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Found 16 papers, 0 papers with code
CONCERT: Covariate-Elaborated Robust Local Information Transfer with Conditional Spike-and-Slab Prior
Distinguished from existing work, CONCERT is a one-step procedure, which achieves variable selection and information transfer simultaneously.
Dynamic Topic Language Model on Heterogeneous Children's Mental Health Clinical Notes
However, few topic models are built for longitudinal settings, and they fail to keep consistent topics and capture temporal trajectories for each document.
Individualized Dynamic Model for Multi-resolutional Data with Application to Mobile Health
In this paper, we propose an individualized dynamic latent factor model for irregular multi-resolution time series data to interpolate unsampled measurements of time series with low resolution.
Stage-Aware Learning for Dynamic Treatments
In particular, the proposed learning scheme builds a more general framework which includes the popular outcome weighted learning framework as a special case of ours.
Stackelberg Batch Policy Learning
From a theoretical standpoint, we provide instance-dependent regret bounds with general function approximation, which shows that our algorithm can learn a best-effort policy that is able to compete against any comparator policy that is covered by batch data.
Distributional Shift-Aware Off-Policy Interval Estimation: A Unified Error Quantification Framework
This task faces two primary challenges: providing a comprehensive and rigorous error quantification in CI estimation, and addressing the distributional shift that results from discrepancies between the distribution induced by the target policy and the offline data-generating process.
A Model-Agnostic Graph Neural Network for Integrating Local and Global Information
Graph Neural Networks (GNNs) have achieved promising performance in a variety of graph-focused tasks.
Query-augmented Active Metric Learning
In particular, we augment the queried constraints by generating more pairwise labels to provide additional information in learning a metric to enhance clustering performance.
High-order joint embedding for multi-level link prediction
Link prediction infers potential links from observed networks, and is one of the essential problems in network analyses.
Estimating Optimal Infinite Horizon Dynamic Treatment Regimes via pT-Learning
To address these challenges, we propose a Proximal Temporal consistency Learning (pT-Learning) framework to estimate an optimal regime that is adaptively adjusted between deterministic and stochastic sparse policy models.
Dermoscopic Image Classification with Neural Style Transfer
We represent each dermoscopic image as the style image and transfer the style of the lesion onto a homogeneous content image.
Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness
Due to accessible big data collections from consumers, products, and stores, advanced sales forecasting capabilities have drawn great attention from many companies especially in the retail business because of its importance in decision making.
Multicategory Angle-based Learning for Estimating Optimal Dynamic Treatment Regimes with Censored Data
An optimal dynamic treatment regime (DTR) consists of a sequence of decision rules in maximizing long-term benefits, which is applicable for chronic diseases such as HIV infection or cancer.
Individualized Multilayer Tensor Learning with An Application in Imaging Analysis
This work is motivated by multimodality breast cancer imaging data, which is quite challenging in that the signals of discrete tumor-associated microvesicles (TMVs) are randomly distributed with heterogeneous patterns.
Semi-orthogonal Non-negative Matrix Factorization with an Application in Text Mining
The subjects can then be interpreted as a non-subtractive linear combination of orthogonal basis topic vectors.
Multilayer tensor factorization with applications to recommender systems
Recommender systems have been widely adopted by electronic commerce and entertainment industries for individualized prediction and recommendation, which benefit consumers and improve business intelligence.
