Demo Data from EDBT 2026 / reviewers in the wild / expert
Yogeshwer Sharma
dblp:44/3609
· DBLP profile ↗
8ranked-venue papers
3as first author
0since 2021 · last 2015
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 5 · 2 first-authorArtificial intelligence and machine learning · 4 · 1 first-authorComputer networks · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
4 papers |
Algorithmic game theory and mechanism design · 89% Approximation and online algorithms · 5% Mathematical optimization · 5% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Distributed systems · 100% | |
| Artificial intelligence
2 papers |
Reinforcement learning · 100% |
Topics — the 18 heaviest of 19, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithmic game theory and mechanism design › multi-armed bandit
multi-armed bandit mechanism |
0.3 | 2 | 2014 | Characterizing Truthful Multi-armed Bandit Mechanisms · SIAM J. Comput. 2014 Characterizing truthful multi-armed bandit mechanisms: extended abstract · EC 2009 |
Algorithmic game theory and mechanism design › auction theory › advertising auctions
pay-per-click auction |
0.3 | 2 | 2014 | Characterizing Truthful Multi-armed Bandit Mechanisms · SIAM J. Comput. 2014 Characterizing truthful multi-armed bandit mechanisms: extended abstract · EC 2009 |
Distributed systems › replication
geo-replication |
0.2 | 1 | 2015 | Wormhole: Reliable Pub-Sub to Support Geo-replicated Internet Services · NSDI 2015 |
Distributed systems
publish/subscribe systems |
0.2 | 1 | 2015 | Wormhole: Reliable Pub-Sub to Support Geo-replicated Internet Services · NSDI 2015 |
Machine learning › Reinforcement learning
multi-armed bandit |
0.2 | 1 | 2014 | Characterizing Truthful Multi-armed Bandit Mechanisms · SIAM J. Comput. 2014 |
Machine learning › Reinforcement learning
regret minimization |
0.2 | 1 | 2014 | Characterizing Truthful Multi-armed Bandit Mechanisms · SIAM J. Comput. 2014 |
Algorithmic game theory and mechanism design › mechanism design
auction design |
0.2 | 1 | 2014 | Characterizing Truthful Multi-armed Bandit Mechanisms · SIAM J. Comput. 2014 |
Algorithmic game theory and mechanism design › mechanism design
truthful mechanism design |
0.2 | 1 | 2014 | Characterizing Truthful Multi-armed Bandit Mechanisms · SIAM J. Comput. 2014 |
Algorithmic game theory and mechanism design › mechanism design › auction design
truthful auction |
0.1 | 1 | 2009 | Characterizing truthful multi-armed bandit mechanisms: extended abstract · EC 2009 |
Algorithmic game theory and mechanism design
welfare maximization |
0.1 | 1 | 2009 | Characterizing truthful multi-armed bandit mechanisms: extended abstract · EC 2009 |
Machine learning › Reinforcement learning
bandit |
0.1 | 1 | 2008 | Regret Bounds for Sleeping Experts and Bandits · COLT 2008 |
Algorithmic game theory and mechanism design
price of anarchy |
0.1 | 1 | 2007 | Stackelberg thresholds in network routing games or the value of altruism · EC 2007 |
Approximation and online algorithms
prize-collecting problems |
0.1 | 1 | 2007 | Approximation algorithms for prize collecting forest problems with submodular penalty functions · SODA 2007 |
Algorithmic game theory and mechanism design › congestion games
selfish routing |
0.1 | 1 | 2007 | Stackelberg thresholds in network routing games or the value of altruism · EC 2007 |
Algorithmic game theory and mechanism design › stackelberg game
stackelberg routing |
0.1 | 1 | 2007 | Stackelberg thresholds in network routing games or the value of altruism · EC 2007 |
Mathematical optimization
submodular optimization |
0.1 | 1 | 2007 | Approximation algorithms for prize collecting forest problems with submodular penalty functions · SODA 2007 |
Distributed systems › fault tolerance
reliable communication |
0.1 | 1 | 2015 | Wormhole: Reliable Pub-Sub to Support Geo-replicated Internet Services · NSDI 2015 |
Distributed systems
replication |
0.1 | 1 | 2015 | Wormhole: Reliable Pub-Sub to Support Geo-replicated Internet Services · NSDI 2015 |
Methods — techniques the papers use, named apart from their topics
regret lower bound · 0.4dominant-strategy truthfulness · 0.2dominant strategy truthfulness · 0.2online learning · 0.1dominant strategy mechanism design · 0.1primal-dual approximation · 0.1optimization · 0.1game theory · 0.1LP rounding · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Wormhole: Reliable Pub-Sub to Support Geo-replicated Internet Services
Yogeshwer Sharma, Philippe Ajoux, Petchean Ang, David Callies, Abhishek Choudhary, Laurent Demailly, Thomas Fersch, Liat Atsmon Guz, Andrzej Kotulski, Sachin Kulkarni, Harry C. Li, Evgeniy Makeev, Kowshik Prakasam, Robbert van Renesse, Sabyasachi Roy, Pratyush Seth, Yee Jiun Song, Benjamin Wester, Kaushik Veeraraghavan, Peter Xie |
NSDI | 1 |
| 2014 | Characterizing Truthful Multi-armed Bandit MechanismsabstractWe consider a multiround auction setting motivated by pay-per-click auctions for Internet advertising. In each round the auctioneer selects an advertiser and shows her ad, which is then either clicked or not. An advertiser derives value from clicks; the value of a click is her private information. Initially, neither the auctioneer nor the advertisers have any information about the likelihood of clicks on the advertisements. The auctioneer's goal is to design a (dominant strategies) truthful mechanism that (approximately) maximizes the social welfare. If the advertisers bid their true private values, our problem is equivalent to the multi-armed bandit problem, and thus can be viewed as a strategic version of the latter. In particular, for both problems the quality of an algorithm can be characterized by regret, the difference in social welfare between the algorithm and the benchmark which always selects the same “best” advertisement. We investigate how the design of multi-armed bandit algorithms is affected by the restriction that the resulting mechanism must be truthful. We find that deterministic truthful mechanisms have certain strong structural properties---essentially, they must separate exploration from exploitation---and they incur much higher regret than the optimal multi-armed bandit algorithms. Moreover, we provide a truthful mechanism which (essentially) matches our lower bound on regret. Moshe Babaioff, Yogeshwer Sharma, Aleksandrs Slivkins |
SIAM J. Comput. | 2 |
| 2010 | Regret bounds for sleeping experts and bandits
Robert D. Kleinberg, Alexandru Niculescu-Mizil, Yogeshwer Sharma |
Mach. Learn. | 3 |
| 2009 | Characterizing truthful multi-armed bandit mechanisms: extended abstractabstractWe consider a multi-round auction setting motivated by pay-per-click auctions for Internet advertising. In each round the auctioneer selects an advertiser and shows her ad, which is then either clicked or not. An advertiser derives value from clicks; the value of a click is her private information. Initially, neither the auctioneer nor the advertisers have any information about the likelihood of clicks on the advertisements. The auctioneer's goal is to design a (dominant strategies) truthful mechanism that (approximately) maximizes the social welfare. Moshe Babaioff, Yogeshwer Sharma, Aleksandrs Slivkins |
EC | 2 |
| 2008 | Regret Bounds for Sleeping Experts and Bandits
Robert D. Kleinberg, Alexandru Niculescu-Mizil, Yogeshwer Sharma |
COLT | 3 |
| 2008 | Approximation Algorithms for Prize-Collecting Network Design Problems with General Connectivity Requirements
Chandrashekhar Nagarajan, Yogeshwer Sharma, David P. Williamson |
WAOA | 2 |
| 2007 | Stackelberg thresholds in network routing games or the value of altruismabstractNoncooperative network routing games are a natural model of userstrying to selfishly route flow through a network in order to minimize their own delays. It is well known that the solution resulting from this selfish routing (called the Nash equilibrium) can have social cost strictly higher than the cost of the optimum solution. One way to improve the quality of the resulting solution is to centrally control a fraction of the flow. A natural problem for the network administrator then is to route the centrally controlled flow in such a way that the overall cost of the solution is minimized after the remaining fraction has routed itself selfishl. Yogeshwer Sharma, David P. Williamson |
EC | 1 |
| 2007 | Approximation algorithms for prize collecting forest problems with submodular penalty functions
Yogeshwer Sharma, Chaitanya Swamy, David P. Williamson |
SODA | 1 |