Philippe Very

According to our database1, Philippe Very authored at least 10 papers between 2015 and 2021.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
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Article 
PhD thesis 
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Other 

Links

On csauthors.net:

Bibliography

2021
Stochastic Adversarial Gradient Embedding for Active Domain Adaptation.
Proceedings of the Workshop on Interactive Adaptive Learning (IAL 2021) co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021), 2021

Robust Domain Adaptation: Representations, Weights and Inductive Bias (Extended Abstract).
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

2020
Robust Domain Adaptation: Representations, Weights and Inductive Bias.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

2019
Learning Invariant Representations for Sentiment Analysis: The Missing Material is Datasets.
CoRR, 2019

Hidden Covariate Shift: A Minimal Assumption For Domain Adaptation.
CoRR, 2019

2016
Toward a generic representation of random variables for machine learning.
Pattern Recognit. Lett., 2016

2015
Comment partitionner automatiquement des marches aléatoires ? Avec application à la finance quantitative.
CoRR, 2015

HCMapper: An interactive visualization tool to compare partition-based flat clustering extracted from pairs of dendrograms.
CoRR, 2015

A Proposal of a Methodological Framework with Experimental Guidelines to Investigate Clustering Stability on Financial Time Series.
Proceedings of the 14th IEEE International Conference on Machine Learning and Applications, 2015

Clustering Random Walk Time Series.
Proceedings of the Geometric Science of Information - Second International Conference, 2015


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