Rejoinder: New Objectives for Policy Learning
- URL: http://arxiv.org/abs/2012.03130v1
- Date: Sat, 5 Dec 2020 22:10:25 GMT
- Title: Rejoinder: New Objectives for Policy Learning
- Abstract summary: I provide a rejoinder for discussion of "More Efficient Policy Learning via Optimal Retargeting" to appear in the Journal of the American Statistical Association.
Oliver Dukes and Stijn Vansteelandt; Sijia Li, Xiudi Li, and Alex Luedtkeand; and Muxuan Liang and Yingqi Zhao.
- Score: 73.15119871364834
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: I provide a rejoinder for discussion of "More Efficient Policy Learning via
Optimal Retargeting" to appear in the Journal of the American Statistical
Association with discussion by Oliver Dukes and Stijn Vansteelandt; Sijia Li,
Xiudi Li, and Alex Luedtkeand; and Muxuan Liang and Yingqi Zhao.
Related papers
- HistoLens: An LLM-Powered Framework for Multi-Layered Analysis of Historical Texts -- A Case Application of Yantie Lun [0.43512163406552007]
HistoLens is a multi-layered analysis framework for historical texts based on Large Language Models (LLMs)
HistoLens integrates NLP technology, including named entity recognition, knowledge graph construction, and geographic information visualization.
This paper showcases how HistoLens explores Western Han culture in "Yantie Lun" through multi-dimensional, visual, and quantitative methods.
arXiv Detail & Related papers (2024-11-15T06:21:13Z) - Real-Time Personalization for LLM-based Recommendation with Customized In-Context Learning [57.28766250993726]
This work explores adapting to dynamic user interests without any model updates.
Existing Large Language Model (LLM)-based recommenders often lose the in-context learning ability during recommendation tuning.
We propose RecICL, which customizes recommendation-specific in-context learning for real-time recommendations.
arXiv Detail & Related papers (2024-10-30T15:48:36Z) - GermanPartiesQA: Benchmarking Commercial Large Language Models for Political Bias and Sycophancy [20.06753067241866]
We evaluate and compare the alignment of six LLMs by OpenAI, Anthropic, and Cohere with German party positions.
We conduct our prompt experiment for which we use the benchmark and sociodemographic data of leading German parliamentarians.
arXiv Detail & Related papers (2024-07-25T13:04:25Z) - XAI meets LLMs: A Survey of the Relation between Explainable AI and Large Language Models [33.04648289133944]
Key challenges in Large Language Models (LLM) research focus on the importance of interpretability.
Driven by increasing interest from AI and business sectors, we highlight the need for transparency in LLMs.
Our paper advocates for a balanced approach that values interpretability equally with functional advancements.
arXiv Detail & Related papers (2024-07-21T19:23:45Z) - Benchmarking Chinese Commonsense Reasoning of LLMs: From Chinese-Specifics to Reasoning-Memorization Correlations [34.07537926291133]
CHARM is the first benchmark for comprehensively and in-depth evaluating the commonsense reasoning ability of large language models (LLMs) in Chinese.
We evaluated 7 English and 12 Chinese-oriented LLMs on CHARM.
Some LLMs struggle with memorizing Chinese commonsense, affecting their reasoning ability, while others show differences in reasoning despite similar performance.
arXiv Detail & Related papers (2024-03-21T03:52:01Z) - IOB: Integrating Optimization Transfer and Behavior Transfer for
Multi-Policy Reuse [50.90781542323258]
Reinforcement learning (RL) agents can transfer knowledge from source policies to a related target task.
Previous methods introduce additional components, such as hierarchical policies or estimations of source policies' value functions.
We propose a novel transfer RL method that selects the source policy without training extra components.
arXiv Detail & Related papers (2023-08-14T09:22:35Z) - Shuo Wen Jie Zi: Rethinking Dictionaries and Glyphs for Chinese Language
Pre-training [50.100992353488174]
We introduce CDBERT, a new learning paradigm that enhances the semantics understanding ability of the Chinese PLMs with dictionary knowledge and structure of Chinese characters.
We name the two core modules of CDBERT as Shuowen and Jiezi, where Shuowen refers to the process of retrieving the most appropriate meaning from Chinese dictionaries.
Our paradigm demonstrates consistent improvements on previous Chinese PLMs across all tasks.
arXiv Detail & Related papers (2023-05-30T05:48:36Z) - A Survey on In-context Learning [77.78614055956365]
In-context learning (ICL) has emerged as a new paradigm for natural language processing (NLP)
We first present a formal definition of ICL and clarify its correlation to related studies.
We then organize and discuss advanced techniques, including training strategies, prompt designing strategies, and related analysis.
arXiv Detail & Related papers (2022-12-31T15:57:09Z) - Discussion of Kallus (2020) and Mo, Qi, and Liu (2020): New Objectives
for Policy Learning [2.578242050187029]
We show that it is important to take the value function into account when working within the framework.
We describe more efficient approaches for leveraging data when learning distributionally robust policies.
arXiv Detail & Related papers (2020-10-09T21:05:01Z) - Efficient Deep Reinforcement Learning via Adaptive Policy Transfer [50.51637231309424]
Policy Transfer Framework (PTF) is proposed to accelerate Reinforcement Learning (RL)
Our framework learns when and which source policy is the best to reuse for the target policy and when to terminate it.
Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art policy transfer methods.
arXiv Detail & Related papers (2020-02-19T07:30:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.