Africa
MonteCarloTreeSearchbasedVariableSelection forHighDimensionalBayesianOptimization
Our regret bound generalizes that of GP-UCB [38] which always selects all variables, aswellasthatofDropout [21]whichselectsdvariables randomly ineachiteration.
Sparse Probabilistic Circuitsvia Pruningand Growing
Inthissection, particular, LLonaD by comparing C and (ii) theC\E caused E: LL(D,C,E)=LL(D,C) LL(D,C\E).
Efficiently avoiding saddle points with zero order methods: No gradients required
Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Georgios Piliouras
Neural Information Processing Systems http://nips.cc/
Bayesian Extensive-Rank Matrix Factorization with Rotational Invariant Priors
These matrix ensembles are defined precisely in section 2.1, but the reader can keep in
Provable Acceleration of Nesterov's Accelerated Gradient for Rectangular Matrix Factorization and Linear Neural Networks
We study the convergence rate of first-order methods for rectangular matrix factorization, which is a canonical nonconvex optimization problem.
Deep Equilibrium Algorithmic Reasoning
Algorithms, while straightforward in theory, become challenging to deploy in real-world scenarios.
