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09/16/2023
Efficient Methods for Non-stationary Online Learning
Non-stationary online learning has drawn much attention in recent years....
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07/17/2023
Universal Online Learning with Gradual Variations: A Multi-layer Online Ensemble Approach
In this paper, we propose an online convex optimization method with two ...
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05/03/2023
Stream Efficient Learning
Data in many real-world applications are often accumulated over time, li...
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03/05/2023
Revisiting Weighted Strategy for Non-stationary Parametric Bandits
Non-stationary parametric bandits have attracted much attention recently...
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02/18/2023
Stochastic Approximation Approaches to Group Distributionally Robust Optimization
This paper investigates group distributionally robust optimization (GDRO...
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10/07/2022
Learnware: Small Models Do Big
There are complaints about current machine learning techniques such as t...
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08/26/2022
Dynamic Regret of Online Markov Decision Processes
We investigate online Markov Decision Processes (MDPs) with adversariall...
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07/05/2022
Adapting to Online Label Shift with Provable Guarantees
The standard supervised learning paradigm works effectively when trainin...
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06/01/2022
Open Environment Machine Learning
Conventional machine learning studies generally assume close world scena...
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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...
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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...
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12/29/2021
Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization
We investigate online convex optimization in non-stationary environments...
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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...
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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...
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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 ...
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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...
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08/15/2021
Towards Understanding Theoretical Advantages of Complex-Reaction Networks
Complex-valued neural networks have attracted increasing attention in re...
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06/17/2021
Seeing Differently, Acting Similarly: Imitation Learning with Heterogeneous Observations
In many real-world imitation learning tasks, the demonstrator and the le...
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06/06/2021
Towards an Understanding of Benign Overfitting in Neural Networks
Modern machine learning models often employ a huge number of parameters ...
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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...
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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...
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07/22/2020
Storage Fit Learning with Feature Evolvable Streams
Feature evolvable learning has been widely studied in recent years where...
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07/07/2020
Dynamic Regret of Convex and Smooth Functions
We investigate online convex optimization in non-stationary environments...
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06/07/2020
Soft Gradient Boosting Machine
Gradient Boosting Machine has proven to be one successful function appro...
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04/08/2020
Flexible Transmitter Network
Current neural networks are mostly built upon the MP model, which usuall...
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02/05/2020
Exploratory Machine Learning with Unknown Unknowns
In conventional supervised learning, a training dataset is given with gr...
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01/20/2020
Model Reuse with Reduced Kernel Mean Embedding Specification
Given a publicly available pool of machine learning models constructed f...
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11/15/2019
Multi-Label Learning with Deep Forest
In multi-label learning, each instance is associated with multiple label...
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10/21/2019
An Unbiased Risk Estimator for Learning with Augmented Classes
In this paper, we study the problem of learning with augmented classes (...
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09/18/2019
Bifurcation Spiking Neural Network
Recently spiking neural networks (SNNs) have received much attention bec...
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09/09/2019
Expert-Level Atari Imitation Learning from Demonstrations Only
One of the key issues for imitation learning lies in making policy learn...
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07/29/2019
Bandit Convex Optimization in Non-stationary Environments
Bandit Convex Optimization (BCO) is a fundamental framework for modeling...
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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...
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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...
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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...
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05/31/2019
Reinforcement Learning Experience Reuse with Policy Residual Representation
Experience reuse is key to sample-efficient reinforcement learning. One ...
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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...
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05/07/2019
Forest Representation Learning Guided by Margin Distribution
In this paper, we reformulate the forest representation learning approac...
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04/27/2019
Prediction with Unpredictable Feature Evolution
Feature space can change or evolve when learning with streaming data. Se...
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04/26/2019
Adaptive Regret of Convex and Smooth Functions
We investigate online convex optimization in changing environments, and ...
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04/22/2019
Reliable Weakly Supervised Learning: Maximize Gain and Maintain Safeness
Weakly supervised data are widespread and have attracted much attention....
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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...
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12/27/2018
Optimal Margin Distribution Network
Recent research about margin theory has proved that maximizing the minim...
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10/25/2018
Adaptive Online Learning in Dynamic Environments
In this paper, we study online convex optimization in dynamic environmen...
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10/25/2018
Learning with Interpretable Structure from RNN
In structure learning, the output is generally a structure that is used ...
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09/08/2018
Handling Concept Drift via Model Reuse
In many real-world applications, data are often collected in the form of...
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05/31/2018
Multi-Layered Gradient Boosting Decision Trees
Multi-layered representation is believed to be the key ingredient of dee...
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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 ...
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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...
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05/02/2018
