Search Results for author: Shaogang Ren

Found 11 papers, 4 papers with code

A Deep Decomposable Model for Disentangling Syntax and Semantics in Sentence Representation

no code implementations Findings (EMNLP) 2021 Dingcheng Li, Hongliang Fei, Shaogang Ren, Ping Li

Recently, disentanglement based on a generative adversarial network or a variational autoencoder has significantly advanced the performance of diverse applications in CV and NLP domains.

Disentanglement Generative Adversarial Network +3

Dynamic Incremental Optimization for Best Subset Selection

no code implementations4 Feb 2024 Shaogang Ren, Xiaoning Qian

Best subset selection is considered the `gold standard' for many sparse learning problems.

Sparse Learning

Causal Bayesian Optimization via Exogenous Distribution Learning

no code implementations3 Feb 2024 Shaogang Ren, Xiaoning Qian

A new CBO method is developed by leveraging the learned exogenous distribution.

Bayesian Optimization

Word Embedding with Neural Probabilistic Prior

no code implementations21 Sep 2023 Shaogang Ren, Dingcheng Li, Ping Li

To improve word representation learning, we propose a probabilistic prior which can be seamlessly integrated with word embedding models.

Representation Learning

Variational Flow Graphical Model

no code implementations6 Jul 2022 Shaogang Ren, Belhal Karimi, Dingcheng Li, Ping Li

VFGs learn the representation of high dimensional data via a message-passing scheme by integrating flow-based functions through variational inference.

Representation Learning Variational Inference

Best Subset Selection with Efficient Primal-Dual Algorithm

no code implementations5 Jul 2022 Shaogang Ren, Guanhua Fang, Ping Li

Best subset selection is considered the `gold standard' for many sparse learning problems.

Sparse Learning

Causal Discovery with Flow-based Conditional Density Estimation

1 code implementation ICDM 21 2021 Shaogang Ren, Haiyan Yin, Mingming Sun, Ping Li

Then we formulate a novel evaluation metric to infer the scores for each potential causal direction based on the variance of the conditional density estimation.

Causal Discovery Density Estimation

Estimate the Implicit Likelihoods of GANs with Application to Anomaly Detection

1 code implementation20 Apr 2020 Shaogang Ren, Dingcheng Li, Zhixin Zhou, Ping Li

The thriving of deep models and generative models provides approaches to model high dimensional distributions.

Anomaly Detection

Safe Active Feature Selection for Sparse Learning

no code implementations15 Jun 2018 Shaogang Ren, Jianhua Z. Huang, Shuai Huang, Xiaoning Qian

More critically, SAIF has the safe guarantee as it has the convergence guarantee to the optimal solution to the original full LASSO problem.

feature selection Sparse Learning

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