Europe
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 [
Factored Bandits
Julian Zimmert, Yevgeny Seldin
Neural Information Processing Systems http://nips.cc/
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/
Learning on the Edge: Online Learning with Stochastic Feedback Graphs
The framework of feedback graphs is a generalizationof sequential decisionmaking with bandit or full information feedback. In this work, we study an extension where the directed feedback graph is stochastic, following a distribution similar to the classical Erdลs-Rรฉnyi model. Specifically, in each round every edge in the graph is either realized or not with a distinct probability for each edge.
Out-of-Distribution Detection using Multiple Semantic Label Representations
Gabi Shalev, Yossi Adi, Joseph Keshet
Neural Information Processing Systems http://nips.cc/
S)GD over Diagonal Linear Networks Implicit Bias Large and Edge of Stability
Currently, most theoretical works on implicit regularisation have primarily focused on continuous time approximations of (S)GD where the impact of crucial hyperparameters such as the stepsize and the minibatch size are ignored. One such common simplification is to analyse gradient flow, which is a continuous time limit of GD and minibatch SGD with an infinitesimal stepsize. By definition, this analysis does not capture the effect of stepsize or stochasticity.