Sina Ghiassian

According to our database, Sina Ghiassian authored at least 19 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

On the Importance of Uncertainty in Decision-Making with Large Language Models.

Nicolò Felicioni, Lucas Maystre, Sina Ghiassian, Kamil Ciosek

Trans. Mach. Learn. Res., 2024

Learning in complex action spaces without policy gradients.

Arash Tavakoli, Sina Ghiassian, Nemanja Rakicevic

CoRR, 2024

Soft Preference Optimization: Aligning Language Models to Expert Distributions.

Arsalan Sharifnassab, Sina Ghiassian, Saber Salehkaleybar, Surya Kanoria, Dale Schuurmans

CoRR, 2024

In-context Exploration-Exploitation for Reinforcement Learning.

Zhenwen Dai, Federico Tomasi, Sina Ghiassian

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

From eye-blinks to state construction: Diagnostic benchmarks for online representation learning.

Banafsheh Rafiee, Zaheer Abbas, Sina Ghiassian, Raksha Kumaraswamy, Richard S. Sutton, Elliot A. Ludvig, Adam White

Adapt. Behav., February, 2023

Auxiliary task discovery through generate-and-test.

Banafsheh Rafiee, Sina Ghiassian, Jun Jin, Richard S. Sutton, Jun Luo, Adam White

Proceedings of the Conference on Lifelong Learning Agents, 2023

2022

Importance Sampling Placement in Off-Policy Temporal-Difference Methods.

Eric Graves, Sina Ghiassian

CoRR, 2022

2021

An Empirical Comparison of Off-policy Prediction Learning Algorithms in the Four Rooms Environment.

Sina Ghiassian, Richard S. Sutton

CoRR, 2021

An Empirical Comparison of Off-policy Prediction Learning Algorithms on the Collision Task.

Sina Ghiassian, Richard S. Sutton

CoRR, 2021

A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning.

Andrew Patterson, Adam White, Sina Ghiassian, Martha White

CoRR, 2021

Does Standard Backpropagation Forget Less Catastrophically Than Adam?

Dylan R. Ashley, Sina Ghiassian, Richard S. Sutton

CoRR, 2021

2020

Gradient Temporal-Difference Learning with Regularized Corrections.

Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White

Proceedings of the 37th International Conference on Machine Learning, 2020

Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks.

Sina Ghiassian, Banafsheh Rafiee, Yat Long Lo, Adam White

Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

2019

Overcoming Catastrophic Interference in Online Reinforcement Learning with Dynamic Self-Organizing Maps.

Yat Long Lo, Sina Ghiassian

CoRR, 2019

Should All Temporal Difference Learning Use Emphasis?

Xiang Gu, Sina Ghiassian, Richard S. Sutton

CoRR, 2019

Prediction in Intelligence: An Empirical Comparison of Off-policy Algorithms on Robots.

Banafsheh Rafiee, Sina Ghiassian, Adam White, Richard S. Sutton

Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

2018

Online Off-policy Prediction.

Sina Ghiassian, Andrew Patterson, Martha White, Richard S. Sutton, Adam White

CoRR, 2018

Two geometric input transformation methods for fast online reinforcement learning with neural nets.

Sina Ghiassian, Huizhen Yu, Banafsheh Rafiee, Richard S. Sutton

CoRR, 2018

2017

A First Empirical Study of Emphatic Temporal Difference Learning.

Sina Ghiassian, Banafsheh Rafiee, Richard S. Sutton

CoRR, 2017