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Weina Wang

25 papers hereh-index 161.2k citations53 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author7
  • middle author11
  • last author6

Across the 24 of 25 papers where every author was matched, so the position is known.

fields
  • cs.PF8
  • cs.CR5
  • cs.DS3
  • cs.LG3
  • cs.GT2
  • cs.IT1
same name
  • Weina Wang — 6 papers, h 2
  • Weina Wang — 2 papers, h 8
  • Weina Wang — 2 papers, h 3
  • Weina Wang — 1 paper, h 2
  • Weina Wang — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20142023
most citedPrivacy-Utility Tradeoffs in Routing Cryptocurrency over Payment Channel Networks

29 citations · 85 across the 23 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.CR2019★ 29 cited

Privacy-Utility Tradeoffs in Routing Cryptocurrency over Payment Channel Networks

Weizhao Tang, Weina Wang, Giulia Fanti +1

Payment channel networks (PCNs) are viewed as one of the most promising scalability solutions for cryptocurrencies today. Roughly, PCNs are networks where each node represents a us…

cs.GT2019★ 1 cited

Impact of Social Learning on Privacy-Preserving Data Collection

Abdullah Basar Akbay, Weina Wang, Junshan Zhang

We study a model where a data collector obtains data from users through a payment mechanism, aiming to learn the underlying state from the elicited data. The private signal of each…

cs.SI2019★ 4 cited

QuickStop: A Markov Optimal Stopping Approach for Quickest Misinformation Detection

Honghao Wei, Xiaohan Kang, Weina Wang +1

This paper combines data-driven and model-driven methods for real-time misinformation detection. Our algorithm, named QuickStop, is an optimal stopping algorithm based on a probabi…

cs.LG2019★ 1 cited

Almost Boltzmann Exploration

Harsh Gupta, Seo Taek Kong, R. Srikant +1

Boltzmann exploration is widely used in reinforcement learning to provide a trade-off between exploration and exploitation. Recently, in (Cesa-Bianchi et al., 2017) it has been sho…

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