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Found 17 papers, 0 papers with code
Structural transfer learning of non-Gaussian DAG
Directed acyclic graph (DAG) has been widely employed to represent directional relationships among a set of collected nodes.
Non-Asymptotic Bounds for Adversarial Excess Risk under Misspecified Models
For the quadratic loss in nonparametric regression, we show that the adversarial excess risk bound can be improved over those for a general loss.
Transfer learning for tensor Gaussian graphical models
Tensor Gaussian graphical models (GGMs), interpreting conditional independence structures within tensor data, have important applications in numerous areas.
Efficient Estimation for Longitudinal Networks via Adaptive Merging
Longitudinal network consists of a sequence of temporal edges among multiple nodes, where the temporal edges are observed in real time.
Signed Network Embedding with Application to Simultaneous Detection of Communities and Anomalies
This paper develops a unified embedding model for signed networks to disentangle the intertwined balance structure and anomaly effect, which can greatly facilitate the downstream analysis, including community detection, anomaly detection, and network inference.
High-Frequency-Based Volatility Model with Network Structure
This paper introduces one new multivariate volatility model that can accommodate an appropriately defined network structure based on low-frequency and high-frequency data.
Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation
In this paper, we provide theoretical results of estimation bounds and excess risk upper bounds for support vector machine (SVM) with sparse multi-kernel representation.
Efficient Learning of Quadratic Variance Function Directed Acyclic Graphs via Topological Layers
Directed acyclic graph (DAG) models are widely used to represent causal relationships among random variables in many application domains.
Learning linear non-Gaussian directed acyclic graph with diverging number of nodes
The proposed method leverages a novel concept of topological layer to facilitate the DAG learning.
Kernel-based estimation for partially functional linear model: Minimax rates and randomized sketches
This paper considers the partially functional linear model (PFLM) where all predictive features consist of a functional covariate and a high dimensional scalar vector.
On strong convergence of the two-tower model for recommender system
Recommender system is capable of predicting preferred items for a user by integrating information from similar users or items.
Community Detection in General Hypergraph via Graph Embedding
Conventional network data has largely focused on pairwise interactions between two entities, yet multi-way interactions among multiple entities have been frequently observed in real-life hypergraph networks.
Projected Robust PCA with Application to Smooth Image Recovery
For image data related matrix recovery problems, approximate low-rankness and smoothness are the two most commonly imposed structures.
Efficient kernel-based variable selection with sparsistency
Variable selection is central to high-dimensional data analysis, and various algorithms have been developed.
Joint estimation of sparse multivariate regression and conditional graphical models
In this paper, we propose a high- dimensional multivariate conditional regression model for constructing sparse estimates of the multivariate regression coefficient matrix that accounts for the dependency struc- ture among the multiple responses.
An efficient model-free estimation of multiclass conditional probability
Conventional multiclass conditional probability estimation methods, such as Fisher's discriminate analysis and logistic regression, often require restrictive distributional model assumption.
Consistent selection of tuning parameters via variable selection stability
The key idea is to select the tuning parameters so that the resultant penalized regression model is stable in variable selection.
