Christine Chung's
Publications

For a more dependably up-to-date list of my publications, please see DBLP.

*undergraduate
research student

*A Case for Copeland: from theory to practice,
*with *Michelle Le, *Chloe Nguyen, *Leo Claney, *Krishh Tipnis, *Brian MacSweeney, and *Eric Huber. To appear in IJTCS 2024.*
*

*Maximizing the number of rides served for
time-limited Dial-a-Ride, *with
Barbara M Anthony, Ananya D Christman, and David Yuen*. Optimization
Methods and Software
(2024). *

*Earliest
Deadline First is a 2-Approximation for DARP with Time Windows, *with Barbara M.
Anthony, Christine Chung, Ananya Christman, David S. Yuen*.
*COCOA
2023. Also see full
version.

*Improved
Bounds for Revenue Maximization in Time-Limited Online Dial-a-Ride, *with A.D. Christman*, **N. Jaczko, *T. Li, *S. Westvold, *X. Xu, D. Yuen.* Springer Nature Operations Research Forum, 2(39), July 2021. *

*Serving Rides of
Equal Importance for Time-Limited Dial-a-Ride*, with Barbara M Anthony, Ananya D Christman,
and David Yuen*. *MOTOR 2021
(*20 ^{th }International Conference
on Mathematical Optimization Theory and Operations Research*), LNCS vol
12755: 35-50, Springer 2021.

We
consider a variant of the offline Dial-a-Ride problem with a single server
where each request has a source, destination, and a prize earned for serving
it. The goal for the server is to serve requests within a given time limit so as to maximize the total prize money. We consider the
variant where prize amounts are uniform which is equivalent to maximizing the
number of requests served. This setting is applicable when all rides may have
equal importance such as paratransit services. We first prove that no
polynomial-time algorithm can be guaranteed to serve the optimal number of
requests, even when the time limit for the algorithm is augmented by any
constant factor. We also show that the approximation ratio for a reasonable
class of algorithms for this problem is unbounded, unless the graph diameter is
bounded. We then show that the segmented best path (SBP) algorithm is a
4-approximation. We then present an algorithm, *k*-Sequence, that repeatedly serves the fastest set of *k* remaining requests,
and provide upper and lower bounds on its
performance.

*Equilibria in Doodle Polls
Under Three Tie-breaking Rules*, with Barbara M Anthony. *Theoretical
Computer Science*, Volume 822, 2020, Pages 61-71.

Doodle polls allow people to schedule meetings or events based on
time preferences of participants. Each participant indicates on a web-based
poll form which time slots they find acceptable and a time
slot with the most votes is chosen. This is a social choice mechanism known as
approval voting, in which a standard assumption is that all voters vote
sincerely -- no one votes "no" on a time slot they prefer to a time
slot they have voted "yes" on. We take a game-theoretic approach to
understanding what happens in approval voting assuming participants vote
sincerely. While our instances are framed in the context of the Doodle poll
application, the results apply more broadly to approval voting. First, we
characterize Doodle poll instances where sincere pure Nash Equilibria (NE)
exist, under lexicographic tie-breaking, random candidate, and random voter
tie-breaking. We then study the quality of such NE voting profiles in Doodle
polls, showing the price of anarchy and price of stability are both unbounded,
even when a time slot that many participants vote yes for is selected. Finally,
we find some reasonable conditions under which the quality of the NE (and
strong NE) is good.

*New Bounds for Maximizing Revenue in Online
Dial-a-Ride*, with Ananya Christman, *Nicholas Jaczko, *Tianzhi Li, *Scott Westvold, and
*Xinyue Xu, and David Yuen. IWOCA
2020 (*31 ^{st} International
Workshop on Combinatorial Algorithms*). LNCS vol 12126, Springer 2020.

In the Online-Dial-a-Ride Problem (OLDARP) a server travels
through a metric space to serve requests for rides. We consider a variant where
each request specifies a source, destination, release time, and revenue that is
earned for serving the request. The goal is to maximize the total revenue
earned within a given time limit. We prove that no non-preemptive deterministic
online algorithm for OLDARP can be guaranteed to earn more than twice the
revenue earned by an optimal offline solution. We then investigate
the Segmented Best Path (

*Maximizing
the Number of Rides Served for Dial-a-Ride*, with Barbara M Anthony, *Ricky Birnbaum, *Sara Boyd, Ananya
Christman, *Patrick Davis, *Jigar Dhimar, and David
Yuen. ATMOS
2019 (*Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and
Systems*).

We study a variation of offline Dial-a-Ride, where each request
has not only a source and destination, but also a revenue that is earned for serving
the request. We investigate this problem for the uniform metric space with
uniform revenues. While we present a study on a simplified setting of the
problem that has limited practical applications, this work provides the
theoretical foundation for analyzing the more general forms of the problem.
Since revenues are uniform the problem is equivalent to maximizing the number
of served requests. We show that the problem is NP-hard and present a 2/3
approximation algorithm. We also show that a natural generalization of this
algorithm has an approximation ratio at most 7/9.

*Robustly
Assigning Unstable Items*, with Ananya Das Christman, *Nicholas
Jaczko, *Scott Westvold, and David Yuen. *Journal of Combinatorial Optimization, *January 2020. Earlier version appeared in COCOA
2018 (12^{th} *Annual
International Conference on Combinatorial Optimization and Applications*).
LNCS vol 11346, Springer 2018.

We study the Robust Assignment Problem where
the goal is to assign items of various types to containers without exceeding
container capacity. We seek an assignment that uses the fewest number of
containers and is robust, that is, if any item becomes corrupt causing all
containers with that type of item to become unstable, every other item type is
still assigned to a stable container. We begin by presenting an optimal
polynomial time algorithm that finds a robust assignment using the minimum
number of containers for the case when the containers have infinite capacity.
Then we consider the case where all containers have some fixed capacity and
give an optimal polynomial-time algorithm for the special case where each type
of item has the same size. When the sizes of the item types are non-uniform, we
provide a polynomial-time 2-approximation for the problem. We also prove that
the approximation ratio of our algorithm is no lower than 1.813.

*Inefficiency of
Equilibria in Doodle Polls*, with Barbara Anthony. COCOA 2018 (12^{th} *Annual International Conference on
Combinatorial Optimization and Applications*). LNCS vol 11346, Springer
2018.

Doodle
polls allow people to schedule meetings or events based on time preferences of
participants. Each participant indicates on a web-based poll form which time
slots they find acceptable and a time slot with the
most votes is chosen. This is a social choice mechanism known as *approval voting*, in which a standard assumption is that all
voters vote *sincerely -- *no one votes "no" on a time slot
they prefer to a time slot they have voted "yes" on. We take a
game-theoretic approach to understanding what happens in Doodle polls assuming
participants vote sincerely. First we characterize
Doodle poll instances where sincere pure Nash Equilibria (NE) exist, both under
lexicographic tie-breaking and randomized tie-breaking. We then study the
quality of such NE voting profiles in Doodle polls, showing the price of
anarchy and price of stability are both unbounded, even when a time slot that
many participants vote yes for is selected. Finally, we find some reasonable
conditions under which the quality of the NE (and strong NE) is good.

*How Bad is Selfish Doodle
Voting?*, with Barbara Anthony. Extended Abstract in AAMAS 2018 (Proceedings
of the 17th *International Conference on
Autonomous Agents and Multiagent Systems*, Stockholm, Sweden, pp. 1856-1858,
July 10-15, 2018.

*Revenue
Maximization in Online Dial a Ride, *with Ananya
Christman, *Nicholas Jaczko, *Marina Milan, *Anna
Vasilchenko, and *Scott Westvold. ATMOS 2017 (17th Workshop on
*Algorithmic Approaches for Transportation
Modeling, Optimization, and Systems*).

We study a variation of the Online-Dial-a-Ride
Problem where each request comes with not only a source, destination and
release time, but also has an associated revenue. The server's goal is to
maximize its total revenue within a given time limit, T. We show that the
competitive ratio is unbounded for any deterministic online algorithm for the
problem. We then provide a 3-competitive algorithm for the problem in a uniform
metric space and a 6-competitive algorithm for the general case of weighted graphs
(under reasonable assumptions about the input instance). We conclude with an
experimental evaluation of our algorithm in simulated settings inspired by
real-world Dial-a-Ride data. Experimental results show that our algorithm
performs well when compared to an offline version of the algorithm and a greedy
algorithm.

*How
well do Doodle polls do?*, with *Danya
Alrawi and Barbara Anthony. SocInfo 2016 (8th *International
Conference on Social Informatics*)*.* LNCS vol 10046, Spring 2016.

Web-based Doodle polls, where respondents
indicate their availability for a collection of times provided by the poll
initiator, are an increasingly common way of selecting a time for an event or
meeting. Yet group dynamics can markedly influence an individual's response,
and thus the overall solution quality. Via theoretical worst-case analysis, we
analyze certain common behaviors of Doodle poll respondents, including when
participants are either more generous with or more protective of their time,
showing that deviating from one's ''true availability" can have a
substantial impact on the overall quality of the selected time.

*Serve or skip: the power of rejection in
online bottleneck matching*, with Barbara
Anthony. *Journal of Combinatorial
Optimization, *32(4), 1232-1253, November 2016. Earlier version appeared in
COCOA 2014 (The 8^{th} Annual *International
Conference on Combinatorial Optimization and Applications*).

We consider the online matching problem, where
n server-vertices lie in a metric space and n request-vertices that arrive over
time each must immediately be permanently assigned to a server-vertex. We focus
on the egalitarian bottleneck objective, where the goal is to minimize the
maximum distance between any request and its server. It has been demonstrated
that while there are effective algorithms for the utilitarian objective
(minimizing total cost) in the resource augmentation setting where the offline
adversary has half the resources, these are not effective for the egalitarian
objective. Thus, we propose a new Serve-or-Skip bicriteria analysis model,
where the online algorithm may reject or skip up to a specified number of requests, and propose two greedy algorithms: GriNN(t) and Grin*(t).

*Fairness in employee scheduling*, with *Erica Stockwell-Alpert. MISTA 2015 (*Multidisciplinary International Conference on Scheduling Theory and
Applications*).

We consider the problem of assigning shifts to
employees such that (1) all shift coverage requirements are satisfied, (2)
employees are available to work all shifts they are assigned, (3) the number of
total hours available for distribution is not exceeded, and (4) each employee
is assigned the number of hours they need. Our focus in this work is on
employee satisfaction (the percentage of their desired
hours an employee is assigned) and fairness (the employee satisfaction level
should be as uniform as possible). Because the problem is NP-hard, we propose
an approximation algorithm for maximizing the minimum employee satisfaction.

*Competitive cost-savings in data stream
management systems*, with Shenoda Guirguis and
Anastasia Kurdia. COCOON 2014 (The 20^{th}
International Computing and Combinatorics Conference).

In Continuous Data Analytics and in monitoring
applications, hundreds of similar Aggregate Continuous Queries (ACQs) are
registered at the Data Stream Management System (DSMS) to continuously monitor
the infinite input stream of data tuples. Optimizing the processing of these
ACQs is crucial in order for the DSMS to operate at
the adequate required scalability. One optimization technique is to share the
results of partial aggregation operations between different ACQs on the same
data stream. However, finding the query execution plan that attains maximum
reduction in total plan cost is computationally expensive. Weave Share, a
multiple ACQs optimizer that computes query plans in a greedy fashion, was
recently shown in experiments to achieve more than an order of magnitude
improvement over the best existing alternatives. In this paper we prove that
Weave Share approximates the optimal cost-savings to within a factor of 4 for a
practical variant of the problem.

*Online bottleneck matching*, with Barbara Anthony. *Journal
of Combinatorial Optimization*, Volume 27, Issue 1, pp. 100-114, January
2014. (Published online: Feb 2013. Earlier version appeared in COCOA 2012.)

We consider the online bottleneck matching
problem, where k server-vertices lie in a metric space and k request-vertices
that arrive over time each must immediately be permanently assigned to a
server-vertex. The goal is to minimize the maximum distance between any request
and its server. Because no algorithm can have a competitive ratio better than
O(k) for this problem, we use resource augmentation analysis to examine the
performance of three algorithms: the naive Greedy algorithm, Permutation, and
Balance. We show that while the competitive ratio of Greedy improves from
exponential (when each server-vertex has one server) to linear (when each
server-vertex has two servers), the competitive ratio of Permutation remains
linear. The competitive ratio of Balance is also linear with an extra server at
each server-vertex, even though it has been shown that an extra server makes it
constant-competitive for the min-weight matching
problem.

*Data plan throttling: a simple
consumer choice mechanism*, with Barbara
Anthony. Proceedings of the IEEE *Global
Communications Conference* (GLOBECOM 2013), pp. 3173-3178, December 2013.

Despite only a small portion of unlimited data
plan users experiencing throttling each month, it is a prominent source of
complaints from users and a significant concern for mobile network operators.
We propose a simple mechanism that allows users to choose when they want their
data transmission "fast," and when they want it "slow."
Users still have the same cap on total high-speed transfer before being
throttled, and hence may still be subject to throttling, but now they are given
some control. We propose a basic model of payoffs, and
demonstrate that the proposed mechanism would be preferable to the user over
the throttling policies currently in place. We then consider the impacts that
extend beyond a single user, and provide a framework
for determining the aggregate effects of such a mechanism.

*Auction-based admission control for
continuous queries in a multi-tenant DSMS*, with Lory Al Moakar, Panos
Chrysanthis, Shenoda Guirguis, Alexandros Labrinidis, Panayiotis Neophytou, and Kirk Pruhs. *International Journal of Next-Generation
Computing*, Vol 3, No 3,
November 2012.

The growing
popularity of monitoring applications and "Big Data" analytics used
by a variety of users will lead to a multi-tenant data stream management
system. This paper deals with the problem of admission control of continuous
queries, where the stream processing resources are sold to the end users. We
employ variable pricing by means of auction-based mechanisms. The admission
control auction mechanism determines which queries to admit, and how much to
charge the user for each query in a way that maximizes system revenue. The
admission mechanism is required to be strategyproof
and sybil-immune, incentivizing users to use the system honestly. Specifically,
we require that each user maximizes her payoff by bidding
her true value of having a query run. We further consider the requirement that
the mechanism be sybil-immune: that is, no user can increase her payoff by
submitting queries that she does not value. Given the above requirements, the
main challenges come from the difficulty of effectively utilizing shared
processing of continuous queries. We design several payment mechanisms and
experimentally evaluate them.

*Completion time scheduling and the WSRPT
algorithm*, with *Bo Xiong. ISCO 2012 (*International Symposium on Combinatorial
Optimization*).

We consider the online scheduling problem of
minimizing the total weighted and unweighted completion time on identical
parallel machines with preemptible jobs. We show a new general lower bound of
21/19 ≈ 1.105 on the competitive ratio of any deterministic
online algorithm for the unweighted problem and 16−14√11≈1.114
for the weighted problem. We then analyze the performance of the natural online
algorithm WSRPT (Weighted Shortest Remaining Processing Time). We show that
WSRPT is 2-competitive. We also prove that the lower bound on the competitive
ratio of WSRPT for this problem is 1.215.

*The power of fair pricing mechanisms*, with Katrina Ligett, Aaron Roth, and Kirk Pruhs. *Algorithmica*, Volume 63, Issue 3, pp. 634-644, July 2012.
(Published online: Nov 2011. Earlier
version appeared in LATIN 2010.)

We explore the revenue capabilities of
truthful, monotone ("fair") allocation and pricing functions for
resource-constrained auction mechanisms within a general framework that
encompasses unlimited supply auctions, knapsack auctions, and auctions with
general non-decreasing convex production cost functions. We study and compare
the revenue obtainable in each fair pricing scheme to the profit obtained by
the ideal omniscient multi-price auction. We show that for capacitated knapsack
auctions, no constant pricing scheme can achieve any approximation to the
optimal profit, but proportional pricing is as powerful as general monotone
pricing. In addition, for auction settings with arbitrary bounded
non-decreasing convex production cost functions, we present a proportional
pricing mechanism which achieves a poly-logarithmic approximation. Unlike
existing approaches, all of our mechanisms have fair
(monotone) prices, and all of our competitive analysis is with respect to the optimal profit extraction.

*Expanding
CS1: applications across the liberal arts*, with
Bridget Baird. *Journal of Computing
Sciences in Colleges*, 25(6), 47-54. (CCSCNE 2010) [Local copy]

This paper describes how applications in a
variety of disciplines can enhance the teaching of the CS1 course. Examples are
given from a range of disciplines, including mathematics, economics,
linguistics, history, biology, art and music. The applications are linked to
the computer science concepts being discussed. Such an approach broadens the
appeal of the introductory course and also teaches students valuable problem solving skills.

*SRPT is 1.86-competitive for completion time
scheduling*, with Tim Nonner and
Alex Souza. SODA 2010 (ACM-SIAM *Symposium
on Discrete Algorithms*).

We consider the classical
problem of scheduling preemptible jobs, that arrive over time, on identical
parallel machines. The goal is to minimize the total completion time of the
jobs. In standard scheduling notation of Graham et al. [5], this problem is
denoted P|r_j, pmtn|Σ_j
c_j. A popular algorithm called SRPT, which always
schedules the unfinished jobs with shortest remaining processing time, is known
to be 2-competitive, see Phillips et al. [13, 14]. This is also the best known competitive ratio for any online algorithm.
However, it is conjectured that the competitive ratio of SRPT is significantly
less than 2. Even breaking the barrier of 2 is considered a significant step
towards the final answer of this classical online
problem. We improve on this open problem by showing that SRPT is
1.86-competitive. This result is obtained using the following method, which
might be of general interest: We define two dependent random variables that sum
up to the difference between the cost of an SRPT schedule and the cost of an
optimal schedule. Then we bound the sum of the expected values of these random
variables with respect to the cost of the optimal schedule, yielding the
claimed competitiveness. Furthermore, we show a lower bound of 21/19 for SRPT,
improving on the previously best known 12/11 due to Lu et al. [11].

*Admission control mechanisms for continuous
queries in the cloud*, with Lory Al Moakar, Panos Chrysanthis,
Shenoda Guirguis, Alexandros Labrinidis, Panayiotis
Neophytou, and Kirk Pruhs. ICDE 2010 (IEEE *International Conference on Data Engineering*).

Amazon, Google, and IBM now sell cloud
computing services. We consider the setting of a
for-profit business selling data stream monitoring/management services and we
investigate auction-based mechanisms for admission control of continuous
queries. When submitting a query, each user also submits a bid of how much she
is willing to pay for that query to run. The admission control auction
mechanism then determines which queries to admit, and how much to charge each
user in a way that maximizes system revenue while being strategyproof
and sybil immune, incentivizing users to use the system honestly. Specifically,
we require that each user maximizes her payoff by bidding
her true value of having her query run. We design several payment mechanisms
and experimentally evaluate them. We describe the provable game theoretic
characteristics of each mechanism alongside its performance with respect to
maximizing profit and total user payoff.

*On the price of stability for
undirected network design*, with Giorgos
Christodoulou, Katrina Ligett, Evangelia Pyrga, and Rob van Stee. WAOA 2009 (*Workshop on Approximation and Online Algorithms*).

We continue
the study of the effects of selfish behavior in the network design problem. We
provide new bounds for the price of stability for network design with fair cost
allocation for undirected graphs. We consider the most general case, for which
the best known upper bound is the Harmonic number H_n
, where n is the number of agents, and the best previously known lower bound is
12/7 ≈ 1.778. We present a nontrivial lower bound of 42/23 ≈ 1.8261.
Furthermore, we show that for two players, the price of stability is exactly
4/3, while for three players it is at least 74/48 ≈ 1.542 and
at most 1.65. These are the first improvements on the bound of H_n for general networks. In particular,
this demonstrates a separation between the price of stability on
undirected graphs and that on directed graphs, where H_n
is tight. Previously, such a gap was only known for the cases where all players
have a shared source, and for weighted players.

*Stochastic stability in internet router
congestion games*, with Evangelia Pyrga.
SAGT 2009 (*Symposium on Algorithmic Game
Theory*). For a more complete version of this work, see the relevant chapter
of my PhD thesis.

Congestion control at bottleneck routers on the
internet is a long standing problem. Many policies
have been proposed for effective ways to drop packets from the queues of these
routers so that network endpoints will be inclined to share router capacity
fairly and minimize the overflow of packets trying to enter the queues. We study
just how effective some of these queuing policies are when each network
endpoint is a self-interested player with no information about the other
players' actions or preferences. By employing the adaptive learning model of
evolutionary game theory, we study policies such as Droptail,
RED, and the greedy-flow-punishing policy proposed by Gao et al. [10] to find
the stochastically stable states: the states of the system that will be reached
in the long run.

*The price of stochastic anarchy*, with Katrina Ligett, Kirk Pruhs and Aaron Roth. SAGT 2008 (*Symposium on Algorithmic Game Theory*).

We consider the solution concept of stochastic stability, and propose the price of stochastic anarchy as an
alternative to the price of (Nash) anarchy for quantifying the cost of
selfishness and lack of coordination in games. As a solution concept, the Nash
equilibrium has disadvantages that the set of stochastically stable states of a
game avoid: unlike Nash equilibria, stochastically stable states are the result
of natural dynamics of computationally bounded and decentralized agents, and are resilient to small perturbations from ideal
play. The price of stochastic anarchy can be viewed as a smoothed analysis of
the price of anarchy, distinguishing equilibria that are resilient to noise
from those that are not. To illustrate the utility of stochastic stability, we
study the load balancing game on unrelated machines. This game has an
unboundedly large price of Nash anarchy even when restricted to two players and
two machines. We show that in the two player case, the
price of stochastic anarchy is 2, and that even in the general case, the price
of stochastic anarchy is bounded. We conjecture that the price of stochastic
anarchy is O(m), matching the price of strong Nash anarchy without requiring
player coordination. We expect that stochastic stability will be useful in
understanding the relative stability of Nash equilibria in other games where
the worst equilibria seem to be inherently brittle.

*The online transportation problem: on
the exponential boost of one extra server*, with Kirk Pruhs and Patchrawat Uthaisombut. LATIN 2008 (*Latin American Theoretical Informatics Symposium*).

We present a poly-log-competitive deterministic
online algorithm for the online transportation problem on hierarchically
separated trees when the online algorithm has one extra server per site. Using
metric embedding results in the literature, one can then obtain a
poly-log-competitive randomized online algorithm for the online transportation
on an arbitrary metric space when the online algorithm has one extra server per
site.