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Sina Ghiassian
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2020 – today
- 2024
- [j2]Nicolò Felicioni, Lucas Maystre, Sina Ghiassian, Kamil Ciosek:
On the Importance of Uncertainty in Decision-Making with Large Language Models. Trans. Mach. Learn. Res. 2024 (2024) - [c5]Zhenwen Dai, Federico Tomasi, Sina Ghiassian:
In-context Exploration-Exploitation for Reinforcement Learning. ICLR 2024 - [i16]Zhenwen Dai, Federico Tomasi, Sina Ghiassian:
In-context Exploration-Exploitation for Reinforcement Learning. CoRR abs/2403.06826 (2024) - [i15]Nicolò Felicioni, Lucas Maystre, Sina Ghiassian, Kamil Ciosek:
On the Importance of Uncertainty in Decision-Making with Large Language Models. CoRR abs/2404.02649 (2024) - [i14]Arsalan Sharifnassab, Sina Ghiassian, Saber Salehkaleybar, Surya Kanoria, Dale Schuurmans:
Soft Preference Optimization: Aligning Language Models to Expert Distributions. CoRR abs/2405.00747 (2024) - 2023
- [j1]Banafsheh Rafiee, Zaheer Abbas, Sina Ghiassian, Raksha Kumaraswamy, Richard S. Sutton, Elliot A. Ludvig, Adam White:
From eye-blinks to state construction: Diagnostic benchmarks for online representation learning. Adapt. Behav. 31(1): 3-19 (2023) - [c4]Banafsheh Rafiee, Sina Ghiassian, Jun Jin, Richard S. Sutton, Jun Luo, Adam White:
Auxiliary task discovery through generate-and-test. CoLLAs 2023: 703-714 - 2022
- [i13]Eric Graves, Sina Ghiassian:
Importance Sampling Placement in Off-Policy Temporal-Difference Methods. CoRR abs/2203.10172 (2022) - [i12]Banafsheh Rafiee, Sina Ghiassian, Jun Jin, Richard S. Sutton, Jun Luo, Adam White:
Auxiliary task discovery through generate-and-test. CoRR abs/2210.14361 (2022) - 2021
- [i11]Dylan R. Ashley, Sina Ghiassian, Richard S. Sutton:
Does Standard Backpropagation Forget Less Catastrophically Than Adam? CoRR abs/2102.07686 (2021) - [i10]Andrew Patterson, Adam White, Sina Ghiassian, Martha White:
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning. CoRR abs/2104.13844 (2021) - [i9]Sina Ghiassian, Richard S. Sutton:
An Empirical Comparison of Off-policy Prediction Learning Algorithms on the Collision Task. CoRR abs/2106.00922 (2021) - [i8]Sina Ghiassian, Richard S. Sutton:
An Empirical Comparison of Off-policy Prediction Learning Algorithms in the Four Rooms Environment. CoRR abs/2109.05110 (2021) - 2020
- [c3]Sina Ghiassian, Banafsheh Rafiee, Yat Long Lo, Adam White:
Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks. AAMAS 2020: 438-446 - [c2]Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White:
Gradient Temporal-Difference Learning with Regularized Corrections. ICML 2020: 3524-3534 - [i7]Sina Ghiassian, Banafsheh Rafiee, Yat Long Lo, Adam White:
Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks. CoRR abs/2003.07417 (2020) - [i6]Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White:
Gradient Temporal-Difference Learning with Regularized Corrections. CoRR abs/2007.00611 (2020)
2010 – 2019
- 2019
- [c1]Banafsheh Rafiee, Sina Ghiassian, Adam White, Richard S. Sutton:
Prediction in Intelligence: An Empirical Comparison of Off-policy Algorithms on Robots. AAMAS 2019: 332-340 - [i5]Xiang Gu, Sina Ghiassian, Richard S. Sutton:
Should All Temporal Difference Learning Use Emphasis? CoRR abs/1903.00194 (2019) - [i4]Yat Long Lo, Sina Ghiassian:
Overcoming Catastrophic Interference in Online Reinforcement Learning with Dynamic Self-Organizing Maps. CoRR abs/1910.13213 (2019) - 2018
- [i3]Sina Ghiassian, Huizhen Yu, Banafsheh Rafiee, Richard S. Sutton:
Two geometric input transformation methods for fast online reinforcement learning with neural nets. CoRR abs/1805.07476 (2018) - [i2]Sina Ghiassian, Andrew Patterson, Martha White, Richard S. Sutton, Adam White:
Online Off-policy Prediction. CoRR abs/1811.02597 (2018) - 2017
- [i1]Sina Ghiassian, Banafsheh Rafiee, Richard S. Sutton:
A First Empirical Study of Emphatic Temporal Difference Learning. CoRR abs/1705.04185 (2017)
Coauthor Index
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last updated on 2024-08-10 00:30 CEST by the dblp team
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