b'Zhi-Hua Zhou'

research

∙ 05/03/2023

Stream Efficient Learning

Data in many real-world applications are often accumulated over time, li...

0 Zhi-Hua Zhou, et al. ∙

research

∙ 03/05/2023

Revisiting Weighted Strategy for Non-stationary Parametric Bandits

Non-stationary parametric bandits have attracted much attention recently...

0 Jing Wang, et al. ∙

research

∙ 02/18/2023

Stochastic Approximation Approaches to Group Distributionally Robust Optimization

This paper investigates group distributionally robust optimization (GDRO...

0 Lijun Zhang, et al. ∙

research

∙ 10/07/2022

Learnware: Small Models Do Big

There are complaints about current machine learning techniques such as t...

0 Zhi-Hua Zhou, et al. ∙

research

∙ 08/26/2022

Dynamic Regret of Online Markov Decision Processes

We investigate online Markov Decision Processes (MDPs) with adversariall...

1 Peng Zhao, et al. ∙

research

∙ 07/05/2022

Adapting to Online Label Shift with Provable Guarantees

The standard supervised learning paradigm works effectively when trainin...

3 Yong Bai, et al. ∙

research

∙ 06/01/2022

Open Environment Machine Learning

Conventional machine learning studies generally assume close world scena...

0 Zhi-Hua Zhou, et al. ∙

research

∙ 02/12/2022

Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits

We consider the problem of combining and learning over a set of adversar...

0 Haipeng Luo, et al. ∙

research

∙ 01/30/2022

No-Regret Learning in Time-Varying Zero-Sum Games

Learning from repeated play in a fixed two-player zero-sum game is a cla...

8 Mengxiao Zhang, et al. ∙

research

∙ 12/29/2021

Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization

We investigate online convex optimization in non-stationary environments...

7 Peng Zhao, et al. ∙

research

∙ 11/11/2021

Towards Theoretical Understanding of Flexible Transmitter Networks via Approximation and Local Minima

Flexible Transmitter Network (FTNet) is a recently proposed bio-plausibl...

4 Jin-Hui Wu, et al. ∙

research

∙ 11/08/2021

ARISE: ApeRIodic SEmi-parametric Process for Efficient Markets without Periodogram and Gaussianity Assumptions

Mimicking and learning the long-term memory of efficient markets is a fu...

5 Shao-Qun Zhang, et al. ∙

research

∙ 10/18/2021

Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees

Given a ground set of items, the result diversification problem aims to ...

4 Chao Qian, et al. ∙

research

∙ 09/30/2021

LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values

Multivariate time series (MTS) prediction is ubiquitous in real-world fi...

4 Zhao-Yu Zhang, et al. ∙

research

∙ 08/15/2021

Towards Understanding Theoretical Advantages of Complex-Reaction Networks

Complex-valued neural networks have attracted increasing attention in re...

10 Shao-Qun Zhang, et al. ∙

research

∙ 06/17/2021

Seeing Differently, Acting Similarly: Imitation Learning with Heterogeneous Observations

In many real-world imitation learning tasks, the demonstrator and the le...

3 Xin-Qiang Cai, et al. ∙

research

∙ 06/06/2021

Towards an Understanding of Benign Overfitting in Neural Networks

Modern machine learning models often employ a huge number of parameters ...

15 Zhu Li, et al. ∙

research

∙ 02/07/2021

Non-stationary Online Learning with Memory and Non-stochastic Control

We study the problem of Online Convex Optimization (OCO) with memory, wh...

0 Peng Zhao, et al. ∙

research

∙ 09/24/2020

Isolation Distributional Kernel: A New Tool for Point Group Anomaly Detection

We introduce Isolation Distributional Kernel as a new way to measure the...

4 Kai Ming Ting, et al. ∙

research

∙ 07/22/2020

Storage Fit Learning with Feature Evolvable Streams

Feature evolvable learning has been widely studied in recent years where...

0 Bo-Jian Hou, et al. ∙

research

∙ 07/07/2020

Dynamic Regret of Convex and Smooth Functions

We investigate online convex optimization in non-stationary environments...

0 Peng Zhao, et al. ∙

research

∙ 06/07/2020

Soft Gradient Boosting Machine

Gradient Boosting Machine has proven to be one successful function appro...

0 Ji Feng, et al. ∙

research

∙ 04/08/2020

Flexible Transmitter Network

Current neural networks are mostly built upon the MP model, which usuall...

1 Shao-Qun Zhang, et al. ∙

research

∙ 02/05/2020

Exploratory Machine Learning with Unknown Unknowns

In conventional supervised learning, a training dataset is given with gr...

72 Yu-Jie Zhang, et al. ∙

research

∙ 01/20/2020

Model Reuse with Reduced Kernel Mean Embedding Specification

Given a publicly available pool of machine learning models constructed f...

26 Xi-Zhu Wu, et al. ∙

research

∙ 11/15/2019

Multi-Label Learning with Deep Forest

In multi-label learning, each instance is associated with multiple label...

22 Liang Yang, et al. ∙

research

∙ 10/21/2019

An Unbiased Risk Estimator for Learning with Augmented Classes

In this paper, we study the problem of learning with augmented classes (...

31 Yu-Jie Zhang, et al. ∙

research

∙ 09/18/2019

Bifurcation Spiking Neural Network

Recently spiking neural networks (SNNs) have received much attention bec...

2 Shao-Qun Zhang, et al. ∙

research

∙ 09/09/2019

Expert-Level Atari Imitation Learning from Demonstrations Only

One of the key issues for imitation learning lies in making policy learn...

9 Xin-Qiang Cai, et al. ∙

research

∙ 07/29/2019

Bandit Convex Optimization in Non-stationary Environments

Bandit Convex Optimization (BCO) is a fundamental framework for modeling...

4 Peng Zhao, et al. ∙

research

∙ 06/26/2019

Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions

To deal with changing environments, a new performance measure---adaptive...

2 Lijun Zhang, et al. ∙

research

∙ 06/10/2019

Joint Semantic Domain Alignment and Target Classifier Learning for Unsupervised Domain Adaptation

Unsupervised domain adaptation aims to transfer the classifier learned f...

5 Dong-Dong Chen, et al. ∙

research

∙ 06/10/2019

Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective

We study the problem of computing the minimum adversarial perturbation o...

0 Lu Wang, et al. ∙

research

∙ 05/31/2019

Reinforcement Learning Experience Reuse with Policy Residual Representation

Experience reuse is key to sample-efficient reinforcement learning. One ...

0 Wen-Ji Zhou, et al. ∙

research

∙ 05/22/2019

Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder

In this work, we consider one challenging training time attack by modify...

13 Ji Feng, et al. ∙

research

∙ 05/07/2019

Forest Representation Learning Guided by Margin Distribution

In this paper, we reformulate the forest representation learning approac...

0 Shen-Huan Lv, et al. ∙

research

∙ 04/27/2019

Prediction with Unpredictable Feature Evolution

Feature space can change or evolve when learning with streaming data. Se...

30 Bo-Jian Hou, et al. ∙

research

∙ 04/26/2019

Adaptive Regret of Convex and Smooth Functions

We investigate online convex optimization in changing environments, and ...

10 Lijun Zhang, et al. ∙

research

∙ 04/22/2019

Reliable Weakly Supervised Learning: Maximize Gain and Maintain Safeness

Weakly supervised data are widespread and have attracted much attention....

6 Lan-Zhe Guo, et al. ∙

research

∙ 01/27/2019

Stochastic Approximation of Smooth and Strongly Convex Functions: Beyond the O(1/T) Convergence Rate

Stochastic approximation (SA) is a classical approach for stochastic con...

12 Lijun Zhang, et al. ∙

research

∙ 12/27/2018

Optimal Margin Distribution Network

Recent research about margin theory has proved that maximizing the minim...

0 Shen-Huan Lv, et al. ∙

research

∙ 10/25/2018

Adaptive Online Learning in Dynamic Environments

In this paper, we study online convex optimization in dynamic environmen...

0 Lijun Zhang, et al. ∙

research

∙ 10/25/2018

Learning with Interpretable Structure from RNN

In structure learning, the output is generally a structure that is used ...

0 Bo-Jian Hou, et al. ∙

research

∙ 09/08/2018

Handling Concept Drift via Model Reuse

In many real-world applications, data are often collected in the form of...

0 Peng Zhao, et al. ∙

research

∙ 05/31/2018

Multi-Layered Gradient Boosting Decision Trees

Multi-layered representation is believed to be the key ingredient of dee...

0 Ji Feng, et al. ∙

research

∙ 05/23/2018

Matrix Co-completion for Multi-label Classification with Missing Features and Labels

We consider a challenging multi-label classification problem where both ...

0 Miao Xu, et al. ∙

research

∙ 05/11/2018

Distributed Deep Forest and its Application to Automatic Detection of Cash-out Fraud

Internet companies are facing the need of handling large scale machine l...

0 Ya-Lin Zhang, et al. ∙

research

∙ 05/02/2018

ℓ_1-regression with Heavy-tailed Distributions

In this paper, we consider the problem of linear regression with heavy-t...

0 Lijun Zhang, et al. ∙

research

∙ 02/04/2018

Tunneling Neural Perception and Logic Reasoning through Abductive Learning

Perception and reasoning are basic human abilities that are seamlessly c...

0 Wang-Zhou Dai, et al. ∙

research

∙ 11/20/2017

Maximizing Non-monotone/Non-submodular Functions by Multi-objective Evolutionary Algorithms

Evolutionary algorithms (EAs) are a kind of nature-inspired general-purp...

0 Chao Qian, et al. ∙

Zhi-Hua Zhou

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