Search Results for author: Shahin Boluki

Found 10 papers, 2 papers with code

Machine Learning based Framework for Robust Price-Sensitivity Estimation with Application to Airline Pricing

no code implementations4 May 2022 Ravi Kumar, Shahin Boluki, Karl Isler, Jonas Rauch, Darius Walczak

To address this concern, we propose a two-stage estimation methodology which makes the estimation of the price-sensitivity parameters robust to biases in the estimators of the nuisance parameters of the model.

VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition

no code implementations31 Mar 2022 Randy Ardywibowo, Shahin Boluki, Zhangyang Wang, Bobak Mortazavi, Shuai Huang, Xiaoning Qian

At its core is an implicit variational distribution on binary gates that are dependent on previous observations, which will select the next subset of features to observe.

Human Activity Recognition

SimCD: Simultaneous Clustering and Differential expression analysis for single-cell transcriptomic data

1 code implementation4 Apr 2021 Seyednami Niyakan, Ehsan Hajiramezanali, Shahin Boluki, Siamak Zamani Dadaneh, Xiaoning Qian

We develop a new method -- SimCD -- that explicitly models cell heterogeneity and dynamic differential changes in one unified hierarchical gamma-negative binomial (hGNB) model, allowing simultaneous cell clustering and differential expression analysis for scRNA-seq data.

Clustering

NADS: Neural Architecture Distribution Search for Uncertainty Awareness

no code implementations ICML 2020 Randy Ardywibowo, Shahin Boluki, Xinyu Gong, Zhangyang Wang, Xiaoning Qian

Machine learning (ML) systems often encounter Out-of-Distribution (OoD) errors when dealing with testing data coming from a distribution different from training data.

Out of Distribution (OOD) Detection

Bayesian Graph Neural Networks with Adaptive Connection Sampling

1 code implementation ICML 2020 Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki, Mingyuan Zhou, Nick Duffield, Krishna Narayanan, Xiaoning Qian

We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) that generalizes existing stochastic regularization methods for training GNNs.

Node Classification

Pairwise Supervised Hashing with Bernoulli Variational Auto-Encoder and Self-Control Gradient Estimator

no code implementations21 May 2020 Siamak Zamani Dadaneh, Shahin Boluki, Mingzhang Yin, Mingyuan Zhou, Xiaoning Qian

Semantic hashing has become a crucial component of fast similarity search in many large-scale information retrieval systems, in particular, for text data.

Information Retrieval Retrieval

Learnable Bernoulli Dropout for Bayesian Deep Learning

no code implementations12 Feb 2020 Shahin Boluki, Randy Ardywibowo, Siamak Zamani Dadaneh, Mingyuan Zhou, Xiaoning Qian

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.

Collaborative Filtering Deep Learning +3

ARSM Gradient Estimator for Supervised Learning to Rank

no code implementations1 Nov 2019 Siamak Zamani Dadaneh, Shahin Boluki, Mingyuan Zhou, Xiaoning Qian

Learning-to-rank methods can generally be categorized into pointwise, pairwise, and listwise approaches.

Learning-To-Rank

Optimal Clustering with Missing Values

no code implementations26 Feb 2019 Shahin Boluki, Siamak Zamani Dadaneh, Xiaoning Qian, Edward R. Dougherty

Missing values frequently arise in modern biomedical studies due to various reasons, including missing tests or complex profiling technologies for different omics measurements.

Clustering Imputation +1

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