Search Results for author: Yifan Wu

Found 29 papers, 6 papers with code

Mixture Proportion Estimation and PU Learning: A Modern Approach

1 code implementation NeurIPS 2021 Saurabh Garg, Yifan Wu, Alex Smola, Sivaraman Balakrishnan, Zachary C. Lipton

Formally, this task is broken down into two subtasks: (i) Mixture Proportion Estimation (MPE) -- determining the fraction of positive examples in the unlabeled data; and (ii) PU-learning -- given such an estimate, learning the desired positive-versus-negative classifier.

On the Fairness of Swarm Learning in Skin Lesion Classification

no code implementations24 Sep 2021 Di Fan, Yifan Wu, Xiaoxiao Li

Distributed and collaborative learning is an approach to involve training models in massive, heterogeneous, and distributed data sources, also known as nodes.

Classification Edge-computing +3

NODEO: A Neural Ordinary Differential Equation Based Optimization Framework for Deformable Image Registration

no code implementations CVPR 2022 Yifan Wu, Tom Z. Jiahao, Jiancong Wang, Paul A. Yushkevich, M. Ani Hsieh, James C. Gee

Deformable image registration (DIR), aiming to find spatial correspondence between images, is one of the most critical problems in the domain of medical image analysis.

Image Registration

A Pseudo Label-wise Attention Network for Automatic ICD Coding

no code implementations12 Jun 2021 Yifan Wu, Min Zeng, Ying Yu, Min Li

The label-wise attention mechanism is widely used in automatic ICD coding because it can assign weights to every word in full Electronic Medical Records (EMR) for different ICD codes.

Multi-Label Classification

Mixture Proportion Estimation and PU Learning:A Modern Approach

1 code implementation NeurIPS 2021 Saurabh Garg, Yifan Wu, Alex Smola, Sivaraman Balakrishnan, Zachary Chase Lipton

Formally, this task is broken down into two subtasks: (i) Mixture Proportion Estimation (MPE)---determining the fraction of positive examples in the unlabeled data; and (ii) PU-learning---given such an estimate, learning the desired positive-versus-negative classifier.

Unsupervised Learning of Multi-level Structures for Anomaly Detection

no code implementations25 Apr 2021 Songmin Dai, Jide Li, Lu Wang, Congcong Zhu, Yifan Wu, Xiaoqiang Li

This paper first introduces a novel method to generate anomalous data by breaking up global structures while preserving local structures of normal data at multiple levels.

Anomaly Detection

On the Optimality of Batch Policy Optimization Algorithms

no code implementations6 Apr 2021 Chenjun Xiao, Yifan Wu, Tor Lattimore, Bo Dai, Jincheng Mei, Lihong Li, Csaba Szepesvari, Dale Schuurmans

First, we introduce a class of confidence-adjusted index algorithms that unifies optimistic and pessimistic principles in a common framework, which enables a general analysis.

Value prediction

Instabilities of Offline RL with Pre-Trained Neural Representation

no code implementations8 Mar 2021 Ruosong Wang, Yifan Wu, Ruslan Salakhutdinov, Sham M. Kakade

In offline reinforcement learning (RL), we seek to utilize offline data to evaluate (or learn) policies in scenarios where the data are collected from a distribution that substantially differs from that of the target policy to be evaluated.

Offline RL

Estimating and Improving Fairness with Adversarial Learning

no code implementations7 Mar 2021 Xiaoxiao Li, Ziteng Cui, Yifan Wu, Lin Gu, Tatsuya Harada

To tackle this issue, we propose an adversarial multi-task training strategy to simultaneously mitigate and detect bias in the deep learning-based medical image analysis system.


BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions

1 code implementation29 Jan 2021 Yifan Wu, Min Gao, Min Zeng, Feiyang Chen, Min Li, Jie Zhang

Therefore, we hope to develop a novel supervised learning method to learn the PPAs and DDAs effectively and thereby improve the prediction performance of the specific task of DPI.

Drug Discovery

SSLIDE: Sound Source Localization for Indoors based on Deep Learning

no code implementations27 Oct 2020 Yifan Wu, Roshan Ayyalasomayajula, Michael J. Bianco, Dinesh Bharadia, Peter Gerstoft

This paper presents SSLIDE, Sound Source Localization for Indoors using DEep learning, which applies deep neural networks (DNNs) with encoder-decoder structure to localize sound sources with random positions in a continuous space.

Quantization in Relative Gradient Angle Domain For Building Polygon Estimation

no code implementations10 Jul 2020 Yuhao Chen, Yifan Wu, Linlin Xu, Alexander Wong

In this paper, we leverage the performance of CNNs, and propose a module that uses prior knowledge of building corners to create angular and concise building polygons from CNN segmentation outputs.


A Unified View of Label Shift Estimation

no code implementations NeurIPS 2020 Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary C. Lipton

Our contributions include (i) consistency conditions for MLLS, which include calibration of the classifier and a confusion matrix invertibility condition that BBSE also requires; (ii) a unified framework, casting BBSE as roughly equivalent to MLLS for a particular choice of calibration method; and (iii) a decomposition of MLLS's finite-sample error into terms reflecting miscalibration and estimation error.

Learning to Combat Compounding-Error in Model-Based Reinforcement Learning

no code implementations24 Dec 2019 Chenjun Xiao, Yifan Wu, Chen Ma, Dale Schuurmans, Martin Müller

Despite its potential to improve sample complexity versus model-free approaches, model-based reinforcement learning can fail catastrophically if the model is inaccurate.

Model-based Reinforcement Learning reinforcement-learning

Game Design for Eliciting Distinguishable Behavior

no code implementations NeurIPS 2019 Fan Yang, Liu Leqi, Yifan Wu, Zachary C. Lipton, Pradeep Ravikumar, William W. Cohen, Tom Mitchell

The ability to inferring latent psychological traits from human behavior is key to developing personalized human-interacting machine learning systems.

Behavior Regularized Offline Reinforcement Learning

1 code implementation26 Nov 2019 Yifan Wu, George Tucker, Ofir Nachum

In reinforcement learning (RL) research, it is common to assume access to direct online interactions with the environment.

Continuous Control Offline RL +1

Enhanced generative adversarial network for 3D brain MRI super-resolution

no code implementations10 Jul 2019 Jiancong Wang, Yu-Hua Chen, Yifan Wu, Jianbo Shi, James Gee

Single image super-resolution (SISR) reconstruction for magnetic resonance imaging (MRI) has generated significant interest because of its potential to not only speed up imaging but to improve quantitative processing and analysis of available image data.

Image Super-Resolution Single Image Super Resolution +1

Stochastic Learning of Additive Second-Order Penalties with Applications to Fairness

no code implementations ICLR 2019 Heinrich Jiang, Yifan Wu, Ofir Nachum

In non-convex settings, the resulting problem may be difficult to solve as the Lagrangian is not guaranteed to have a deterministic saddle-point equilibrium.


Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment

1 code implementation ICLR Workshop LLD 2019 Yifan Wu, Ezra Winston, Divyansh Kaushik, Zachary Lipton

Domain adaptation addresses the common problem when the target distribution generating our test data drifts from the source (training) distribution.

Domain Adaptation

Repetitive Motion Estimation Network: Recover cardiac and respiratory signal from thoracic imaging

no code implementations8 Nov 2018 Xiaoxiao Li, Vivek Singh, Yifan Wu, Klaus Kirchberg, James Duncan, Ankur Kapoor

Tracking organ motion is important in image-guided interventions, but motion annotations are not always easily available.

Motion Estimation

The Laplacian in RL: Learning Representations with Efficient Approximations

no code implementations ICLR 2019 Yifan Wu, George Tucker, Ofir Nachum

In this paper, we present a fully general and scalable method for approximating the eigenvectors of the Laplacian in a model-free RL context.

Representation Learning

Privacy-Protective-GAN for Face De-identification

no code implementations23 Jun 2018 Yifan Wu, Fan Yang, Haibin Ling

In this paper, we propose a new framework called Privacy-Protective-GAN (PP-GAN) that adapts GAN with novel verificator and regulator modules specially designed for the face de-identification problem to ensure generating de-identified output with retained structure similarity according to a single input.

De-identification Face Recognition

Generating Synthetic X-ray Images of a Person from the Surface Geometry

no code implementations CVPR 2018 Brian Teixeira, Vivek Singh, Terrence Chen, Kai Ma, Birgi Tamersoy, Yifan Wu, Elena Balashova, Dorin Comaniciu

Furthermore, the synthetic X-ray image is parametrized and can be manipulated by adjusting a set of body markers which are also generated during the X-ray image prediction.

Anomaly Detection

Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent

no code implementations13 Feb 2018 Yifan Wu, Barnabas Poczos, Aarti Singh

A major challenge in understanding the generalization of deep learning is to explain why (stochastic) gradient descent can exploit the network architecture to find solutions that have good generalization performance when using high capacity models.

Planar Object Tracking in the Wild: A Benchmark

no code implementations23 Mar 2017 Pengpeng Liang, Yifan Wu, Hu Lu, Liming Wang, Chunyuan Liao, Haibin Ling

In this paper, we present a carefully designed planar object tracking benchmark containing 210 videos of 30 planar objects sampled in the natural environment.

Homography Estimation Object Tracking

Conservative Bandits

no code implementations13 Feb 2016 Yifan Wu, Roshan Shariff, Tor Lattimore, Csaba Szepesvári

We consider both the stochastic and the adversarial settings, where we propose, natural, yet novel strategies and analyze the price for maintaining the constraints.

Online Learning with Gaussian Payoffs and Side Observations

no code implementations NeurIPS 2015 Yifan Wu, András György, Csaba Szepesvári

For the first time in the literature, we provide non-asymptotic problem-dependent lower bounds on the regret of any algorithm, which recover existing asymptotic problem-dependent lower bounds and finite-time minimax lower bounds available in the literature.

online learning

Cannot find the paper you are looking for? You can Submit a new open access paper.