https://github.com/aiueola/webconf2024-slate-ope-via-abstraction
공식 코드Off-Policy Evaluation of Slate Bandit Policies via Optimizing Abstraction
Papers with Code 아카이브
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/aiueola/wsdm2022-cascade-dr
공식 코드Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model
https://github.com/akagrecha/Neurips-2019-simulations
공식 코드Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards
https://github.com/akagrecha/constrained_regret_minimization
공식 코드Constrained regret minimization for multi-criterion multi-armed bandits
https://github.com/akshaykr/oracle_cb
공식 코드https://github.com/albertometelli/stochastic-rising-bandits
공식 코드https://github.com/alexandertornede/online_as
공식 코드Machine Learning for Online Algorithm Selection under Censored Feedback
https://github.com/alihanhyk/invconban
공식 코드Inverse Contextual Bandits: Learning How Behavior Evolves over Time
https://github.com/alimama-creative/AES-Adaptive-and-Efficient-ad-creative-Selection
공식 코드Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure
https://github.com/alimama-creative/AutoCO
공식 코드https://github.com/amirbalef/PS_MOMAB
공식 코드https://github.com/angie-lab-jhu/neuralvarlinucb
공식 코드Variance-Aware Linear UCB with Deep Representation for Neural Contextual Bandits
https://github.com/anndvision/vsop
공식 코드ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages
https://github.com/ansebi/wain22_sim_env
공식 코드Simulated Contextual Bandits for Personalization Tasks from Recommendation Datasets
https://github.com/armmn/Smoothness-Adaptive-Contextual-Bandits
공식 코드https://github.com/arpita8/Estimation-of-Warfarin-Dosage
공식 코드https://github.com/arunv3rma/NeurIPS-2019
공식 코드Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback
https://github.com/aryandeshwal/MerCBO
공식 코드Mercer Features for Efficient Combinatorial Bayesian Optimization
https://github.com/aryandeshwal/bops
공식 코드https://github.com/ashutoshaay26/Relational-Boosted-Bandits
공식 코드https://github.com/aslanides/aixijs
공식 코드https://github.com/b-castleman/hierarchical-mab-tutoring
공식 코드https://github.com/baekjin-kim/NonstationaryLB
공식 코드Randomized Exploration for Non-Stationary Stochastic Linear Bandits
https://github.com/bagavi/Meddit
공식 코드https://github.com/banyikun/KDD2021_MuFasa
공식 코드Multi-facet Contextual Bandits: A Neural Network Perspective
https://github.com/banyikun/ee-net-iclr-2022
공식 코드EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits
https://github.com/bilkent-cyborg/escada
공식 코드ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine
https://github.com/biyonka/mixture_adaptive_design
공식 코드An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits
https://github.com/borealisai/cbhf
공식 코드https://github.com/borealisai/latent-bottlenecked-anp
공식 코드https://github.com/buseskorkmaz/arteo
공식 코드Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO Algorithm
https://github.com/bvarici/causal-bandits-linear-sem
공식 코드https://github.com/caisr-hh/combineuserenvironment
공식 코드A New Bandit Setting Balancing Information from State Evolution and Corrupted Context
https://github.com/chang-li/MergeDTS
공식 코드MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation
https://github.com/chanlaw/assistive-bandits
공식 코드https://github.com/chaoukia/thompson-sampling-decision-trees
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.