By identifying an instrumental variable correlated with the variableX butunrelated tothe confounders, researchers can isolate the exogenous variation inX and estimate acausal relationship betweenX andY.

As afollow-up work, [26]further released the rank-one restriction on the action feature matrices, andtheyintroduced analgorithm LowGLOC based ontheonline-to-confidenceset conversion [2]for generalized low-rank matrix bandits with O( p (d1+d2)3rT)regret bound.

In this paper, we study the finite-time convergence of nonlinear SA with multiplecoupledsequences.

More specifically,inour main result (Theorem 3.2) we identify a set of sufficient conditions on the initialization and the network topology under which theglobal convergence ofgradient descent isobtained.

Modifications to a neural network's input and output layers are often required to accommodate the specificities of most practical learning tasks.

Inthisworkwepresenta scalable quasi-Bayesian procedure for IV regression, building upon the recently developed kernelized IV models.

Multi-Agent Reinforcement Learning (MARL) has achieved impressive performance in a wide array of applications including multi-player game play [42, 31], multi-robot systems [13], and autonomousdriving[25].

Furthermore, sinceft is L-Lipschitz, w.r.t.

We propose an adaptive version of the Condat-V u algorithm, which alternates between primal gradient steps anddual proximal steps.