Search Results for author: Petar Stojanov

Found 5 papers, 3 papers with code

Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View

1 code implementation21 Mar 2024 Haoyue Dai, Ignavier Ng, Gongxu Luo, Peter Spirtes, Petar Stojanov, Kun Zhang

This particular test-wise deletion procedure, in which we perform CI tests on the samples without zeros for the conditioned variables, can be seamlessly integrated with existing structure learning approaches including constraint-based and greedy score-based methods, thus giving rise to a principled framework for GRNI in the presence of dropouts.


Partial Identifiability for Domain Adaptation

no code implementations10 Jun 2023 Lingjing Kong, Shaoan Xie, Weiran Yao, Yujia Zheng, Guangyi Chen, Petar Stojanov, Victor Akinwande, Kun Zhang

In general, without further assumptions, the joint distribution of the features and the label is not identifiable in the target domain.

Unsupervised Domain Adaptation

Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?

1 code implementation NeurIPS 2021 Petar Stojanov, Zijian Li, Mingming Gong, Ruichu Cai, Jaime Carbonell, Kun Zhang

We provide reasoning why when the supports of the source and target data from overlap, any map of $X$ that is fixed across domains may not be suitable for domain adaptation via invariant features.

Representation Learning Transfer Learning +1

Domain Adaptation as a Problem of Inference on Graphical Models

1 code implementation NeurIPS 2020 Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Qingsong Liu, Clark Glymour

Such a graphical model distinguishes between constant and varied modules of the distribution and specifies the properties of the changes across domains, which serves as prior knowledge of the changing modules for the purpose of deriving the posterior of the target variable $Y$ in the target domain.

Bayesian Inference Unsupervised Domain Adaptation

On Domain Transfer When Predicting Intent in Text

no code implementations NeurIPS Workshop Document_Intelligen 2019 Petar Stojanov, Ahmed Hassan Awadallah, Paul Bennett, Saghar Hosseini

In many domains, especially enterprise text analysis, there is an abundance of data which can be used for the development of new AI-powered intelligent experiences to improve people's productivity.

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