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Multi-Armed Bandits

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평가표: 1
보존된 평가 기록: 2

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/iurteaga/bandits

공식 코드

Bayesian bandits: balancing the exploration-exploitation tradeoff via double sampling

https://github.com/iurteaga/gp_ts_nlp

공식 코드

Multi-armed bandits for resource efficient, online optimization of language model pre-training: the use case of dynamic masking

https://github.com/jaayala/risk_aware_contextual_bandit

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Risk-Aware Continuous Control with Neural Contextual Bandits

https://github.com/jamie01713/ucblcb

공식 코드

From Restless to Contextual: A Thresholding Bandit Approach to Improve Finite-horizon Performance

https://github.com/jazzbob/stb

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Stacked Thompson Bandits

https://github.com/jd-applied-economics/WWW22-cvr-bandit

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Adaptive Experimentation with Delayed Binary Feedback

https://github.com/jedwards24/gittins

공식 코드

Practical Calculation of Gittins Indices for Multi-armed Bandits

https://github.com/jenna-fromer/qpo

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Batched Bayesian optimization by maximizing the probability of including the optimum

https://github.com/jhl-hust/bandhs

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Incorporating Multi-armed Bandit with Local Search for MaxSAT

https://github.com/jhwjhw0123/streaming-regret-minimization-mabs

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Tight Regret Bounds for Single-pass Streaming Multi-armed Bandits

https://github.com/jiaruzouu/prb

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PageRank Bandits for Link Prediction

https://github.com/jkomiyama/multiplaybanditlib

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Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays

https://github.com/joel-ql-chang/continuous-rho-ts

공식 코드

A Unifying Theory of Thompson Sampling for Continuous Risk-Averse Bandits

https://github.com/jsunn-y/SGPO

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Steering Generative Models with Experimental Data for Protein Fitness Optimization

https://github.com/juliancodaforno/meta-in-context-learning

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Meta-in-context learning in large language models

https://github.com/junghunkim7786/contextual_linear_bandits_under_noisy_features

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Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles

https://github.com/junghunkim7786/dynamicassortmentselectionpricing

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Dynamic Assortment Selection and Pricing with Censored Preference Feedback

https://github.com/junghunkim7786/queueing-matching-bandits-with-preference-feedback

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Queueing Matching Bandits with Preference Feedback

https://github.com/kazizzad/BDQN-MxNet-Gluon

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Efficient Exploration through Bayesian Deep Q-Networks

https://github.com/keraJLi/synthetic-gymnax

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Discovering Minimal Reinforcement Learning Environments

https://github.com/khashayarkhv/many-armed-bandit

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The Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms

https://github.com/khtae8250/falcon

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Falcon: Fair Active Learning using Multi-armed Bandits

https://github.com/killian-34/MAIQL_and_LPQL

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Q-Learning Lagrange Policies for Multi-Action Restless Bandits

https://github.com/kishanpb/offpolicyevaluation_informationborrowing_contextswitching

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Off-Policy Evaluation Using Information Borrowing and Context-Based Switching

https://github.com/kstoneriv3/confounding-robust-inference

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A Convex Framework for Confounding Robust Inference

https://github.com/kstoneriv3/confounding-robust-inference-old

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Kernel Conditional Moment Constraints for Confounding Robust Inference

https://github.com/kutuz4/implicitlynormalizedforecasterwithclipping

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Implicitly normalized forecaster with clipping for linear and non-linear heavy-tailed multi-armed bandits

https://github.com/lamda-bbo/madac

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Multi-agent Dynamic Algorithm Configuration

https://github.com/lgordon99/heterogeneous-stochastic-bandits

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Combining Diverse Information for Coordinated Action: Stochastic Bandit Algorithms for Heterogeneous Agents

https://github.com/liangzp/LDP-Bandit

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Generalized Linear Bandits with Local Differential Privacy

https://github.com/liangzp/dp-streaming-sco

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On Private Online Convex Optimization: Optimal Algorithms in $\ell_p$-Geometry and High Dimensional Contextual Bandits

https://github.com/licong-lin/in-context-rl

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Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining

https://github.com/likelyt/double-matching

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Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences Constraints

https://github.com/lily-x/dual-mandate

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Dual-Mandate Patrols: Multi-Armed Bandits for Green Security

https://github.com/lily-x/online-rmab

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Optimistic Whittle Index Policy: Online Learning for Restless Bandits

https://github.com/liuzy0708/Awesome_OL

공식 코드

Performance-bounded Online Ensemble Learning Method Based on Multi-armed bandits and Its Applications in Real-time Safety Assessment

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