Papers with Code 아카이브
Multi-Armed Bandits
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 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/raminaghods/NATS
공식 코드Multi-Agent Active Search using Realistic Depth-Aware Noise Model
https://github.com/raymzhang/cts-mismatched-paradox
공식 코드Thompson Sampling For Combinatorial Bandits: Polynomial Regret and Mismatched Sampling Paradox
https://github.com/rchiso/mf-rmab
공식 코드https://github.com/rctzeng/neurips2023-cr
공식 코드Best Arm Identification with Fixed Budget: A Large Deviation Perspective
https://github.com/rgautron/DssatBanditEnv
공식 코드Optimal Thompson Sampling strategies for support-aware CVaR bandits
https://github.com/rinuboney/l2p-pommerman
공식 코드Learning to Play Imperfect-Information Games by Imitating an Oracle Planner
https://github.com/rjagerman/tois2019-safe-exploration-algorithm
공식 코드https://github.com/rubinxin/CoCaBO_code
공식 코드Bayesian Optimisation over Multiple Continuous and Categorical Inputs
https://github.com/rvignav/diffusion-tts
공식 코드Test-Time Scaling of Diffusion Models via Noise Trajectory Search
https://github.com/sabzer/distributed-bo
공식 코드Distributed Thompson sampling under constrained communication
https://github.com/saeedghoorchian/d-lints-rp
공식 코드Bayesian Non-stationary Linear Bandits for Large-Scale Recommender Systems
https://github.com/sail-sg/oat
공식 코드https://github.com/shashankg7/recsys2024_optimal_baseline
공식 코드Optimal Baseline Corrections for Off-Policy Contextual Bandits
https://github.com/shengroup/fedigw
공식 코드Harnessing the Power of Federated Learning in Federated Contextual Bandits
https://github.com/shengroup/mpmab_beacon
공식 코드Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization
https://github.com/shengroup/observe_then_incentivize
공식 코드(Almost) Free Incentivized Exploration from Decentralized Learning Agents
https://github.com/shidani/rankingcontextualbandits
공식 코드https://github.com/shiralab/opts
공식 코드Bandit-Based Prompt Design Strategy Selection Improves Prompt Optimizers
https://github.com/shirleydongj/StrategicCUCB
공식 코드https://github.com/shreyasc-13/correlated_bandits
공식 코드https://github.com/simonzhou86/tr_linucb
공식 코드https://github.com/sinanyildirim/smc_dp_adatr
공식 코드Differentially Private Online Bayesian Estimation With Adaptive Truncation
https://github.com/skadio/balans
공식 코드https://github.com/skezle/BADAM
공식 코드Practical Bayesian Learning of Neural Networks via Adaptive Optimisation Methods
https://github.com/skywalker6174/3d-isometry-robust
공식 코드On Isometry Robustness of Deep 3D Point Cloud Models under Adversarial Attacks
https://github.com/sorawitj/invariant-policy-learning
공식 코드https://github.com/st-tech/zr-obp
공식 코드Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model
https://github.com/stanfordai4hi/adaptive-interventions-with-goals
공식 코드Adaptive Interventions with User-Defined Goals for Health Behavior Change
https://github.com/statsdlmathsrecomsys/d-optimal-recommender-selection
공식 코드Towards the D-Optimal Online Experiment Design for Recommender Selection
https://github.com/sulgik/orts
공식 코드Odds-Ratio Thompson Sampling to Control for Time-Varying Effect
https://github.com/szrlee/GPT-HyperAgent
공식 코드https://github.com/szrlee/ensemble_plus_plus
공식 코드https://github.com/tavlab-iiitd/VacSIM
공식 코드VacSIM: Learning Effective Strategies for COVID-19 Vaccine Distribution using Reinforcement Learning
https://github.com/tavorb/adasimplicialdepth
공식 코드https://github.com/thanhnguyentang/offline_neural_bandits
공식 코드Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.