Statistical Learning
Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning Zhongzheng Ren
But not all unlabeled data are equal. In this paper we study how to use a different weight for every unlabeled example. Manual tuning of all those weights - as done in prior work - is no longer possible.
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
Numerical experiments have become an essential part of statistics and machine learning (ML).
Appendix
Stimuli are visualized in Figures 7 and 8. Our Python library,"modelvshuman", to test and benchmark models against high-quality human These two models are referred to as ViT -L (14M) and ViT -B (14M) in the paper. Note that the "im-agenet1k" suffix in the model names does not mean the model was only trained on ImageNet1K. We then made two predictions which we test here. While this relationship is not perfect (e.g., the difference is small for silhouette Prior to the experiment, visual acuity was measured with a Snellen chart to ensure normal or corrected to normal vision. Our experiment was a standard perceptual experiment, for which no IRB approval was required.
Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects
Causal inference aims to draw a causal relationship between some treatment and target responses.
A Damped Newton Method Achieves Global O null 1 k 2 null and Local Quadratic Convergence Rate
Newton method of Polyak and Nesterov (2006) and of regularized Newton method of Mishchenko (2021) and Doikov and Nesterov (2021), b) we prove a local quadratic rate, which matches the best-known local rate of second-order methods, and c) our stepsize formula is simple, explicit, and does not require solving any subproblem.
