Ahmed Touati

According to our database1, Ahmed Touati authored at least 26 papers between 2014 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2024
Simple Ingredients for Offline Reinforcement Learning.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Score Models for Offline Goal-Conditioned Reinforcement Learning.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Fast Imitation via Behavior Foundation Models.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
A State Representation for Diminishing Rewards.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Does Zero-Shot Reinforcement Learning Exist?
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Learning One Representation to Optimize All Rewards.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

TDprop: Does Adaptive Optimization With Jacobi Preconditioning Help Temporal Difference Learning?
Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

2020
Efficient Learning in Non-Stationary Linear Markov Decision Processes.
CoRR, 2020

Maximum Reward Formulation In Reinforcement Learning.
CoRR, 2020

Sharp Analysis of Smoothed Bellman Error Embedding.
CoRR, 2020

TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning?
CoRR, 2020

Zooming for Efficient Model-Free Reinforcement Learning in Metric Spaces.
CoRR, 2020

Stable Policy Optimization via Off-Policy Divergence Regularization.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

SVRG for Policy Evaluation with Fewer Gradient Evaluations.
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Stochastic Neural Network with Kronecker Flow.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future.
CoRR, 2019

Separating value functions across time-scales.
CoRR, 2019

Randomized Value Functions via Multiplicative Normalizing Flows.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Separable value functions across time-scales.
Proceedings of the 36th International Conference on Machine Learning, 2019

Modeling the Long Term Future in Model-Based Reinforcement Learning.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
Convergent TREE BACKUP and RETRACE with Function Approximation.
Proceedings of the 35th International Conference on Machine Learning, 2018

Parametric Adversarial Divergences are Good Task Losses for Generative Modeling.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Learnable Explicit Density for Continuous Latent Space and Variational Inference.
CoRR, 2017

Adversarial Divergences are Good Task Losses for Generative Modeling.
CoRR, 2017

2014
Real-time privacy-preserving model-based estimation of traffic flows.
Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems, 2014


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