:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Kallel, Mahdi, Basu, Debabrota, Akrour, Riad, D'Eramo, Carlo
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	https://arxiv.org/abs/2407.04864
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Do Not Imitate, Reinforce: Iterative Classification via Belief Refinement
por: Kallel, Mahdi, et al.
Publicado: (2026)

Use the Online Network If You Can: Towards Fast and Stable Reinforcement Learning
por: Hendawy, Ahmed, et al.
Publicado: (2025)

Deterministic Exploration via Stationary Bellman Error Maximization
por: Griesbach, Sebastian, et al.
Publicado: (2024)

Learning to Explore in Diverse Reward Settings via Temporal-Difference-Error Maximization
por: Griesbach, Sebastian, et al.
Publicado: (2025)

$K$-Level Policy Gradients for Multi-Agent Reinforcement Learning
por: Reddi, Aryaman, et al.
Publicado: (2025)

Limits of Actor-Critic Algorithms for Decision Tree Policies Learning in IBMDPs
por: Kohler, Hector, et al.
Publicado: (2023)

Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts
por: Hendawy, Ahmed, et al.
Publicado: (2023)

StaQ it! Growing neural networks for Policy Mirror Descent
por: Shilova, Alena, et al.
Publicado: (2025)

Breiman meets Bellman: Non-Greedy Decision Trees with MDPs
por: Kohler, Hector, et al.
Publicado: (2023)

On the Benefit of Optimal Transport for Curriculum Reinforcement Learning
por: Klink, Pascal, et al.
Publicado: (2023)

Streaming Reinforcement Learning under Partial Observability with Real-Time Recurrent Learning
por: Farr, Noah, et al.
Publicado: (2026)

Dynamic Obstacle Avoidance with Bounded Rationality Adversarial Reinforcement Learning
por: Holgado-Alvarez, Jose-Luis, et al.
Publicado: (2025)

PB$^2$: Preference Space Exploration via Population-Based Methods in Preference-Based Reinforcement Learning
por: Driss, Brahim, et al.
Publicado: (2025)

Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning
por: Kohler, Hector, et al.
Publicado: (2024)

Evaluating Interpretable Reinforcement Learning by Distilling Policies into Programs
por: Kohler, Hector, et al.
Publicado: (2025)

Sharing Knowledge in Multi-Task Deep Reinforcement Learning
por: D'Eramo, Carlo, et al.
Publicado: (2024)

Adaptive $Q$-Network: On-the-fly Target Selection for Deep Reinforcement Learning
por: Vincent, Théo, et al.
Publicado: (2024)

Iterated $Q$-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning
por: Vincent, Théo, et al.
Publicado: (2024)

Eau De $Q$-Network: Adaptive Distillation of Neural Networks in Deep Reinforcement Learning
por: Vincent, Théo, et al.
Publicado: (2025)

Preference-based Pure Exploration
por: Shukla, Apurv, et al.
Publicado: (2024)

Domain Randomization via Entropy Maximization
por: Tiboni, Gabriele, et al.
Publicado: (2023)

Learning to Explore with Lagrangians for Bandits under Unknown Linear Constraints
por: Das, Udvas, et al.
Publicado: (2024)

Stochastic Online Instrumental Variable Regression: Regrets for Endogeneity and Bandit Feedback
por: Della Vecchia, Riccardo, et al.
Publicado: (2023)

Parameterized Projected Bellman Operator
por: Vincent, Théo, et al.
Publicado: (2023)

Some Targets Are Harder to Identify than Others: Quantifying the Target-dependent Membership Leakage
por: Azize, Achraf, et al.
Publicado: (2024)

Auditing Fairness under Model Updates: Fundamental Complexity and Property-Preserving Updates
por: Ajarra, Ayoub, et al.
Publicado: (2026)

Performative Policy Gradient: Optimality in Performative Reinforcement Learning
por: Basu, Debabrota, et al.
Publicado: (2025)

Sublinear Algorithms for Wasserstein and Total Variation Distances: Applications to Fairness and Privacy Auditing
por: Basu, Debabrota, et al.
Publicado: (2025)

Concentrated Differential Privacy for Bandits
por: Azize, Achraf, et al.
Publicado: (2023)

Isoperimetry is All We Need: Langevin Posterior Sampling for RL with Sublinear Regret
por: Jorge, Emilio, et al.
Publicado: (2024)

FLIPHAT: Joint Differential Privacy for High Dimensional Sparse Linear Bandits
por: Chakraborty, Sunrit, et al.
Publicado: (2024)

Gradient Iterated Temporal-Difference Learning
por: Vincent, Théo, et al.
Publicado: (2026)

Bridging the Performance Gap Between Target-Free and Target-Based Reinforcement Learning
por: Vincent, Théo, et al.
Publicado: (2025)

Active Fourier Auditor for Estimating Distributional Properties of ML Models
por: Ajarra, Ayoub, et al.
Publicado: (2024)

Sequential Membership Inference Attacks
por: Michel, Thomas, et al.
Publicado: (2026)

DP-SPRT: Differentially Private Sequential Probability Ratio Tests
por: Michel, Thomas, et al.
Publicado: (2025)

Pure Exploration in Bandits with Linear Constraints
por: Carlsson, Emil, et al.
Publicado: (2023)

Light to Heavy, Brief to Eternal: An Axion for Every Occasion (in the Early Universe)
por: D'Eramo, Francesco
Publicado: (2026)

Lagrangian-based Equilibrium Propagation: generalisation to arbitrary boundary conditions & equivalence with Hamiltonian Echo Learning
por: Pourcel, Guillaume, et al.
Publicado: (2025)

FraPPE: Fast and Efficient Preference-based Pure Exploration
por: Das, Udvas, et al.
Publicado: (2025)