Results 81 to 90 of about 497,803 (209)
Online‐optimized RAG for tool use and function calling
Decision Sciences, EarlyView.Abstract In many applications, retrieval‐augmented generation (RAG) drives tool use and function calling by embedding the (user) queries and matching them to prespecified tool/function descriptions. In this article, we address an embedding misalignment issue that often arises in practical applications due to imperfect embedding models or noisy ...
Yu Pan, Xiaocheng Li, Hanzhao Wang
wiley +1 more source
Random Forest for the Contextual Bandit Problem
, 2020 To address the contextual bandit problem, we propose an online random forest algorithm. The analysis of the proposed algorithm is based on the sample complexity needed to find the optimal decision stump.
Robin Allesiardo +3 more
core
Empirical Study on Contextual Bandit Algorithms with Neural Processes
, 2022 Graduate School of Artificial IntelligenceMulti-armed bandit is a well-formulated test bed for designing sequential decision-making algorithms that deal with the exploration-exploitation dilemma.
Park, Junhyun
core
Evidence Gathering Under Competitive and Noncompetitive Rewards
Journal of Economics &Management Strategy, EarlyView.ABSTRACT Reward schemes may affect not only agents' effort but also their incentives to gather information in order to reduce the riskiness of the productive activity. In a laboratory experiment using a novel task, we find that the relationship between incentives and evidence gathering depends critically on the availability of information about peers ...
Philip Brookins +2 more
wiley +1 more source
Making Boundaries: Polish Memory, Identity and the Ukrainian Other
Nations and Nationalism, EarlyView.ABSTRACT Political elites use historical references abundantly when drawing national boundaries. Despite the prominence of such language, we know little about the ways in which ordinary citizens internalise historical narratives of the Other to construct their national identity.
Félix Krawatzek
wiley +1 more source
Conservative Contextual Linear Bandits
CoRR, 2016 Safety is a desirable property that can immensely increase the applicability of learning algorithms in real-world decision-making problems. It is much easier for a company to deploy an algorithm that is safe, i.e., guaranteed to perform at least as well as a baseline.
Abbas Kazerouni +3 more
openaire +3 more sources
Context Attentive Bandits: Contextual Bandit with Restricted Context [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 We consider a novel formulation of the multi-armed bandit model, which we call the contextual bandit with restricted context, where only a limited number of features can be accessed by the learner at every iteration. This novel formulation is motivated by different online problems arising in clinical trials, recommender systems and attention modeling ...
Djallel Bouneffouf 0001 +3 more
openaire +4 more sources
Active Learning for Streaming Data in A Contextual Bandit Framework
, 2019 Contextual bandit algorithms have been shown to be effective in solving sequential decision making problems under uncertainty. A common assumption in the literature is that the realized (ground truth) reward is observed by the learner at no cost, which ...
Xu, Jie +5 more
core +1 more source
Permission to Dream: The Marxist Ban on Imagining Utopia Reconsidered
Constellations, EarlyView.
Paula Keller
wiley +1 more source
The Enemy Who Never Went Away: Memory, Nationalism and China's Japan Policy During the Cold War
Nations and Nationalism, EarlyView.ABSTRACT This study reassesses the relationship between war memory, nationalism and Sino–Japanese relations during the Cold War. Contrary to the dominant view in existing scholarship that China's nationalist remembering of the war emerged during the 1980s and 1990s, this article, drawing on an extensive survey of the People's Daily, demonstrates that ...
Yi Wang
wiley +1 more source