A First Empirical Study of Emphatic Temporal Difference Learning. - Sina Ghiassian; Banafsheh Rafiee; Richard S. Sutton (CoRR, 2017)

Two geometric input transformation methods for fast online reinforcement learning with neural nets. - Sina Ghiassian; Huizhen Yu; Banafsheh Rafiee; Richard S. Sutton (CoRR, 2018)

Online Off-policy Prediction. - Sina Ghiassian; Andrew Patterson; Martha White; Richard S. Sutton; Adam White 001 (CoRR, 2018)

Prediction in Intelligence: An Empirical Comparison of Off-policy Algorithms on Robots. - Banafsheh Rafiee; Sina Ghiassian; Adam White 001; Richard S. Sutton (AAMAS, 2019)

Should All Temporal Difference Learning Use Emphasis? - Xiang Gu 002; Sina Ghiassian; Richard S. Sutton (CoRR, 2019)

Overcoming Catastrophic Interference in Online Reinforcement Learning with Dynamic Self-Organizing Maps. - Yat Long Lo; Sina Ghiassian (CoRR, 2019)

Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks. - Sina Ghiassian; Banafsheh Rafiee; Yat Long Lo; Adam White 001 (AAMAS, 2020)

Gradient Temporal-Difference Learning with Regularized Corrections. - Sina Ghiassian; Andrew Patterson; Shivam Garg 006; Dhawal Gupta; Adam White 001; Martha White (ICML, 2020)

Does Standard Backpropagation Forget Less Catastrophically Than Adam? - Dylan R. Ashley; Sina Ghiassian; Richard S. Sutton (CoRR, 2021)

A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning. - Andrew Patterson; Adam White 001; Sina Ghiassian; Martha White (CoRR, 2021)

An Empirical Comparison of Off-policy Prediction Learning Algorithms on the Collision Task. - Sina Ghiassian; Richard S. Sutton (CoRR, 2021)

An Empirical Comparison of Off-policy Prediction Learning Algorithms in the Four Rooms Environment. - Sina Ghiassian; Richard S. Sutton (CoRR, 2021)

Importance Sampling Placement in Off-Policy Temporal-Difference Methods. - Eric Graves 002; Sina Ghiassian (CoRR, 2022)

Auxiliary task discovery through generate-and-test. - Banafsheh Rafiee; Sina Ghiassian; Jun Jin 001; Richard S. Sutton; Jun Luo 009; Adam White 001 (CoLLAs, 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 001 (Adapt. Behav., 2023)

In-context Exploration-Exploitation for Reinforcement Learning. - Zhenwen Dai; Federico Tomasi; Sina Ghiassian (ICLR, 2024)

Soft Preference Optimization: Aligning Language Models to Expert Distributions. - Arsalan Sharifnassab; Sina Ghiassian; Saber Salehkaleybar; Surya Kanoria; Dale Schuurmans (CoRR, 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 (Trans. Mach. Learn. Res., 2025)

Off-Policy Prediction Learning: An Empirical Study of Online Algorithms. - Sina Ghiassian; Banafsheh Rafiee; Richard S. Sutton (IEEE Trans. Neural Networks Learn. Syst., 2025)

Hallucination Detection on a Budget: Efficient Bayesian Estimation of Semantic Entropy. - Kamil Ciosek; Nicolò Felicioni; Sina Ghiassian (CoRR, 2025)