Statistical Learning
Embedding Dimension of Contrastive Learning and k-Nearest Neighbors
We study the embedding dimension of distance comparison data in two settings: contrastive learning and k -nearest neighbors ( k -NN).
Anytime Model Selection in Linear Bandits
M different samples in parallel from the environment so that the reward for each agent is realized.
Hyperparameter Learning via Distributional Transfer
Ho Chung Law, Peilin Zhao, Leung Sing Chan, Junzhou Huang, Dino Sejdinovic
However, BO suffers from the so called'cold start' problem [
cpSGD: Communication-efficient and differentially-private distributed SGD
Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X. Yu, Sanjiv Kumar, Brendan McMahan
Neural Information Processing Systems http://nips.cc/
Out-of-Distribution Detection using Multiple Semantic Label Representations
Gabi Shalev, Yossi Adi, Joseph Keshet
Neural Information Processing Systems http://nips.cc/