Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Ren, Shaogang"'
Granger causality, commonly used for inferring causal structures from time series data, has been adopted in widespread applications across various fields due to its intuitive explainability and high compatibility with emerging deep neural network pre
Externí odkaz:
http://arxiv.org/abs/2406.10419
In the transformative landscape of smart cities, the integration of the cutting-edge web technologies into time series forecasting presents a pivotal opportunity to enhance urban planning, sustainability, and economic growth. The advancement of deep
Externí odkaz:
http://arxiv.org/abs/2405.05430
Autor:
Ren, Shaogang, Qian, Xiaoning
Best subset selection is considered the `gold standard' for many sparse learning problems. A variety of optimization techniques have been proposed to attack this non-smooth non-convex problem. In this paper, we investigate the dual forms of a family
Externí odkaz:
http://arxiv.org/abs/2402.02322
Autor:
Ren, Shaogang, Qian, Xiaoning
Maximizing a target variable as an operational objective in a structural causal model is an important problem. Existing Causal Bayesian Optimization~(CBO) methods either rely on hard interventions that alter the causal structure to maximize the rewar
Externí odkaz:
http://arxiv.org/abs/2402.02277
To improve word representation learning, we propose a probabilistic prior which can be seamlessly integrated with word embedding models. Different from previous methods, word embedding is taken as a probabilistic generative model, and it enables us t
Externí odkaz:
http://arxiv.org/abs/2309.11824
This paper introduces a novel approach to embed flow-based models with hierarchical structures. The proposed framework is named Variational Flow Graphical (VFG) Model. VFGs learn the representation of high dimensional data via a message-passing schem
Externí odkaz:
http://arxiv.org/abs/2207.02722
Best subset selection is considered the `gold standard' for many sparse learning problems. A variety of optimization techniques have been proposed to attack this non-convex and NP-hard problem. In this paper, we investigate the dual forms of a family
Externí odkaz:
http://arxiv.org/abs/2207.02058
We present safe active incremental feature selection~(SAIF) to scale up the computation of LASSO solutions. SAIF does not require a solution from a heavier penalty parameter as in sequential screening or updating the full model for each iteration as
Externí odkaz:
http://arxiv.org/abs/1806.05817
Publikováno v:
In Robotics and Autonomous Systems April 2014 62(4):487-496
Publikováno v:
BMC Bioinformatics, Vol 14, Iss Suppl 2, p S17 (2013)
Abstract Background Optimization procedures to identify gene knockouts for targeted biochemical overproduction have been widely in use in modern metabolic engineering. Flux balance analysis (FBA) framework has provided conceptual simplifications for
Externí odkaz:
https://doaj.org/article/d4856e64c90e45ec83d9f5fef9061f65