research
∙
06/24/2023
High-dimensional outlier detection and variable selection via adaptive weighted mean regression
This paper proposes an adaptive penalized weighted mean regression for o...
share
research
∙
06/17/2023
Online Local Differential Private Quantile Inference via Self-normalization
Based on binary inquiries, we developed an algorithm to estimate populat...
research
∙
03/24/2023
Mathematical Challenges in Deep Learning
Deep models are dominating the artificial intelligence (AI) industry sin...
research
∙
02/05/2023
Scalable inference in functional linear regression with streaming data
Traditional static functional data analysis is facing new challenges due...
research
∙
10/17/2022
Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy
Gaussian differential privacy (GDP) is a single-parameter family of priv...
research
∙
10/05/2022
Conformalized Fairness via Quantile Regression
Algorithmic fairness has received increased attention in socially sensit...
research
∙
10/03/2022
Inference on High-dimensional Single-index Models with Streaming Data
Traditional statistical methods are faced with new challenges due to str...
research
∙
09/29/2022
How Does Value Distribution in Distributional Reinforcement Learning Help Optimization?
We consider the problem of learning a set of probability distributions f...
research
∙
04/04/2022
An adaptive model checking test for functional linear model
Numerous studies have been devoted to the estimation and inference probl...
research
∙
02/01/2022
Distributional Reinforcement Learning via Sinkhorn Iterations
Distributional reinforcement learning (RL) is a class of state-of-the-ar...
research
∙
12/17/2021
Functional Linear Regression for Partially Observed Functional Data
In the functional linear regression model, many methods have been propos...
research
∙
12/09/2021
Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving
With widening deployments of natural language processing (NLP) in daily ...
research
∙
10/17/2021
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization
Anderson mixing has been heuristically applied to reinforcement learning...
research
∙
10/13/2021
TAG: Toward Accurate Social Media Content Tagging with a Concept Graph
Although conceptualization has been widely studied in semantics and know...
research
∙
10/07/2021
Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm
Distributional reinforcement learning (RL) is a class of state-of-the-ar...
research
∙
09/25/2021
L^2NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning
Neural architecture search (NAS) has achieved remarkable results in deep...
research
∙
09/21/2021
A Simple Unified Framework for Anomaly Detection in Deep Reinforcement Learning
Abnormal states in deep reinforcement learning (RL) are states that are ...
research
∙
09/17/2021
Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations
In real scenarios, state observations that an agent observes may contain...
research
∙
03/08/2021
A reproducing kernel Hilbert space framework for functional data classification
We encounter a bottleneck when we try to borrow the strength of classica...
research
∙
12/19/2020
High-Dimensional Spatial Quantile Function-on-Scalar Regression
This paper develops a novel spatial quantile function-on-scalar regressi...
research
∙
07/17/2019
Learning Privately over Distributed Features: An ADMM Sharing Approach
Distributed machine learning has been widely studied in order to handle ...
research
∙
05/13/2019
Distributional Reinforcement Learning for Efficient Exploration
In distributional reinforcement learning (RL), the estimated distributio...
research
∙
03/18/2019
Deep Reinforcement Learning with Decorrelation
Learning an effective representation for high-dimensional data is a chal...
research
∙
11/05/2018
QUOTA: The Quantile Option Architecture for Reinforcement Learning
In this paper, we propose the Quantile Option Architecture (QUOTA) for e...
research
∙
03/01/2018
House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis
Online real-estate information systems such as Zillow and Trulia have ga...
research
∙
03/01/2018
Recover Fine-Grained Spatial Data from Coarse Aggregation
In this paper, we study a new type of spatial sparse recovery problem, t...
research
∙
03/01/2018
Growing Story Forest Online from Massive Breaking News
We describe our experience of implementing a news content organization s...
research
∙
09/13/2017
cqrReg: An R Package for Quantile and Composite Quantile Regression and Variable Selection
The cqrReg package for R is the first to introduce a family of robust, h...
research
∙
09/07/2017
An Alternative Approach to Functional Linear Partial Quantile Regression
We have previously proposed the partial quantile regression (PQR) predic...
research
∙
06/07/2016
Expectile Matrix Factorization for Skewed Data Analysis
Matrix factorization is a popular approach to solving matrix estimation ...
research
∙
05/27/2016