726 citations · 2.2k across the 66 of their papers we have counts for
38 papers · 1 filter
Learning by Grouping: A Multilevel Optimization Framework for Improving Fairness in Classification without Losing Accuracy
Ramtin Hosseini, Li Zhang, Bhanu Garg +1
The integration of machine learning models in various real-world applications is becoming more prevalent to assist humans in their daily decision-making tasks as a result of recent…
Betty: An Automatic Differentiation Library for Multilevel Optimization
Sang Keun Choe, Willie Neiswanger, Pengtao Xie +1
Gradient-based multilevel optimization (MLO) has gained attention as a framework for studying numerous problems, ranging from hyperparameter optimization and meta-learning to neura…
Self-directed Machine Learning
Wenwu Zhu, Xin Wang, Pengtao Xie
Conventional machine learning (ML) relies heavily on manual design from machine learning experts to decide learning tasks, data, models, optimization algorithms, and evaluation met…
Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search
Jay Gala, Pengtao Xie
Learning from mistakes is an effective learning approach widely used in human learning, where a learner pays greater focus on mistakes to circumvent them in the future to improve t…
Improving Differentiable Architecture Search with a Generative Model
Ruisi Zhang, Youwei Liang, Sai Ashish Somayajula +1
In differentiable neural architecture search (NAS) algorithms like DARTS, the training set used to update model weight and the validation set used to update model architectures are…
Learning from Mistakes -- A Framework for Neural Architecture Search
Bhanu Garg, Li Zhang, Pradyumna Sridhara +3
Learning from one's mistakes is an effective human learning technique where the learners focus more on the topics where mistakes were made, so as to deepen their understanding. In…
Learning by Examples Based on Multi-level Optimization
Shentong Mo, Pengtao Xie
Learning by examples, which learns to solve a new problem by looking into how similar problems are solved, is an effective learning method in human learning. When a student learns…
Interleaving Learning, with Application to Neural Architecture Search
Hao Ban, Pengtao Xie
Interleaving learning is a human learning technique where a learner interleaves the studies of multiple topics, which increases long-term retention and improves ability to transfer…
Learning by Teaching, with Application to Neural Architecture Search
Parth Sheth, Yueyu Jiang, Pengtao Xie
In human learning, an effective skill in improving learning outcomes is learning by teaching: a learner deepens his/her understanding of a topic by teaching this topic to others. I…
DSRNA: Differentiable Search of Robust Neural Architectures
Ramtin Hosseini, Xingyi Yang, Pengtao Xie
In deep learning applications, the architectures of deep neural networks are crucial in achieving high accuracy. Many methods have been proposed to search for high-performance neur…
Learning by Ignoring, with Application to Domain Adaptation
Xingchen Zhao, Xuehai He, Pengtao Xie
Learning by ignoring, which identifies less important things and excludes them from the learning process, is broadly practiced in human learning and has shown ubiquitous effectiven…
Learning by Self-Explanation, with Application to Neural Architecture Search
Ramtin Hosseini, Pengtao Xie
Learning by self-explanation is an effective learning technique in human learning, where students explain a learned topic to themselves for deepening their understanding of this to…
Small-Group Learning, with Application to Neural Architecture Search
Xuefeng Du, Pengtao Xie
In human learning, an effective learning methodology is small-group learning: a small group of students work together towards the same learning objective, where they express their…
Validate and Enable Machine Learning in Industrial AI
Hongbo Zou, Guangjing Chen, Pengtao Xie +10
Industrial Artificial Intelligence (Industrial AI) is an emerging concept which refers to the application of artificial intelligence to industry. Industrial AI promises more effici…
Skillearn: Machine Learning Inspired by Humans' Learning Skills
Pengtao Xie, Xuefeng Du, Hao Ban
Humans, as the most powerful learners on the planet, have accumulated a lot of learning skills, such as learning through tests, interleaving learning, self-explanation, active reca…
Learning by Passing Tests, with Application to Neural Architecture Search
Xuefeng Du, Haochen Zhang, Pengtao Xie
Learning through tests is a broadly used methodology in human learning and shows great effectiveness in improving learning outcome: a sequence of tests are made with increasing lev…
Contrastive Self-supervised Learning for Graph Classification
Jiaqi Zeng, Pengtao Xie
Graph classification is a widely studied problem and has broad applications. In many real-world problems, the number of labeled graphs available for training classification models…
Differentially-private Federated Neural Architecture Search
Ishika Singh, Haoyi Zhou, Kunlin Yang +3
Neural architecture search, which aims to automatically search for architectures (e.g., convolution, max pooling) of neural networks that maximize validation performance, has achie…
MedDialog: Two Large-scale Medical Dialogue Datasets
Xuehai He, Shu Chen, Zeqian Ju +10
Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs. To fac…
COVID-CT-Dataset: A CT Scan Dataset about COVID-19
Xingyi Yang, Xuehai He, Jinyu Zhao +3
During the outbreak time of COVID-19, computed tomography (CT) is a useful manner for diagnosing COVID-19 patients. Due to privacy issues, publicly available COVID-19 CT datasets a…
Adversarial Domain Adaptation Being Aware of Class Relationships
Zeya Wang, Baoyu Jing, Yang Ni +3
Adversarial training is a useful approach to promote the learning of transferable representations across the source and target domains, which has been widely applied for domain ada…
Explaining a black-box using Deep Variational Information Bottleneck Approach
Seojin Bang, Pengtao Xie, Heewook Lee +2
Interpretable machine learning has gained much attention recently. Briefness and comprehensiveness are necessary in order to provide a large amount of information concisely when ex…
Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures
Hongyang Zhang, Susu Xu, Jiantao Jiao +3
We study the problem of alleviating the instability issue in the GAN training procedure via new architecture design. The discrepancy between the minimax and maximin objective value…
Multimodal Machine Learning for Automated ICD Coding
Keyang Xu, Mike Lam, Jingzhi Pang +9
This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modali…