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Found 9 papers, 2 papers with code
DENOISER: Rethinking the Robustness for Open-Vocabulary Action Recognition
The denoised text classes help OVAR models classify visual samples more accurately; in return, classified visual samples help better denoising.
Extending iLQR method with control delay
Iterative linear quadradic regulator(iLQR) has become a benchmark method to deal with nonlinear stochastic optimal control problem.
Robust inference on the average treatment effect using the outcome highly adaptive lasso
Many estimators of the average effect of a treatment on an outcome require estimation of the propensity score, the outcome regression, or both.
Semisupervised Learning on Heterogeneous Graphs and its Applications to Facebook News Feed
We show that the HELP algorithm improves the predictive performance across multiple tasks, together with semantically meaningful embedding that are discriminative for downstream classification or regression tasks.
Collaborative targeted inference from continuously indexed nuisance parameter estimators
Say that the above product is not fast enough and the algorithm for the $G$-component is fine-tuned by a real-valued $h$.
On Adaptive Propensity Score Truncation in Causal Inference
Even if the positivity assumption holds, practical violations of this assumption may jeopardize the finite sample performance of the causal estimator.
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data
Collaborative minimum loss-based estimation (C-TMLE) is a novel methodology for causal inference that takes into account information on the causal parameter of interest when selecting a PS model.
The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification
In this work, we investigated multiple widely used ensemble methods, including unweighted averaging, majority voting, the Bayes Optimal Classifier, and the (discrete) Super Learner, for image recognition tasks, with deep neural networks as candidate algorithms.
Propensity score prediction for electronic healthcare databases using Super Learner and High-dimensional Propensity Score Methods
In this study, we applied and evaluated the performance of the SL in its ability to predict treatment assignment using three electronic healthcare databases.
