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Same-Day Delivery with Fairness

Same-Day Delivery with Fairness

19 July 2020
Xinwei Chen
Tong Wang
Barrett W. Thomas
M. Ulmer
ArXivPDFHTML

Papers citing "Same-Day Delivery with Fairness"

14 / 14 papers shown
Title
A Deep Reinforcement Learning Approach for the Meal Delivery Problem
A Deep Reinforcement Learning Approach for the Meal Delivery Problem
H. Jahanshahi
Aysun Bozanta
Mucahit Cevik
E. M. Kavuk
Ayse Tosun Misirli
Sibel B. Sonuc
Bilgin Kosucu
Ayse Basar
58
28
0
24 Apr 2021
Fairness in Machine Learning
Fairness in Machine Learning
L. Oneto
Silvia Chiappa
FaML
275
493
0
31 Dec 2020
Deep Reinforcement Learning for Crowdsourced Urban Delivery: System
  States Characterization, Heuristics-guided Action Choice, and
  Rule-Interposing Integration
Deep Reinforcement Learning for Crowdsourced Urban Delivery: System States Characterization, Heuristics-guided Action Choice, and Rule-Interposing Integration
T. Ahamed
Bo Zou
Nahid Parvez Farazi
Theja Tulabandhula
17
4
0
29 Nov 2020
Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning
  with Average and Discounted Rewards
Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewards
Umer Siddique
Paul Weng
Matthieu Zimmer
FaML
OffRL
29
85
0
18 Aug 2020
Balancing the Tradeoff between Profit and Fairness in Rideshare
  Platforms During High-Demand Hours
Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours
Vedant Nanda
Pan Xu
Karthik Abinav Sankararaman
John P. Dickerson
A. Srinivasan
70
65
0
18 Dec 2019
Deep Q-Learning for Same-Day Delivery with Vehicles and Drones
Deep Q-Learning for Same-Day Delivery with Vehicles and Drones
Xinwei Chen
M. Ulmer
Barrett W. Thomas
19
101
0
25 Oct 2019
A Survey on Bias and Fairness in Machine Learning
A Survey on Bias and Fairness in Machine Learning
Ninareh Mehrabi
Fred Morstatter
N. Saxena
Kristina Lerman
Aram Galstyan
SyDa
FaML
475
4,285
0
23 Aug 2019
Repairing without Retraining: Avoiding Disparate Impact with
  Counterfactual Distributions
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions
Hao Wang
Berk Ustun
Flavio du Pin Calmon
FaML
62
85
0
29 Jan 2019
Bias Mitigation Post-processing for Individual and Group Fairness
Bias Mitigation Post-processing for Individual and Group Fairness
P. Lohia
Karthikeyan N. Ramamurthy
M. Bhide
Diptikalyan Saha
Kush R. Varshney
Ruchir Puri
FaML
43
156
0
14 Dec 2018
Learning Adversarially Fair and Transferable Representations
Learning Adversarially Fair and Transferable Representations
David Madras
Elliot Creager
T. Pitassi
R. Zemel
FaML
311
678
0
17 Feb 2018
A Convex Framework for Fair Regression
A Convex Framework for Fair Regression
R. Berk
Hoda Heidari
S. Jabbari
Matthew Joseph
Michael Kearns
Jamie Morgenstern
Seth Neel
Aaron Roth
FaML
97
342
0
07 Jun 2017
Fairness in Reinforcement Learning
Fairness in Reinforcement Learning
S. Jabbari
Matthew Joseph
Michael Kearns
Jamie Morgenstern
Aaron Roth
FaML
57
200
0
09 Nov 2016
Equality of Opportunity in Supervised Learning
Equality of Opportunity in Supervised Learning
Moritz Hardt
Eric Price
Nathan Srebro
FaML
105
4,276
0
07 Oct 2016
Job Selection in a Network of Autonomous UAVs for Delivery of Goods
Job Selection in a Network of Autonomous UAVs for Delivery of Goods
Pasquale Grippa
D. Behrens
C. Bettstetter
F. Wall
15
27
0
14 Apr 2016
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