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researcher

Mihai Cucuringu

20 papers hereh-index 11386 citations26 works total

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

author position
  • middle author11
  • last author9

Across the 20 of 20 papers where every author was matched, so the position is known.

fields
  • stat.ML6
  • cs.LG5
  • cs.SI3
  • q-fin.TR2
  • stat.AP2
  • q-fin.CP1
same name
  • Mihai Cucuringu — 31 papers, h 12
  • Mihai Cucuringu — 12 papers, h 8
  • Mihai Cucuringu — 11 papers, h 10
  • Mihai Cucuringu — 5 papers, h 3
  • Mihai Cucuringu — 5 papers, h 2
  • Mihai Cucuringu — 3 papers, h 2

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
20192025
most citedCore-Periphery Structure in Directed Networks

41 citations · 95 across the 19 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

stat.AP2024

GDP nowcasting with large-scale inter-industry payment data in real time -- A network approach

Anastasia Mantziou, Kerstin Hotte, Mihai Cucuringu +1

Real-time economic information is essential for policy-making but difficult to obtain. We introduce a granular nowcasting method for macro- and industry-level GDP using a network a…

q-fin.CP2024★ 1 cited

Causality-Inspired Models for Financial Time Series Forecasting

Daniel Cunha Oliveira, Yutong Lu, Xi Lin +2

We introduce a novel framework to financial time series forecasting that leverages causality-inspired models to balance the trade-off between invariance to distributional changes a…

stat.ML2024

Split Conformal Prediction under Data Contamination

Jase Clarkson, Wenkai Xu, Mihai Cucuringu +2

Conformal prediction is a non-parametric technique for constructing prediction intervals or sets from arbitrary predictive models under the assumption that the data is exchangeable…

cs.LG2024

L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders

Ruikang Ouyang, Andrew Elliott, Stratis Limnios +2

For analysing real-world networks, graph representation learning is a popular tool. These methods, such as a graph autoencoder (GAE), typically rely on low-dimensional representati…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.