Alex Slivkins: publications

1. Selection and influence in cultural dynamics (2012)
   David Kempe, Jon Kleinberg, Sigal Oren and Aleksandrs Slivkins One of the fundamental principles driving diversity or homogeneity in domains such as cultural differentiation, political affiliation, and product adoption is the tension between two forces: influence (the tendency of people to become similar to others they interact with) and selection (the tendency to interact with others who are already similar). Influence tends to promote homogeneity within a society, while selection frequently causes fragmentation. When both forces are in effect simultaneously, it becomes an interesting question to analyze which societal outcomes should be expected.

   We analyze a natural class of models built upon active lines of work in political opinion formation, cultural diversity, and language evolution, due to Axelrod (1997), Deffuant et al. (2000) and Abrams and Strogatz (2003). Our basic model posits an arbitrary graph structure describing which "types" of people can influence one another (i.e., people are only influenced by sufficiently similar interaction partners). We achieve a complete characterization of (stable) equilibria and prove convergence from all starting states. We also obtain partial results for a more general model with a second graph structure describing which types of people can interact with one another.

2. Low-distortion Inference of Latent Similarities from a Multiplex Social Network (2012)
   Ittai Abraham, Shiri Chechik, David Kempe and Aleksandrs Slivkins It is commonly assumed that individuals tend to be more similar to their friends than to strangers. Thus, we can view an observed social network as a noisy signal about the latent underlying "social space": the way in which individuals are (dis)similar. This naturally raises the inverse question: given a social network, how accurately can we reconstruct the social space?

   We begin to address this problem formally. We assume that each category (e.g., geography, profession, hobbies) is characterized by a latent metric capturing (dis)similarities in this category, and gives rise to a separate social network: a random graph parametrized by this metric. The algorithm only observes the unlabeled union of these graphs, and reconstructs each metric with provably low distortion.

3. The best of both worlds: stochastic and adversarial bandits.
   Sébastien Bubeck and Aleksandrs Slivkins
   COLT 2012: Conf. on Learning Theory. We present a new bandit algorithm whose regret is optimal both for adversarial rewards and for stochastic rewards, achieving, resp., square-root regret and polylog regret. Adversarial rewards and stochastic rewards are the two main settings for (non-Bayesian) multi-armed bandits; prior work treats them separately, and does not attempt to jointly optimize for both.
4. Dynamic pricing with limited supply
   Moshe Babaioff, Shaddin Dughmi, Robert Kleinberg and Aleksandrs Slivkins
   EC 2012: ACM Symp. on Electronic Commerce
   Preliminary version appeared in WBMD 2011 (a workshop at ACM EC 2011). We consider dynamic pricing with limited supply and unknown demand distribution. We extend multi-armed bandit techniques to the limited supply setting, and obtain optimal regret rates.
5. Multi-armed bandits on implicit metric spaces
   NIPS 2011: Conf. on Neural Information Processing Systems. Suppose an MAB algorithm is given a tree-based classification of arms. This tree implicitly defines a "similarity distance" between arms, but the numeric distances are not revealed to the algorithm. Our algorithm (almost) matches the best known guarantees for the setting (Lipschitz MAB) in which the distances are revealed.
6. Monotone multi-armed bandit allocations
   Open Question at COLT 2011: Conf. on Learning Theory. The reduction from my EC'10 paper, applied to multi-armed bandit mechanisms, opens up a problem of designing monotone (regret-minimizing) MAB allocation rules -- a new twist on the familiar multi-armed bandit problem.
7. Contextual bandits with similarity information
   COLT 2011: Conf. on Learning Theory. In each round nature reveals a 'context' x, algorithm chooses an 'arm' y, and the expected payoff is μ(x,y). Similarity info is given: a metric space over the (x,y) pairs such that μ is a Lipschitz function. Interpreting the current time as a part of the 'context', we obtain a very general bandit framework that includes slowly changing payoffs and variable sets of arms. The main algorithmic idea is to adapt the partitions of the metric space to frequent context arrivals and high-payoff regions.
8. Learning optimally diverse rankings over large document collections
   Aleksandrs Slivkins, Filip Radlinski and Sreenivas Gollapudi
   ICML 2010: Intl. Conf. on Machine Learning.
   Preliminary version appeared in NIPS 2009 Ranking Workshop. We present a learning-to-rank framework for web search that incorporates similarity and correlation between documents and thus, unlike prior work, scales to large document collections.
9. Truthful Mechanisms with Implicit Payment Computation
   Moshe Babaioff, Robert Kleinberg and Aleksandrs Slivkins
   EC 2010: ACM Symp. on Electronic Commerce
   • Best paper award
   We show that creating a randomized single-parameter truthful mechanism is essentially as easy as a single call to a monotone allocation function. Applying this reduction to MAB mechanisms, we design truthful MAB mechanisms for stochastic payoffs.
10. 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 bandits 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 (essentially) on whether the metric space is countable.
11. Adapting to the Shifting Intent of Search Queries
    Umar Syed, Aleksandrs Slivkins and Nina Mishra
    NIPS'09: Annual Conf. on Neural Information Processing Systems Query intent may shift over time. A classifier can use the available signals to predict a shift in intent. Then a bandit algorithm can be used to find the new relevant results. We present a meta-algorithm that combines such classifier with a bandit algorithm in a feedback loop, with favorable regret guarantees.
12. Triangulation and Embedding using Small Sets of Beacons
    Jon Kleinberg, Aleksandrs Slivkins and Tom Wexler.
    J. of the ACM, 56(6), Sept 2009.
    • Significantly revised merge of papers from FOCS'04 and SODA'05.
    • For the full story (with results from FOCS'05) see Chapter 3 of my PhD thesis.
    We consider metric embeddings and triangulation-based distance estimation in a distributed framework with low load on the participating nodes. Our results provide theoretical insight into the empirical success of several recent Internet-related projects.
13. Characterizing Truthful Multi-Armed Bandit Mechanisms
    Moshe Babaioff, Yogeshwer Sharma and Aleksandrs Slivkins
    EC 2009: ACM Symp. on Electronic Commerce We consider a natural strategic version of the MAB problem motivated by pay-per-click auctions. We show that requiring an MAB algorithm to be incentive-compatible has striking consequences both for structure and regret.
14. 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 multi-armed bandit 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.
15. Multi-armed Bandits in Metric Spaces
    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.
16. Approximate Matching for Peer-to-Peer Overlays with Cubit
    Bernard Wong, Aleksandrs Slivkins and Emin G. Sirer Cubit is a system that provides fully decentralized approximate keyword search capabilities to a peer-to-peer network. You can use Cubit to find a movie, song or artist even if you misspell the title or the name.
17. Towards Fast Decentralized Construction of Locality-Aware Overlay Networks
    PODC 2007: ACM Symp. on Principles of Distributed Computing [slides] We provide fast (polylog-time) distributed constructions for various locality-aware (low-stretch) distributed data structures, such as distance labeling schemes, name-independent routing schemes, and multicast trees.
18. Oscillations with TCP-like Flow Control in Networks of Queues
    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.
19. Metric Embeddings with Relaxed Guarantees
    T-H. Hubert Chan, Kedar Dhamdhere, Anupam Gupta, Jon Kleinberg and A. Slivkins
    SIAM J. on Computing, 38(6): 2303-2329, March 2009.
    FOCS 2005: IEEE Symp. on Foundations of Computer Science [slides]
    • the FOCS'05 version is merged with (I.Abraham, Y.Bartal, O.Neiman).
    Given any x, any metric admits a low-dim embedding into L_p, 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.
20. Meridian: A Lightweight Network Location Service without Virtual Coordinates
    ACM SIGCOMM 2005
    Bernard Wong, Aleksandrs Slivkins and Emin G. Sirer. Meridian is a scalable overlay network for performing locality-aware node selection. The project features a live PlanetLab deployement.
21. Distance Estimation and Object Location via Rings of Neighbors
    PODC 2005: ACM Symp. on Principles of Distributed Computing [slides]
    • Best Student Paper Award (eligibility: at least one student author)
    Special issue of "Distributed Computing": Vol. 19, No. 4. (March 2007). We approach several problems on distance estimation and object location with a unified technique called ''rings of neighbors''. Using this technique on metrics of low doubling dimension, we obtain significant improvements for low-stretch routing schemes, searchable small-world networks, distance labeling, and triangulation-based distance estimation.
22. Distributed Approaches to Triangulation and Embedding
    SODA 2005: ACM-SIAM Symp. on Discrete Algorithms
    [recommended version: merged journal version of the FOCS'04 paper] Following up on the FOCS'04 paper, we consider metric embeddings and triangulation-based distance estimation in a distributed framework with low load on all participating nodes.
23. Triangulation and Embedding using Small Sets of Beacons
    Jon Kleinberg, Aleksandrs Slivkins and Tom Wexler.
    FOCS 2004: The IEEE Symp. on Foundations of Computer Science
    • The journal version includes results from the SODA'05 paper.
    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.
24. Network Failure Detection and Graph Connectivity
    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.
25. Parameterized Tractability of Edge-Disjoint Paths on DAGs
    SIAM J. on Discrete Math, 24(1): 146-157, Feb 2010.
    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.
26. Interleaving Schemes on Circulant Graphs with Two Offsets
    Aleksandrs Slivkins and Shuki Bruck.
    Discrete Mathematics 309(13): 4384-4398, July 2009.
    Undergraduate research project (1999-2000), tech report (2002). We construct optimal interleaving schemes on infinite circulant graphs with two offsets. Interleaving is used for error-correcting on a bursty noisy channel.

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