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Multi-Armed Bandits
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Multi-armed bandits refer to a task where a fixed amount of resources must be allocated between competing resources that maximizes expected gain. Typically these problems involve an exploration/exploitation trade-off.
<span style="color:grey; opacity: 0.6">( Image credit: Microsoft Research )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/chicagohai/hypothesis_generation
공식 코드https://github.com/corl-team/headless-ad
공식 코드In-Context Reinforcement Learning for Variable Action Spaces
https://github.com/crherlihy/networked_restless_bandits
공식 코드https://github.com/criteo-research/efficient-kernel-ucb
공식 코드https://github.com/csdlpsu/qpots
공식 코드qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson sampling
https://github.com/csml-iit-ucl/gmfbandits
공식 코드https://github.com/daizhongxiang/Federated_Bayesian_Optimization
공식 코드https://github.com/daizhongxiang/federated-neural-bandits
공식 코드https://github.com/daizhongxiang/meta-bo
공식 코드https://github.com/daizhongxiang/sto-bnts
공식 코드https://github.com/damaru2/decentralized-bandits
공식 코드https://github.com/david-cortes/contextualbandits
공식 코드Adapting multi-armed bandits policies to contextual bandits scenarios
https://github.com/david-lindner/single-peaked-bandits
공식 코드Addressing the Long-term Impact of ML Decisions via Policy Regret
https://github.com/davidbrandfonbrener/deep-offline-bandits
공식 코드https://github.com/davidrey123/vaccine_allocation
공식 코드Vaccine allocation policy optimization and budget sharing mechanism using Thompson sampling
https://github.com/deepmind/deepmind-research/tree/master/gated_linear_networks
공식 코드https://github.com/deepmind/enn_acme
공식 코드https://github.com/deepmind/offpolicy_selection_eslb
공식 코드Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting
https://github.com/deezer/carousel_bandits
공식 코드Carousel Personalization in Music Streaming Apps with Contextual Bandits
https://github.com/deezer/new-releases-ecir2024
공식 코드Let's Get It Started: Fostering the Discoverability of New Releases on Deezer
https://github.com/denysgerasymuk799/virny-flow
공식 코드https://github.com/devzhk/lmcts
공식 코드https://github.com/dimarkov/aibandits
공식 코드An empirical evaluation of active inference in multi-armed bandits
https://github.com/doerlbh/BanditZoo
공식 코드Evolutionary Multi-Armed Bandits with Genetic Thompson Sampling
https://github.com/doerlbh/BerlinUCB
공식 코드Online Semi-Supervised Learning in Contextual Bandits with Episodic Reward
https://github.com/doerlbh/dilemmaRL
공식 코드Online Learning in Iterated Prisoner's Dilemma to Mimic Human Behavior
https://github.com/doerlbh/mentalRL
공식 코드Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL
https://github.com/dotbielefeld/acband
공식 코드AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration
https://github.com/dtak/power-constrained-bandits-public
공식 코드https://github.com/duykhuongnguyen/laser-mab
공식 코드LASeR: Learning to Adaptively Select Reward Models with Multi-Armed Bandits
https://github.com/eastonhuch/rome
공식 코드RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health Interventions
https://github.com/eboursier/sic-mmab
공식 코드SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits
https://github.com/edouardfouche/G-NS-MAB
공식 코드Finite-time Analysis of Globally Nonstationary Multi-Armed Bandits
https://github.com/elise-crepon/sequential-pareto-learning-experiments
공식 코드Sequential Learning of the Pareto Front for Multi-objective Bandits
https://github.com/extremebandits/extremebandits_submission
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.