4 citations · 8 across the 9 of their papers we have counts for
11 papers
ObjectRL: An Object-Oriented Reinforcement Learning Codebase
Gulcin Baykal, Abdullah Akgül, Manuel Haussmann +4
ObjectRL is an open-source Python codebase for deep reinforcement learning (RL), designed for research-oriented prototyping with minimal programming effort. Unlike existing codebas…
Improving Actor-Critic Training with Steerable Action-Value Approximation Errors
Bahareh Tasdighi, Nicklas Werge, Yi-Shan Wu +1
Off-policy actor-critic algorithms have shown strong potential in deep reinforcement learning for continuous control tasks. Their success primarily comes from leveraging pessimisti…
Deep Exploration with PAC-Bayes
Bahareh Tasdighi, Manuel Haussmann, Nicklas Werge +2
Reinforcement learning (RL) for continuous control under delayed rewards is an under-explored problem despite its significance in real-world applications. Many complex skills are b…
On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations
Antoine Godichon-Baggioni, Nicklas Werge
Stochastic optimization methods encounter new challenges in the realm of streaming, characterized by a continuous flow of large, high-dimensional data. While first-order methods, l…
Demystifying the Myths and Legends of Nonconvex Convergence of SGD
Aritra Dutta, El Houcine Bergou, Soumia Boucherouite +3
Stochastic gradient descent (SGD) and its variants are the main workhorses for solving large-scale optimization problems with nonconvex objective functions. Although the convergenc…
Weighted Sequential Bayesian Inference for Non-Stationary Linear Contextual Bandits
Nicklas Werge, Yi-Shan Wu, Abdullah Akgül +1
In non-stationary linear contextual bandits, existing efficient algorithms typically rely on the Weighted Regularized Least-Squares (WRLS) estimator. Because WRLS only provides poi…
An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition
Joseph de Vilmarest, Nicklas Werge
In this paper, we address the problem of probabilistic forecasting using an adaptive volatility method rooted in classical time-varying volatility models and leveraging online stoc…
Learning from time-dependent streaming data with online stochastic algorithms
Antoine Godichon-Baggioni, Nicklas Werge, Olivier Wintenberger
This paper addresses stochastic optimization in a streaming setting with time-dependent and biased gradient estimates. We analyze several first-order methods, including Stochastic…
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Streaming Data
Antoine Godichon-Baggioni, Nicklas Werge, Olivier Wintenberger
We introduce a streaming framework for analyzing stochastic approximation/optimization problems. This streaming framework is analogous to solving optimization problems using time-v…
Predicting Risk-adjusted Returns using an Asset Independent Regime-switching Model
Nicklas Werge
Financial markets tend to switch between various market regimes over time, making stationarity-based models unsustainable. We construct a regime-switching model independent of asse…
AdaVol: An Adaptive Recursive Volatility Prediction Method
Nicklas Werge, Olivier Wintenberger
Quasi-Maximum Likelihood (QML) procedures are theoretically appealing and widely used for statistical inference. While there are extensive references on QML estimation in batch set…