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Found 6 papers, 0 papers with code
Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources
We show that due to the relative nature of the feedback, the problem is more difficult than its bandit counterpart and that without further assumptions the problem is not learnable from a regret minimization perspective.
Contextual Bandits with Online Neural Regression
Based on such a perturbed prediction, we show a ${\mathcal{O}}(\log T)$ regret for online regression with both squared loss and KL loss, and subsequently convert these respectively to $\tilde{\mathcal{O}}(\sqrt{KT})$ and $\tilde{\mathcal{O}}(\sqrt{KL^*} + K)$ regret for NeuCB, where $L^*$ is the loss of the best policy.
$N$-Timescale Stochastic Approximation: Stability and Convergence
This paper presents the first sufficient conditions that guarantee the stability and almost sure convergence of $N$-timescale stochastic approximation (SA) iterates for any $N\geq1$.
Schedule Based Temporal Difference Algorithms
However, the weights assigned to different $n$-step returns in TD($\lambda$), controlled by the parameter $\lambda$, decrease exponentially with increasing $n$.
Gradient Temporal Difference with Momentum: Stability and Convergence
Here, we consider Gradient TD algorithms with an additional heavy ball momentum term and provide choice of step size and momentum parameter that ensures almost sure convergence of these algorithms asymptotically.
Does Momentum Help? A Sample Complexity Analysis
Stochastic Heavy Ball (SHB) and Nesterov's Accelerated Stochastic Gradient (ASG) are popular momentum methods in stochastic optimization.
