Bayesian experimental design (BED) is to answer the question that how to...
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11/28/2020
Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials
Topology optimization (TO) is a popular and powerful computational appro...
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11/03/2020
AdaDGS: An adaptive black-box optimization method with a nonlocal directional Gaussian smoothing gradient
The local gradient points to the direction of the steepest slope in an i...
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02/21/2020
Accelerating Reinforcement Learning with a Directional-Gaussian-Smoothing Evolution Strategy
Evolution strategy (ES) has been shown great promise in many challenging...
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02/07/2020
A Scalable Evolution Strategy with Directional Gaussian Smoothing for Blackbox Optimization
We developed a new scalable evolution strategy with directional Gaussian...
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08/10/2019