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Found 8 papers, 1 papers with code
Probabilistic Actor-Critic: Learning to Explore with PAC-Bayes Uncertainty
We introduce Probabilistic Actor-Critic (PAC), a novel reinforcement learning algorithm with improved continuous control performance thanks to its ability to mitigate the exploration-exploitation trade-off.
Demystifying the Myths and Legends of Nonconvex Convergence of SGD
Additionally, our analyses allow us to measure the density of the $\epsilon$-stationary points in the final iterates of SGD, and we recover the classical $O(\frac{1}{\sqrt{T}})$ asymptotic rate under various existing assumptions on the objective function and the bounds on the stochastic gradient.
BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits
We propose a novel Bayesian-Optimistic Frequentist Upper Confidence Bound (BOF-UCB) algorithm for stochastic contextual linear bandits in non-stationary environments.
An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition
In this note, we address the problem of probabilistic forecasting using an adaptive volatility method based on classical time-varying volatility models and stochastic optimization algorithms.
Learning from time-dependent streaming data with online stochastic algorithms
This paper addresses stochastic optimization in a streaming setting with time-dependent and biased gradient estimates.
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Streaming Data
We provide non-asymptotic convergence rates of various gradient-based algorithms; this includes the famous Stochastic Gradient (SG) descent (a. k. a.
Predicting Risk-adjusted Returns using an Asset Independent Regime-switching Model
Financial markets tend to switch between various market regimes over time, making stationarity-based models unsustainable.
AdaVol: An Adaptive Recursive Volatility Prediction Method
An investigation of the convergence properties of the QML procedure in a general conditionally heteroscedastic time series model is conducted, and the classical batch optimization routines extended to the framework of streaming and large-scale problems.
