See my
main page
for the chronological list of publications and other info.
Algorithmic Mechanism Design
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Characterizing Truthful Multi-Armed Bandit Mechanisms
(full version: rev. Sept'09)
Moshe Babaioff, Yogeshwer Sharma and Aleksandrs Slivkins
EC 2009: ACM Symp. on Electronic Commerce
We consider a multi-round auction setting motivated by pay-per-click auctions in the Internet advertising. Our setting can be viewed as a strategic version of the MAB problem. We investigate how the design of MAB algorithms is affected by the restriction of truthfulness. We show striking differences in terms of both structure and regret.
Multi-Armed Bandits
We study various multi-armed bandit (MAB) problems in which external information on payoffs is available. We focus on prior-free (regret minimizing) formulations.
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Adapting to a Changing Environment: the Brownian Restless Bandits
Aleksandrs Slivkins and Eli Upfal.
COLT 2008:
Conf. on Learning Theory.
We study a version of the stochastic MAB problem in which the expected reward of each arm evolves stochastically and gradually in time, following an independent Brownian motion or a similar process. Our benchmark is a hypothetical policy that chooses the best arm in each round.
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Multi-armed Bandits in Metric Spaces (full version)
Robert Kleinberg, Aleksandrs Slivkins and Eli Upfal.
STOC 2008:
ACM Symp. on Theory of Computing
We introduce the 'Lipschitz MAB problem': a stochastic MAB problem, possibly with a very large set of arms, such that the expected payoffs obey a Lipschitz condition with respect to a given metric space. The goal is to minimize regret as a function of time, both in the worst case and for 'nice' problem instances.
- Sharp Dichotomies for Regret Minimization in Metric Spaces
Robert Kleinberg and Aleksandrs Slivkins
SODA 2010:
ACM-SIAM Symp. on Discrete Algorithms
We further study multi-armed bandit problems in metric spaces, focusing on the connections between online learning and metric topology. The main result is that the worst-case regret is either O(log t) or at least sqrt{t}, depending on whether the completion of the metric space is compact and countable. We prove a number of other dichotomy-style results, and extend them to the full-feedback (experts) version.
- Adapting to the Shifting Intent of Search Queries
Umar Syed, Nina Mishra and Aleksandrs Slivkins
NIPS'09:
Annual Conf. on Neural Information Processing Systems
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Contextual bandits with similarity information (2009)
In the 'contextual bandits' setting, in each round nature reveals a 'context' x, algorithm chooses an 'arm' y, and the expected payoff is &mu(x,y). Similarity info is expressed by a metric space over the (x,y) pairs such that &mu is a Lipschitz function. Our algorithms are based on adaptive (rather than uniform) partitions of the metric space which are adjusted to the popular and high-payoff regions.
Metric embeddings
We consider metric embeddings and triangulation-based distance estimation
in a distributed framework where nodes
measure distances only to a small set of beacons.
Our results provide theoretical insight into the empirical success of several recent
Internet-related projects.
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Triangulation and Embedding using Small Sets of Beacons
(journal version: rev. Aug'08)
Jon Kleinberg, Aleksandrs Slivkins and Tom Wexler.
J. of the ACM, 56(6), Sept 2009.
FOCS 2004
IEEE Symp. on Foundations of Computer Science
[slides]
[The journal version is merged with the SODA'05 paper]
For a full story that includes results from soda05 and focs05, see Chapter 3 of my thesis.
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Distributed Approaches to Triangulation and Embedding
SODA 2005
ACM-SIAM Symp. on Discrete Algorithms
[recommended version: merged journal version of the FOCS'04 paper]
We consider metric embeddings and triangulation-based distance
estimation in a distributed framework with low load on
all participating nodes.
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Metric Embeddings with Relaxed Guarantees
(journal version: rev. Aug'08)
T-H. Hubert Chan, Kedar Dhamdhere, Anupam Gupta, Jon Kleinberg and Aleksandrs Slivkins
SIAM J. on Computing, 38(6): 2303-2329, March 2009.
FOCS 2005:
IEEE Symp. on Foundations of Computer Science
[slides]
- The focs05 version is merged with an independent submission by
I.Abraham, Y.Bartal and O.Neiman.
Given any x, any metric admits a low-dim embedding
into Lp, p>=1 with disortion D(x) = O(log 1/x)
on all but an x-fraction of edges.
Moreover, any decomposable metric (e.g. any doubling metric)
admits a low-dim embedding such that
D(x) = O(log 1/x)^{1/p}
for all x.
Locality-aware overlay networks
We approach locality-aware overlay networks
with a unified framework:
an overlay network structured around multi-resolution rings.
Miscellaneous network design problems
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Oscillations with TCP-like Flow Control in Networks of Queues
(full version)
Matthew Andrews and Aleksandrs Slivkins
INFOCOM 2006
IEEE Conf. on Computer Communications
For a wide range of TCP-like fluid-based congestion control models,
we construct a network of sessions and (almost) FIFO routers such that
starting from a certain initial state, the system returns to the same
state eventually. Contrasting the prior work, in our example the total
sending rate of all sessions that come through any given router never
exceeds its capacity.
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Network Failure Detection and Graph Connectivity
(full version)
Jon Kleinberg, Mark Sandler and Aleksandrs Slivkins.
SIAM J. on Computing, 38(4): 1330-1346, Aug 2008.
SODA 2004:
The ACM-SIAM Symp. on Discrete Algorithms
[slides]
We detect network partitions -- with strong provable guarantees -- using
a small set of 'agents' placed randomly on nodes of the network.
We parameterize our guarantees by edge- and
node-connectivity of the underlying graph.
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Parameterized Tractability of Edge-Disjoint Paths on DAGs
(journal version: rev. Sept'08)
SIAM J. on Discrete Math, to appear in 2009.
ESA 2003:
The European Symp. on Algorithms
[slides]
We resolve a long-standing open question about the complexity of the k-edge-disjoint paths problem: we show that on DAGs it is W[1]-hard, hence unlikely to admit running time f(k)*poly(n). However, such running time can be achieved if the input+demands graph is almost Eulerian.
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