Optimal Algorithms for Stochastic Contextual Preference Bandits

By Aadirupa Saha. 2021

62 citations recorded by the source corpus.

Subfields: Reinforcement Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Reinforcement Learning

1. Jonathan Lee
2. Aldo Pacchiano
3. Aadirupa Saha
4. Chen Ye
5. Han Zhong
6. Tong Zhang
7. Wei Xiong
8. Nan Jiang

Citations in the corpus

• Dueling RL: Reinforcement Learning with Trajectory Preferences (2021, background): “Importantly, the regret scales nearly optimally with d √ T dependency given existing Ω(d √ T ) lower bounds for linear bandits (Lattimore and Szepesvári, 2020) and known reductions between the standard and preference regret Saha (2021).”
• Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint (2023, background): “The theoretical understanding of RLHF can be traced back to research on dueling bandits (e.g., Yue et al., 2012; Saha, 2021; Bengs et al., 2021 is that instead of optimizing against an external preference oracle, they adopt a clever idea by using the LLM itself as the reward model to provide…”