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Found 14 papers, 4 papers with code
AI in Pharma for Personalized Sequential Decision-Making: Methods, Applications and Opportunities
In the pharmaceutical industry, the use of artificial intelligence (AI) has seen consistent growth over the past decade.
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.
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.
Quasi-optimal Reinforcement Learning with Continuous Actions
Many real-world applications of reinforcement learning (RL) require making decisions in continuous action environments.
Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks
This paper revisits the approach from a matrix approximation perspective, and identifies two issues in the existing layer-wise sampling methods: suboptimal sampling probabilities and estimation biases induced by sampling without replacement.
Confidence Band Estimation for Survival Random Forests
The idea is to estimate the variance-covariance matrix of the cumulative hazard function prediction on a grid of time points.
On Variance Estimation of Random Forests with Infinite-Order U-statistics
To bridge these gaps in the literature, we propose a new view of the Hoeffding decomposition for variance estimation that leads to an unbiased estimator.
Random Forest Weighted Local Fréchet Regression with Random Objects
Statistical analysis is increasingly confronted with complex data from metric spaces.
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.
Revisiting Layer-wise Sampling in Fast Training for Graph Convolutional Networks
To accelerate the training of graph convolutional networks (GCN), many sampling-based methods have been developed for approximating the embedding aggregation.
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.
Estimating heterogeneous treatment effects with right-censored data via causal survival forests
Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation.
Constructing Stabilized Dynamic Treatment Regimes
We propose a new method termed stabilized O-learning for deriving stabilized dynamic treatment regimes, which are sequential decision rules for individual patients that not only adapt over the course of the disease progression but also remain consistent over time in format.
Methodology
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.
