Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Boluki, Shahin"'
We consider the problem of dynamic pricing of a product in the presence of feature-dependent price sensitivity. Developing practical algorithms that can estimate price elasticities robustly, especially when information about no purchases (losses) is
Externí odkaz:
http://arxiv.org/abs/2205.01875
Autor:
Ardywibowo, Randy, Boluki, Shahin, Wang, Zhangyang, Mortazavi, Bobak, Huang, Shuai, Qian, Xiaoning
In many machine learning tasks, input features with varying degrees of predictive capability are acquired at varying costs. In order to optimize the performance-cost trade-off, one would select features to observe a priori. However, given the changin
Externí odkaz:
http://arxiv.org/abs/2204.00130
Autor:
Niyakan, Seyednami, Hajiramezanali, Ehsan, Boluki, Shahin, Dadaneh, Siamak Zamani, Qian, Xiaoning
Single-Cell RNA sequencing (scRNA-seq) measurements have facilitated genome-scale transcriptomic profiling of individual cells, with the hope of deconvolving cellular dynamic changes in corresponding cell sub-populations to better understand molecula
Externí odkaz:
http://arxiv.org/abs/2104.01512
Machine learning (ML) systems often encounter Out-of-Distribution (OoD) errors when dealing with testing data coming from a distribution different from training data. It becomes important for ML systems in critical applications to accurately quantify
Externí odkaz:
http://arxiv.org/abs/2006.06646
Autor:
Hasanzadeh, Arman, Hajiramezanali, Ehsan, Boluki, Shahin, Zhou, Mingyuan, Duffield, Nick, Narayanan, Krishna, Qian, Xiaoning
We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) that generalizes existing stochastic regularization methods for training GNNs. The proposed framework not only alleviates over-smoothing and over-fitting
Externí odkaz:
http://arxiv.org/abs/2006.04064
Publikováno v:
Uncertainty in Artificial Intelligence Conference (UAI) 2020
Semantic hashing has become a crucial component of fast similarity search in many large-scale information retrieval systems, in particular, for text data. Variational auto-encoders (VAEs) with binary latent variables as hashing codes provide state-of
Externí odkaz:
http://arxiv.org/abs/2005.10477
In this work, we propose learnable Bernoulli dropout (LBD), a new model-agnostic dropout scheme that considers the dropout rates as parameters jointly optimized with other model parameters. By probabilistic modeling of Bernoulli dropout, our method e
Externí odkaz:
http://arxiv.org/abs/2002.05155
We propose a new model for supervised learning to rank. In our model, the relevance labels are assumed to follow a categorical distribution whose probabilities are constructed based on a scoring function. We optimize the training objective with respe
Externí odkaz:
http://arxiv.org/abs/1911.00465
Missing values frequently arise in modern biomedical studies due to various reasons, including missing tests or complex profiling technologies for different omics measurements. Missing values can complicate the application of clustering algorithms, w
Externí odkaz:
http://arxiv.org/abs/1902.09694
Autor:
Solomou, Alexandros, Zhao, Guang, Boluki, Shahin, Joy, Jobin K., Qian, Xiaoning, Karaman, Ibrahim, Arróyave, Raymundo, Lagoudas, Dimitris C.
In this study, a framework for the multi-objective materials discovery based on Bayesian approaches is developed. The capabilities of the framework are demonstrated on an example case related to the discovery of precipitation strengthened NiTi shape
Externí odkaz:
http://arxiv.org/abs/1807.06868