activity
20182021
most citedMaximising the Benefits of an Acutely Limited Number of COVID-19 Tests

7 citations · 9 across the 2 of their papers we have counts for

collaborators

6 papers

cs.GT20212 cited

RPPLNS: Pay-per-last-N-shares with a Randomised Twist

Philip Lazos, Francisco J. Marmolejo-Cossío, Xinyu Zhou +1

"Pay-per-last--shares" (PPLNS) is one of the most common payout strategies used by mining pools in Proof-of-Work (PoW) cryptocurrencies. As with any payment scheme, it is impera…

cs.GT2020

Learning Strong Substitutes Demand via Queries

Paul W. Goldberg, Edwin Lock, Francisco Marmolejo-Cossío

This paper addresses the computational challenges of learning strong substitutes demand when given access to a demand (or valuation) oracle. Strong substitutes demand generalises t…

q-bio.PE20207 cited

Maximising the Benefits of an Acutely Limited Number of COVID-19 Tests

Jakob Jonnerby, Philip Lazos, Edwin Lock +4

We propose a novel testing and containment strategy in order to contain the spread of SARS-CoV2 while permitting large parts of the population to resume social and economic activit…

cs.CR2019

Fairness and Efficiency in DAG-based Cryptocurrencies

Georgios Birmpas, Elias Koutsoupias, Philip Lazos +1

Bitcoin is a decentralised digital currency that serves as an alternative to existing transaction systems based on an external central authority for security. Although Bitcoin has…

cs.GT2019

Competing (Semi)-Selfish Miners in Bitcoin

Francisco J. Marmolejo-Cossío, Eric Brigham, Benjamin Sela +1

The Bitcoin protocol prescribes certain behavior by the miners who are responsible for maintaining and extending the underlying blockchain; in particular, miners who successfully s…

cs.GT2018

Learning Convex Partitions and Computing Game-theoretic Equilibria from Best Response Queries

Paul W. Goldberg, Francisco J. Marmolejo-Cossío

Suppose that an -simplex is partitioned into convex regions having disjoint interiors and distinct labels, and we may learn the label of any point by querying it. The learni…