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/feji3769/VecchiaBO
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes
https://github.com/finn-no/recsys_slates_dataset
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling
https://github.com/instadeepai/catx
https://github.com/irskid5/drr_restaurants
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
https://github.com/ishank-juneja/Correlated-AoI-Bandits
https://github.com/jehankairasvakharia/Santa_2020_Jehan
The Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms
https://github.com/jparkerholder/ASEBO
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization
https://github.com/kevin-yu-0602/NeuralUCB_257
https://github.com/kingcong/models
Locally Differentially Private (Contextual) Bandits Learning
https://github.com/kirthevasank/gp-parallel-ts
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling
https://github.com/kirthevasank/mps
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming
https://github.com/ksetdekov/trip_choice_optimizer
https://github.com/lasgroup/alexp
https://github.com/leoguelman/BLBF
https://github.com/lily-x/rankedCUCB
Dual-Mandate Patrols: Multi-Armed Bandits for Green Security
https://github.com/maxqyzhu/TS_for_mean_variance_bandit
https://github.com/microsoft/csrobust
https://github.com/mindspore-ai/models/tree/master/research/rl/ldp_linucb
Locally Differentially Private (Contextual) Bandits Learning
https://github.com/mrateike/fair_minimonster
Equal Opportunity in Online Classification with Partial Feedback
https://github.com/nick-jhlee/logistic_bandit
https://github.com/niravnb/Movie-Recommendation-using-Cascading-Bandits
https://github.com/ofirnabati/neural-linear-bandits-with-likelihood-matching
Online Limited Memory Neural-Linear Bandits with Likelihood Matching
https://github.com/oh-lab/UTE-Uncertainty-aware-Temporal-Extension-
Randomized Value Functions via Multiplicative Normalizing Flows
https://github.com/otmhi/offpolicy_ls
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting
https://github.com/pwc-1/Paper-9/tree/main/5/vits
VITS : Variational Inference Thompson Sampling for contextual bandits
https://github.com/rajatsen91/XtremeContextualBandits
https://github.com/renan-cunha/Bandits
Adapting multi-armed bandits policies to contextual bandits scenarios
https://github.com/roysaptaumich/HD-bandit-codes
Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits
https://github.com/rubinxin/CoCaBO
Bayesian Optimisation over Multiple Continuous and Categorical Inputs
https://github.com/saum-g/quantum-bai
https://github.com/sauxpa/neural_exploration
https://github.com/sb-ai-lab/RePlay
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
https://github.com/shashist/recsys-rl
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
https://github.com/shobigrock/online-matching
Online Matching: A Real-time Bandit System for Large-scale Recommendations
https://github.com/tatsu432/DR-estimator-OPE-large-action
Off-Policy Evaluation for Large Action Spaces via Embeddings
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.