Search Results for author: Xiaoqian Wang

Found 21 papers, 5 papers with code

Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch

1 code implementation21 Mar 2024 Xidong Wu, Shangqian Gao, Zeyu Zhang, Zhenzhen Li, Runxue Bao, yanfu Zhang, Xiaoqian Wang, Heng Huang

Current techniques for deep neural network (DNN) pruning often involve intricate multi-step processes that require domain-specific expertise, making their widespread adoption challenging.

Network Pruning

Learning the irreversible progression trajectory of Alzheimer's disease

no code implementations10 Mar 2024 Yipei Wang, Bing He, Shannon Risacher, Andrew Saykin, Jingwen Yan, Xiaoqian Wang

Specifically, we introduce a monotonicity constraint that encourages the model to predict disease risk in a consistent and ordered manner across follow-up visits.

SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification

no code implementations19 Feb 2023 Tianci Liu, Haoyu Wang, Yaqing Wang, Xiaoqian Wang, Lu Su, Jing Gao

This new framework utilizes data that have similar labels when estimating fairness on a particular label group for better stability, and can unify DP and EOp.

Classification Fairness +1

Forecast combinations: an over 50-year review

no code implementations9 May 2022 Xiaoqian Wang, Rob J Hyndman, Feng Li, Yanfei Kang

Forecast combinations have flourished remarkably in the forecasting community and, in recent years, have become part of the mainstream of forecasting research and activities.

A Unified Study of Machine Learning Explanation Evaluation Metrics

no code implementations27 Mar 2022 Yipei Wang, Xiaoqian Wang

The growing need for trustworthy machine learning has led to the blossom of interpretability research.

Benchmarking BIG-bench Machine Learning

Self-Interpretable Model with Transformation Equivariant Interpretation

no code implementations NeurIPS 2021 Yipei Wang, Xiaoqian Wang

With the proliferation of machine learning applications in the real world, the demand for explaining machine learning predictions continues to grow especially in high-stakes fields.

BIG-bench Machine Learning valid

Self-Interpretable Model with TransformationEquivariant Interpretation

no code implementations9 Nov 2021 Yipei Wang, Xiaoqian Wang

In this paper, we propose a self-interpretable model SITE with transformation-equivariant interpretations.

Open-Ended Question Answering

Group-Aware Threshold Adaptation for Fair Classification

no code implementations NeurIPS 2021 Taeuk Jang, Pengyi Shi, Xiaoqian Wang

As we only need an estimated probability distribution of model output instead of the classification model structure, our post-processing model can be applied to a wide range of classification models and improve fairness in a model-agnostic manner and ensure privacy.

Classification Fairness

Adversarial Fairness Network

no code implementations29 Sep 2021 Taeuk Jang, Xiaoqian Wang, Heng Huang

To achieve this goal, we reformulate the data input by eliminating the sensitive information and strengthen model fairness by minimizing the marginal contribution of the sensitive feature.

BIG-bench Machine Learning Fairness

Shapley Explanation Networks

2 code implementations ICLR 2021 Rui Wang, Xiaoqian Wang, David I. Inouye

This intrinsic explanation approach enables layer-wise explanations, explanation regularization of the model during training, and fast explanation computation at test time.

Parallel Extraction of Long-term Trends and Short-term Fluctuation Framework for Multivariate Time Series Forecasting

no code implementations18 Aug 2020 Yifu Zhou, Ziheng Duan, Haoyan Xu, Jie Feng, Anni Ren, Yueyang Wang, Xiaoqian Wang

In this paper, a MTS forecasting framework that can capture the long-term trends and short-term fluctuations of time series in parallel is proposed.

Decision Making Multi-Task Learning +2

Distributed ARIMA Models for Ultra-long Time Series

1 code implementation19 Jul 2020 Xiaoqian Wang, Yanfei Kang, Rob J. Hyndman, Feng Li

Providing forecasts for ultra-long time series plays a vital role in various activities, such as investment decisions, industrial production arrangements, and farm management.

Applications Computation

Approaching Machine Learning Fairness through Adversarial Network

no code implementations6 Sep 2019 Xiaoqian Wang, Heng Huang

In order to achieve this goal, we reformulate the data input by removing the sensitive information and strengthen model fairness by minimizing the marginal contribution of the sensitive feature.

BIG-bench Machine Learning Fairness

Que será será? The uncertainty estimation of feature-based time series forecasts

2 code implementations8 Aug 2019 Xiaoqian Wang, Yanfei Kang, Fotios Petropoulos, Feng Li

In the training part, we use a collection of time series to train a model to explore how time series features affect the interval forecasting accuracy of different forecasting methods, which makes our proposed framework interpretable in terms of the contribution of each feature to the models' uncertainty prediction.

Methodology Applications Computation

An Iteratively Re-weighted Method for Problems with Sparsity-Inducing Norms

no code implementations2 Jul 2019 Feiping Nie, Zhanxuan Hu, Xiaoqian Wang, Rong Wang, Xuelong. Li, Heng Huang

This work aims at solving the problems with intractable sparsity-inducing norms that are often encountered in various machine learning tasks, such as multi-task learning, subspace clustering, feature selection, robust principal component analysis, and so on.

BIG-bench Machine Learning Clustering +2

Group Sparse Additive Machine

no code implementations NeurIPS 2017 Hong Chen, Xiaoqian Wang, Cheng Deng, Heng Huang

Among them, learning models with grouped variables have shown competitive performance for prediction and variable selection.

Additive models Classification +2

Learning A Structured Optimal Bipartite Graph for Co-Clustering

no code implementations NeurIPS 2017 Feiping Nie, Xiaoqian Wang, Cheng Deng, Heng Huang

In graph based co-clustering methods, a bipartite graph is constructed to depict the relation between features and samples.

Clustering

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