Search Results for author: Xiaoning Qian

Found 42 papers, 13 papers with code

Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks

no code implementations6 Sep 2023 Sanket Jantre, Nathan M. Urban, Xiaoning Qian, Byung-Jun Yoon

Bayesian inference for neural networks, or Bayesian deep learning, has the potential to provide well-calibrated predictions with quantified uncertainty and robustness.

Bayesian Inference Variational Inference

Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction

1 code implementation12 Jun 2023 Yuchao Lin, Keqiang Yan, Youzhi Luo, Yi Liu, Xiaoning Qian, Shuiwang Ji

This is enabled by our approximations of infinite potential summations, where we extend the Ewald summation for several potential series approximations with provable error bounds.

Band Gap Formation Energy +2

Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian

1 code implementation8 Jun 2023 Haiyang Yu, Zhao Xu, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji

We consider the prediction of the Hamiltonian matrix, which finds use in quantum chemistry and condensed matter physics.

DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness

no code implementations26 Sep 2022 Zepeng Huo, Taowei Ji, Yifei Liang, Shuai Huang, Zhangyang Wang, Xiaoning Qian, Bobak Mortazavi

We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors.

Activity Recognition Denoising +3

Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data

no code implementations23 Jul 2022 Zepeng Huo, Xiaoning Qian, Shuai Huang, Zhangyang Wang, Bobak J. Mortazavi

Medical events of interest, such as mortality, often happen at a low rate in electronic medical records, as most admitted patients survive.

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

MoReL: Multi-omics Relational Learning

no code implementations ICLR 2022 Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield, Xiaoning Qian

Multi-omics data analysis has the potential to discover hidden molecular interactions, revealing potential regulatory and/or signal transduction pathways for cellular processes of interest when studying life and disease systems.

Graph Embedding Relational Reasoning

Efficient Active Learning for Gaussian Process Classification by Error Reduction

no code implementations NeurIPS 2021 Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, Xiaoning Qian

Moreover, as the EER is not smooth, it can not be combined with gradient-based optimization techniques to efficiently explore the continuous instance space for query synthesis.

Active Learning Classification

COVID-Datathon: Biomarker identification for COVID-19 severity based on BALF scRNA-seq data

1 code implementation11 Oct 2021 Seyednami Niyakan, Xiaoning Qian

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emergence began in late 2019 and has since spread rapidly worldwide.

Optimal Decision Making in High-Throughput Virtual Screening Pipelines

no code implementations23 Sep 2021 Hyun-Myung Woo, Xiaoning Qian, Li Tan, Shantenu Jha, Francis J. Alexander, Edward R. Dougherty, Byung-Jun Yoon

The need for efficient computational screening of molecular candidates that possess desired properties frequently arises in various scientific and engineering problems, including drug discovery and materials design.

Decision Making Drug Discovery +2

Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning

no code implementations5 Sep 2021 Omar Maddouri, Xiaoning Qian, Francis J. Alexander, Edward R. Dougherty, Byung-Jun Yoon

In this paper, we fill this gap by investigating knowledge transferability in the context of classification error estimation within a Bayesian paradigm.

Classification Decision Making +2

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.


Geometric Affinity Propagation for Clustering with Network Knowledge

no code implementations26 Mar 2021 Omar Maddouri, Xiaoning Qian, Byung-Jun Yoon

This interest primarily stems from the amount of compressed information encoded in these exemplars that effectively reflect the major characteristics of the respective clusters.


Uncertainty-aware Active Learning for Optimal Bayesian Classifier

no code implementations ICLR 2021 Guang Zhao, Edward Dougherty, Byung-Jun Yoon, Francis Alexander, Xiaoning Qian

For pool-based active learning, in each iteration a candidate training sample is chosen for labeling by optimizing an acquisition function.

Active Learning Classification +1

BayReL: Bayesian Relational Learning for Multi-omics Data Integration

1 code implementation NeurIPS 2020 Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna R Narayanan, Xiaoning Qian

High-throughput molecular profiling technologies have produced high-dimensional multi-omics data, enabling systematic understanding of living systems at the genome scale.

Data Integration Relational Reasoning +1

Quantifying the multi-objective cost of uncertainty

no code implementations7 Oct 2020 Byung-Jun Yoon, Xiaoning Qian, Edward R. Dougherty

Various real-world applications involve modeling complex systems with immense uncertainty and optimizing multiple objectives based on the uncertain model.

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

Probabilistic Best Subset Selection via Gradient-Based Optimization

1 code implementation11 Jun 2020 Mingzhang Yin, Nhat Ho, Bowei Yan, Xiaoning Qian, Mingyuan Zhou

This paper proposes a novel optimization method to solve the exact L0-regularized regression problem, which is also known as the best subset selection.


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

Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery

no code implementations3 Mar 2020 Zepeng Huo, Arash Pakbin, Xiaohan Chen, Nathan Hurley, Ye Yuan, Xiaoning Qian, Zhangyang Wang, Shuai Huang, Bobak Mortazavi

Activity recognition in wearable computing faces two key challenges: i) activity characteristics may be context-dependent and change under different contexts or situations; ii) unknown contexts and activities may occur from time to time, requiring flexibility and adaptability of the algorithm.

Clustering Human Activity Recognition

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 Image Classification +1

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.


Semi-Implicit Stochastic Recurrent Neural Networks

no code implementations28 Oct 2019 Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna Narayanan, Mingyuan Zhou, Xiaoning Qian

Stochastic recurrent neural networks with latent random variables of complex dependency structures have shown to be more successful in modeling sequential data than deterministic deep models.

Variational Inference

Semi-Implicit Graph Variational Auto-Encoders

1 code implementation NeurIPS 2019 Arman Hasanzadeh, Ehsan Hajiramezanali, Nick Duffield, Krishna R. Narayanan, Mingyuan Zhou, Xiaoning Qian

Compared to VGAE, the derived graph latent representations by SIG-VAE are more interpretable, due to more expressive generative model and more faithful inference enabled by the flexible semi-implicit construction.

Variational Inference

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

Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models

no code implementations8 Jan 2019 Randy Ardywibowo, Guang Zhao, Zhangyang Wang, Bobak Mortazavi, Shuai Huang, Xiaoning Qian

This power-efficient sensing scheme can be achieved by deciding which group of sensors to use at a given time, requiring an accurate characterization of the trade-off between sensor energy usage and the uncertainty in ignoring certain sensor signals while monitoring.

Gaussian Processes Human Activity Recognition

Fast Exact Computation of Expected HyperVolume Improvement

no code implementations18 Dec 2018 Guang Zhao, Raymundo Arroyave, Xiaoning Qian

The first grid-based algorithm has a complexity of $O(m\cdot n^m)$ with $n$ denoting the size of the nondominated set and $m$ the number of objectives.

Bayesian Optimization Evolutionary Algorithms

Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data

no code implementations NeurIPS 2018 Ehsan Hajiramezanali, Siamak Zamani Dadaneh, Alireza Karbalayghareh, Mingyuan Zhou, Xiaoning Qian

Second, compared to the number of involved molecules and system complexity, the number of available samples for studying complex disease, such as cancer, is often limited, especially considering disease heterogeneity.

Multi-Task Learning

Unsupervised CNN-based Co-Saliency Detection with Graphical Optimization

no code implementations ECCV 2018 Kuang-Jui Hsu, Chung-Chi Tsai, Yen-Yu Lin, Xiaoning Qian, Yung-Yu Chuang

In this paper, we address co-saliency detection in a set of images jointly covering objects of a specific class by an unsupervised convolutional neural network (CNN).

Co-Salient Object 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

Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chain

no code implementations7 Mar 2018 Ehsan Hajiramezanali, Siamak Zamani Dadaneh, Paul de Figueiredo, Sing-Hoi Sze, Mingyuan Zhou, Xiaoning Qian

Next-generation sequencing (NGS) to profile temporal changes in living systems is gaining more attention for deriving better insights into the underlying biological mechanisms compared to traditional static sequencing experiments.

Data Augmentation

Optimal Bayesian Transfer Learning

no code implementations2 Jan 2018 Alireza Karbalayghareh, Xiaoning Qian, Edward R. Dougherty

Transfer learning has recently attracted significant research attention, as it simultaneously learns from different source domains, which have plenty of labeled data, and transfers the relevant knowledge to the target domain with limited labeled data to improve the prediction performance.

Domain Adaptation Transfer Learning

Prognostics of Surgical Site Infections using Dynamic Health Data

no code implementations12 Nov 2016 Chuyang Ke, Yan Jin, Heather Evans, Bill Lober, Xiaoning Qian, Ji Liu, Shuai Huang

Since existing prediction models of SSI have quite limited capacity to utilize the evolving clinical data, we develop the corresponding solution to equip these mHealth tools with decision-making capabilities for SSI prediction with a seamless assembly of several machine learning models to tackle the analytic challenges arising from the spatial-temporal data.

Decision Making Imputation +1

Detection of Cooperative Interactions in Logistic Regression Models

no code implementations12 Feb 2016 Easton Li Xu, Xiaoning Qian, Tie Liu, Shuguang Cui

For the case when the underlying interaction graph is known to be acyclic, it is shown that a simple algorithm that is based on a maximum-weight spanning tree with respect to the plug-in estimates of the influences not only has strong theoretical performance guarantees, but can also outperform generic feature selection algorithms for recovering the interaction graph from i. i. d.

feature selection regression

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