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Fair Allocation in Online Markets
2014
Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14
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In the first algorithm, we address the max-min fairness objective which is defined as the minimum ratio among all advertisers of the actual revenue obtained by the allocation to given target revenues. ...
to interested advertisers, fairness considerations have surprisingly not received much attention in online allocation algorithms. ...
The second author is supported in part by startup funds from Duke University. ...
doi:10.1145/2661829.2662011
dblp:conf/cikm/GollapudiP14
fatcat:jufbywwllzhfdmmqrhk5pvhmlm
Fairness-aware Online Price Discrimination with Nonparametric Demand Models
[article]
2021
arXiv
pre-print
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In contrast to the standard √(T)-type regret in online learning, we show that the optimal regret in our case is Θ̃(T^4/5). ...
In particular, we consider a finite selling horizon of length T for a single product with two groups of customers. Each group of customers has its unknown demand function that needs to be learned. ...
Kandasamy et al. (2020) studied online demand estimation and allocation under the max-min fairness with applications to cloud computing. ...
arXiv:2111.08221v1
fatcat:pa5e4izs4fgtxppu3dku3cqpru
Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve
[article]
2021
arXiv
pre-print
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We show that in the online setting, the two desired properties (envy-freeness and efficiency) are in direct contention, in that any algorithm achieving additive counterfactual envy-freeness up to a factor ...
In simulation results, our algorithm provides allocations close to the optimal fair solution in hindsight, motivating its use in practical applications as the algorithm is able to adapt to any desired ...
We would also like to thank the Food Bank of the Southern Tier and the Cornell Mathematical Contest in Modeling for their collaboration. ...
arXiv:2105.05308v2
fatcat:7z4gm4s2tzervobey63aoubi6u
Group-level Fairness Maximization in Online Bipartite Matching
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2011.13908v2 fatcat:zijrdtiwqnfjrgbfo6mj5cndkqOther Versions
For these fairness objectives, we analyze how competitive online algorithms can be, in comparison to offline algorithms which know the sequence of demands in advance. ...
optimal in the total demand. ...
. = max{s, 1} · E[min{Pois(b/s),b}] b for integer b ≥ 1 and s > 0. ...
arXiv:2011.13908v3
fatcat:p3uvmaawzna3lkuhxr2ny3mcme
A Survey of Online Auction Mechanism Design Using Deep Learning Approaches
[article]
2021
arXiv
pre-print
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Online auction has been very widespread in the recent years. ...
With the advancement of computing technology and the bottleneck in theoretical frameworks, researchers are shifting gears towards online auction designs using deep learning approaches. ...
= min 1≤g≤G { max 1≤n≤N (w i g,n b i + β i g,n )} (2) b i = φ shared i (b ′ i ) = min 1≤g≤G { max 1≤n≤N (w shared g,n b ′ i + β shared g,n )} (3) The payment rule network takes in the outputb i from the ...
arXiv:2110.06880v1
fatcat:op5iia46xjfrznq7vfynbknkiq
Fair Dynamic Rationing
[article]
2022
arXiv
pre-print
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For an arbitrarily correlated sequence of demands, we establish upper bounds on the expected minimum fill rate (ex-post fairness) and the minimum expected fill rate (ex-ante fairness) achievable by any ...
As one example, early in the COVID-19 pandemic, the Federal Emergency Management Agency faced overwhelming, temporally scattered, a priori uncertain, and correlated demands for medical supplies from different ...
See also the work of Cayci et al. (2020) that considers fair resource allocation with online learning. ...
arXiv:2102.01240v4
fatcat:4uyqliwwi5dr3fftoiybcobbwm
Structural Self-adaptation for Decentralized Pervasive Intelligence
[article]
2019
arXiv
pre-print
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Communication structure plays a key role in the learning capability of decentralized systems. ...
Based on this benchmark dataset, 124 deterministic structural criteria, applied as learning meta-features, are systematically evaluated as well as two online structural self-adaptation strategies designed ...
For instance, minimizing the total power demand in the Smart Grid is a result of locally choosing the plan with the lowest power demand. ...
arXiv:1904.09681v2
fatcat:7pzzaqkvljfwvju4dxlkrqngte
Long-term resource fairness
2014
Proceedings of the 28th ACM international conference on Supercomputing - ICS '14
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We have implemented LTRF in YARN by developing LT-YARN, a long-term YARN fair scheduler, and shown that it leads to a better resource fairness than other state-of-the-art fair schedulers. ...
However, MemoryLess Resource Fairness (MLRF), widely used in many existing frameworks such as YARN, Mesos and Dryad, is not suitable for pay-as-you-use computing. ...
Long-Term Max-Min Fairness Model This subsection proposes long-term max-min fairness model for LTYARN. ...
doi:10.1145/2597652.2597672
dblp:conf/ics/TangLHL14
fatcat:ufs2ohptvndp3ps7un63qwkwpe
Online Stochastic Packing Applied to Display Ad Allocation
[chapter]
2010
Lecture Notes in Computer Science
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We then focus on the online display ad allocation problem and study the efficiency and fairness of various training-based and online allocation algorithms on data sets collected from real-life display ...
As our main theoretical result, we prove that a simple dual training-based algorithm achieves a (1−o(1))approximation guarantee in the random order stochastic model. ...
We also thank the Google display ad team, and especially Scott Benson for helping us with data sets used in this paper. ...
doi:10.1007/978-3-642-15775-2_16
fatcat:noti7knkyjgync3lst6td4qsdu
ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning
[article]
2020
arXiv
pre-print
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Inspired by these advances, in this paper, we extend techniques for approximating auctions using deep learning to address concerns of fairness while maintaining high revenue and strong incentive guarantees ...
In parallel, new research from Ilvento et al. and other groups has developed notions of fairness in the context of auction design. ...
With unit-demand valuations, the value of a subset is ( ) = max ∈ ({ }), the maximum individual valuation within that subset. ...
arXiv:2010.06398v1
fatcat:6btl3437nbaibaclnlotrmsa4e
Same-Day Delivery with Fairness
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2007.09541v1 fatcat:4aulssrscnbidamxff6bbg75xu
The demand for same-day delivery (SDD) has increased rapidly in the last few years and has particularly boomed during the COVID-19 pandemic. The fast growth is not without its challenge. ...
In addition to the overall service rate (utility), we maximize the minimal regional service rate across all regions (fairness). ...
8) where r [d] max = max j r [d] j , r [d] min = min j r [d] j . (9) The r [d] max and r [d] min represent the maximum and minimum acceptance rates across regions on day d. ...
arXiv:2007.09541v2
fatcat:tcuy4ykqz5g5bfltdp44srf6za
A General and Practical Datacenter Selection Framework for Cloud Services
2012
2012 IEEE Fifth International Conference on Cloud Computing
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In this paper, we argue that fairness should be considered to ensure users at disadvantageous locations also enjoy reasonable performance, and performance is balanced across the entire system. ...
We adopt a general fairness criterion based on Nash bargaining solutions, and present a general optimization framework that models the realistic environment and practical constraints that a cloud faces ...
Max-min and proportional fairness models are arguably the most widely used in the literature [5] - [7] . ...
doi:10.1109/cloud.2012.16
dblp:conf/IEEEcloud/XuL12
fatcat:dgp7g5soined5aqa6vxpfi63cy
Real-time electricity pricing for demand response using online convex optimization
2014
ISGT 2014
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Real-time electricity pricing strategies for demand response in smart grids are proposed. ...
Fairness and sparsity constraints are also incorporated. Numerical tests verify the effectiveness of the proposed approach. ...
p t − m t )θ t k P k P k (10) where [·] b a := min{max{·, a}, b}, and soft th λ (·) is a softthresholding function defined as soft th λ (x) := sgn(x) max{0, |x| − λ}. ...
doi:10.1109/isgt.2014.6816447
dblp:conf/isgt/KimG14
fatcat:3z2g5wjfsbhnzkeeqq7dhv3np4
An Online Task Assignment based on Quality Constraint for Spatio-temporal Crowdsourcing
2019
IEEE Access
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There are three algorithms: Random Algorithm, Random-Threshold-Based Algorithm (RT) and Adaptive random-threshold-based Algorithm (Adaptive RT) for maximizing the total utility in the online task assignment ...
But these algorithms ignore the distance cost and fairness between task requester and workers. ...
Q(t, w) = 1 − DR t − DR min DR max − DR min − q w − q min q max − q min . (1) Finally, the Online Task Assignment of Three Types of Objects in Spatio-temporal Crowdsourcing is introduced. ...
doi:10.1109/access.2019.2942155
fatcat:6bkfmabgirfrxbsohfsr3dzq5e
Improving Fairness for Distributed Interactive Applications in Software-Defined Networks
2020
Mathematical Problems in Engineering
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In this context, we address the DIAs' fair resource provisioning problems on computing and links load with the objective of balancing the achievable request rate and fairness among multiple flows in SDN ...
Then, we propose proactive assignment controller algorithm based on deep learning and fairness path allocation algorithm to share the bottleneck links. ...
allocated of r f i going through allocated path p f i , eventually converging to the egalitarian max-min fair allocation as α ⟶ ∞, and α ≈ 5 is sufficient for very good approximation in [26] . us, the ...
doi:10.1155/2020/5207105
fatcat:r4hnbqsfevgdnddnaottsaxkm4
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