Search Results for author: Xiaocheng Tang

Found 12 papers, 3 papers with code

Rethinking Architecture Selection in Differentiable NAS

1 code implementation ICLR 2021 Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-Jui Hsieh

Differentiable Neural Architecture Search is one of the most popular Neural Architecture Search (NAS) methods for its search efficiency and simplicity, accomplished by jointly optimizing the model weight and architecture parameters in a weight-sharing supernet via gradient-based algorithms.

Neural Architecture Search

A Deep Value-network Based Approach for Multi-Driver Order Dispatching

no code implementations8 Jun 2021 Xiaocheng Tang, Zhiwei Qin, Fan Zhang, Zhaodong Wang, Zhe Xu, Yintai Ma, Hongtu Zhu, Jieping Ye

In this work, we propose a deep reinforcement learning based solution for order dispatching and we conduct large scale online A/B tests on DiDi's ride-dispatching platform to show that the proposed method achieves significant improvement on both total driver income and user experience related metrics.

Transfer Learning

Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark

no code implementations29 Mar 2021 Sharada Mohanty, Jyotish Poonganam, Adrien Gaidon, Andrey Kolobov, Blake Wulfe, Dipam Chakraborty, Gražvydas Šemetulskis, João Schapke, Jonas Kubilius, Jurgis Pašukonis, Linas Klimas, Matthew Hausknecht, Patrick MacAlpine, Quang Nhat Tran, Thomas Tumiel, Xiaocheng Tang, Xinwei Chen, Christopher Hesse, Jacob Hilton, William Hebgen Guss, Sahika Genc, John Schulman, Karl Cobbe

We present the design of a centralized benchmark for Reinforcement Learning which can help measure Sample Efficiency and Generalization in Reinforcement Learning by doing end to end evaluation of the training and rollout phases of thousands of user submitted code bases in a scalable way.

Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning

no code implementations8 Mar 2021 Yan Jiao, Xiaocheng Tang, Zhiwei Qin, Shuaiji Li, Fan Zhang, Hongtu Zhu, Jieping Ye

We present a new practical framework based on deep reinforcement learning and decision-time planning for real-world vehicle repositioning on ride-hailing (a type of mobility-on-demand, MoD) platforms.

Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem

no code implementations25 Nov 2019 John Holler, Risto Vuorio, Zhiwei Qin, Xiaocheng Tang, Yan Jiao, Tiancheng Jin, Satinder Singh, Chenxi Wang, Jieping Ye

Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ride-sharing platform marketplace.

Decision Making

CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

no code implementations27 May 2019 Jiarui Jin, Ming Zhou, Wei-Nan Zhang, Minne Li, Zilong Guo, Zhiwei Qin, Yan Jiao, Xiaocheng Tang, Chenxi Wang, Jun Wang, Guobin Wu, Jieping Ye

How to optimally dispatch orders to vehicles and how to trade off between immediate and future returns are fundamental questions for a typical ride-hailing platform.

Multiagent Systems

HIPAD - A Hybrid Interior-Point Alternating Direction algorithm for knowledge-based SVM and feature selection

no code implementations16 Nov 2014 Zhiwei Qin, Xiaocheng Tang, Ioannis Akrotirianakis, Amit Chakraborty

We consider classification tasks in the regime of scarce labeled training data in high dimensional feature space, where specific expert knowledge is also available.

Feature Selection General Classification

Practical Inexact Proximal Quasi-Newton Method with Global Complexity Analysis

1 code implementation26 Nov 2013 Katya Scheinberg, Xiaocheng Tang

Recently several methods were proposed for sparse optimization which make careful use of second-order information [10, 28, 16, 3] to improve local convergence rates.

Efficiently Using Second Order Information in Large l1 Regularization Problems

no code implementations27 Mar 2013 Xiaocheng Tang, Katya Scheinberg

We propose a novel general algorithm LHAC that efficiently uses second-order information to train a class of large-scale l1-regularized problems.

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