Search Results for author:
Found 7 papers, 6 papers with code
SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs
While incorporating new information with the retrieval of relevant passages is a promising way to improve QA with LLMs, the existing methods often require additional fine-tuning which becomes infeasible with recent LLMs.
Preference Transformer: Modeling Human Preferences using Transformers for RL
In this paper, we present Preference Transformer, a neural architecture that models human preferences using transformers.
Meta-Learning with Self-Improving Momentum Target
The idea of using a separately trained target model (or teacher) to improve the performance of the student model has been increasingly popular in various machine learning domains, and meta-learning is no exception; a recent discovery shows that utilizing task-wise target models can significantly boost the generalization performance.
SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning
In order to leverage unlabeled samples for reward learning, we infer pseudo-labels of the unlabeled samples based on the confidence of the preference predictor.
Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning
Behavioral cloning has proven to be effective for learning sequential decision-making policies from expert demonstrations.
OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data
Semi-supervised learning (SSL) has been a powerful strategy to incorporate few labels in learning better representations.
Regularizing Class-wise Predictions via Self-knowledge Distillation
Deep neural networks with millions of parameters may suffer from poor generalization due to overfitting.
