no code implementations • 10 Feb 2025 • Sanket Jantre, Tianle Wang, Gilchan Park, Kriti Chopra, Nicholas Jeon, Xiaoning Qian, Nathan M. Urban, Byung-Jun Yoon
Identification of protein-protein interactions (PPIs) helps derive cellular mechanistic understanding, particularly in the context of complex conditions such as neurodegenerative disorders, metabolic syndromes, and cancer.
no code implementations • 10 Dec 2024 • Puhua Niu, Byung-Jun Yoon, Xiaoning Qian
Therefore, we introduce model calibration methods based on a "graybox" Bayesian optimization (BO) scheme, more efficient calibration for general epidemiological models.
no code implementations • 4 Dec 2024 • Meltem Apaydin Ustun, Liang Xu, Bo Zeng, Xiaoning Qian
Most of the existing bilevel optimization solutions either assume the uniqueness of the optimal training model given hyperparameters or adopt an optimistic view when the non-uniqueness issue emerges.
1 code implementation • 4 Dec 2024 • Mingzhou Fan, Ruida Zhou, Chao Tian, Xiaoning Qian
We propose a path-guided particle-based sampling~(PGPS) method based on a novel Log-weighted Shrinkage (LwS) density path linking an initial distribution to the target distribution.
no code implementations • 11 Nov 2024 • Nicholas Jeon, Xiaoning Qian, Lamin SaidyKhan, Paul de Figueiredo, Byung-Jun Yoon
The emergence of next-generation sequencing technologies has greatly advanced the detection and identification of lncRNA transcripts and deep learning-based approaches have been introduced to classify long non-coding RNAs (lncRNAs).
1 code implementation • 25 Sep 2024 • Alif Bin Abdul Qayyum, Xihaier Luo, Nathan M. Urban, Xiaoning Qian, Byung-Jun Yoon
The world is moving towards clean and renewable energy sources, such as wind energy, in an attempt to reduce greenhouse gas emissions that contribute to global warming.
no code implementations • 24 Aug 2024 • Amir Hossein Rahmati, Mingzhou Fan, Ruida Zhou, Nathan M. Urban, Byung-Jun Yoon, Xiaoning Qian
Instead of randomly acquiring training data points, Uncertainty-based Active Learning (UAL) operates by querying the label(s) of pivotal samples from an unlabeled pool selected based on the prediction uncertainty, thereby aiming at minimizing the labeling cost for model training.
1 code implementation • 12 Aug 2024 • Puhua Niu, Shili Wu, Mingzhou Fan, Xiaoning Qian
Generative Flow Networks (GFlowNets) have been shown effective to generate combinatorial objects with desired properties.
1 code implementation • 14 Jun 2024 • Ziyi Zhang, Shaogang Ren, Xiaoning Qian, Nick Duffield
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 prediction models.
1 code implementation • 3 Jun 2024 • Keqiang Yan, Alexandra Saxton, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji
A key challenge here is how to make the predictions satisfy the unique tensor equivariance to O(3) group and invariance to crystal space groups.
no code implementations • 20 May 2024 • Xihaier Luo, Xiaoning Qian, Byung-Jun Yoon
Grounded in operator learning, the proposed method is resolution-invariant.
no code implementations • 8 May 2024 • Ziyi Zhang, Shaogang Ren, Xiaoning Qian, Nick Duffield
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.
1 code implementation • 18 Mar 2024 • Keqiang Yan, Cong Fu, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji
Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space.
Ranked #2 on
ADP Prediction
on ADP Dataset
no code implementations • 4 Feb 2024 • Shaogang Ren, Xiaoning Qian
Best subset selection is considered the `gold standard' for many sparse learning problems.
no code implementations • 3 Feb 2024 • Shaogang Ren, Xiaoning Qian
We develop a new CBO method by leveraging the learned exogenous distribution.
no code implementations • 30 Jan 2024 • Alif Bin Abdul Qayyum, Xihaier Luo, Nathan M. Urban, Xiaoning Qian, Byung-Jun Yoon
World is looking for clean and renewable energy sources that do not pollute the environment, in an attempt to reduce greenhouse gas emissions that contribute to global warming.
no code implementations • 6 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.
1 code implementation • 17 Jul 2023 • Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik Bekkers, Michael Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, Shuiwang Ji
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
1 code implementation • NeurIPS 2023 • Haiyang Yu, Meng Liu, Youzhi Luo, Alex Strasser, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji
Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT).
1 code implementation • 12 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.
Ranked #2 on
Formation Energy
on JARVIS-DFT
1 code implementation • 8 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.
1 code implementation • 6 May 2023 • Cong Fu, Keqiang Yan, Limei Wang, Wing Yee Au, Michael McThrow, Tao Komikado, Koji Maruhashi, Kanji Uchino, Xiaoning Qian, Shuiwang Ji
Proteins are complex biomolecules that perform a variety of crucial functions within living organisms.
1 code implementation • 3 Jan 2023 • Xihaier Luo, Sean McCorkle, Gilchan Park, Vanessa Lopez-Marrero, Shinjae Yoo, Edward R. Dougherty, Xiaoning Qian, Francis J. Alexander, Byung-Jun Yoon
There are various sources of ionizing radiation exposure, where medical exposure for radiation therapy or diagnosis is the most common human-made source.
no code implementations • 26 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.
no code implementations • 23 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.
no code implementations • 31 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.
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.
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.
1 code implementation • 11 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.
no code implementations • 23 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.
no code implementations • 5 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.
1 code implementation • 4 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.
no code implementations • 26 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.
1 code implementation • ICLR 2021 • Xinjie Fan, Shujian Zhang, Korawat Tanwisuth, Xiaoning Qian, Mingyuan Zhou
However, the quality of uncertainty estimation is highly dependent on the dropout probabilities.
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.
no code implementations • 1 Jan 2021 • Randy Ardywibowo, Shahin Boluki, Zhangyang Wang, Bobak J Mortazavi, Shuai Huang, Xiaoning Qian
In many machine learning tasks, input features with varying degrees of predictive capability are usually acquired at some cost.
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.
no code implementations • 7 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.
1 code implementation • 11 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.
Methodology
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.
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.
no code implementations • 21 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.
no code implementations • 3 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.
no code implementations • 12 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.
no code implementations • 1 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.
no code implementations • 28 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.
2 code implementations • NeurIPS 2019 • Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna R. Narayanan, Mingyuan Zhou, Xiaoning Qian
Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dynamic graphs are still scant.
Ranked #2 on
Dynamic Link Prediction
on Enron Emails
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.
1 code implementation • CVPR 2019 • Wuyang Chen, Ziyu Jiang, Zhangyang Wang, Kexin Cui, Xiaoning Qian
In either way, the loss of local fine details or global contextual information results in limited segmentation accuracy.
Ranked #4 on
Land Cover Classification
on DeepGlobe
no code implementations • 26 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.
no code implementations • 8 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.
no code implementations • 18 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.
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.
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).
no code implementations • 15 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.
no code implementations • 7 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.
no code implementations • 2 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.
no code implementations • 12 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.
no code implementations • 12 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.