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Found 17 papers, 1 papers with code
Dr3: Ask Large Language Models Not to Give Off-Topic Answers in Open Domain Multi-Hop Question Answering
To alleviate this issue, we propose the Discriminate->Re-Compose->Re- Solve->Re-Decompose (Dr3) mechanism.
Robust Representation Learning for Unified Online Top-K Recommendation
The robust representation learning employs domain adversarial learning and multi-view wasserstein distribution learning to learn robust representations.
DRL4Route: A Deep Reinforcement Learning Framework for Pick-up and Delivery Route Prediction
Deep neural networks based on supervised learning have emerged as the dominant model for the task because of their powerful ability to capture workers' behavior patterns from massive historical data.
LaDe: The First Comprehensive Last-mile Delivery Dataset from Industry
In this paper, we introduce \texttt{LaDe}, the first publicly available last-mile delivery dataset with millions of packages from the industry.
G2PTL: A Pre-trained Model for Delivery Address and its Applications in Logistics System
How to effectively encode the delivery address is a core task to boost the performance of downstream tasks in the logistics system.
An Online Algorithm for Chance Constrained Resource Allocation
This paper studies the online stochastic resource allocation problem (RAP) with chance constraints.
AdaptDHM: Adaptive Distribution Hierarchical Model for Multi-Domain CTR Prediction
Large-scale commercial platforms usually involve numerous business domains for diverse business strategies and expect their recommendation systems to provide click-through rate (CTR) predictions for multiple domains simultaneously.
Communication-Efficient Decentralized Online Continuous DR-Submodular Maximization
Maximizing a monotone submodular function is a fundamental task in machine learning, economics, and statistics.
Online Learning for Non-monotone Submodular Maximization: From Full Information to Bandit Feedback
In this paper, we revisit the online non-monotone continuous DR-submodular maximization problem over a down-closed convex set, which finds wide real-world applications in the domain of machine learning, economics, and operations research.
An end-to-end predict-then-optimize clustering method for intelligent assignment problems in express systems
To solve these problems, we put forward an intelligent end-to-end predict-then-optimize clustering method to simultaneously predict the future pick-up requests of AOIs and assign AOIs to couriers by clustering.
Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function
In the online setting, for the first time we consider the adversarial delays for stochastic gradient feedback, under which we propose a boosting online gradient algorithm with the same non-oblivious function $F$.
Online Allocation Problem with Two-sided Resource Constraints
As a result, only limited violations of constraints or pessimistic competitive bounds could be guaranteed.
A Deep Reinforcement Learning Approach for Online Parcel Assignment
In this paper, we investigate the online parcel assignment (OPA) problem, in which each stochastically generated parcel needs to be assigned to a candidate route for delivery to minimize the total cost subject to certain business constraints.
Balanced Order Batching with Task-Oriented Graph Clustering
In this paper, rather than designing heuristics, we propose an end-to-end learning and optimization framework named Balanced Task-orientated Graph Clustering Network (BTOGCN) to solve the BOBP by reducing it to balanced graph clustering optimization problem.
Can Sophisticated Dispatching Strategy Acquired by Reinforcement Learning? - A Case Study in Dynamic Courier Dispatching System
In this paper, we study a courier dispatching problem (CDP) raised from an online pickup-service platform of Alibaba.
A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem
A 3D flexible bin packing problem (3D-FBPP) arises from the process of warehouse packing in e-commerce.
Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method
The objective is to find a way to place these items that can minimize the surface area of the bin.
