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Decision Making

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/russellyq/medhalltune

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MedHallTune: An Instruction-Tuning Benchmark for Mitigating Medical Hallucination in Vision-Language Models

https://github.com/rutgerswiselab/NCR

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Neural Collaborative Reasoning

https://github.com/rvimlab/endoscopy

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Deep Homography Estimation in Dynamic Surgical Scenes for Laparoscopic Camera Motion Extraction

https://github.com/rvimlab/homography_imitation_learning

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Deep Homography Prediction for Endoscopic Camera Motion Imitation Learning

https://github.com/rwickman/sparrl-pytorch

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A Generic Graph Sparsification Framework using Deep Reinforcement Learning

https://github.com/rxian/fair-classification

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Fair and Optimal Classification via Post-Processing

https://github.com/ryanchankh/VariationalInformationPursuit

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Variational Information Pursuit for Interpretable Predictions

https://github.com/ryansteed/heuristic-moral-machine

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Heuristic-Based Weak Learning for Automated Decision-Making

https://github.com/ryanxhr/dwbc

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Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations

https://github.com/ryojitanabe/prefqi

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Quality Indicators for Preference-based Evolutionary Multi-objective Optimization Using a Reference Point: A Review and Analysis

https://github.com/rzayanov/irl-teaching-limited-feedback

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Interactively Teaching an Inverse Reinforcement Learner with Limited Feedback

https://github.com/s-a-barnett/bayesian-persuasion

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A pragmatic account of the weak evidence effect

https://github.com/s-abdelnabi/llm-deliberation

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Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation

https://github.com/s-m-asjad/ego4d_vq2d

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Bayesian Decision Making to Localize Visual Queries in 2D

https://github.com/s-marton/sympol

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Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization

https://github.com/saccharomycetes/text-based-traffic-understanding

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A Study of Situational Reasoning for Traffic Understanding

https://github.com/sacs-epfl/fairness-audits-with-collaboration

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Fairness Auditing with Multi-Agent Collaboration

https://github.com/saeedghoorchian/d-lints-rp

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Bayesian Non-stationary Linear Bandits for Large-Scale Recommender Systems

https://github.com/saeedghoorchian/ncc-bandits

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Online Learning with Costly Features in Non-stationary Environments

https://github.com/saeedmarzban/wavecorr

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WaveCorr: Correlation-savvy Deep Reinforcement Learning for Portfolio Management

https://github.com/saferl-lab/safe-marl-in-autonomous-driving

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Safe Multi-Agent Reinforcement Learning with Bilevel Optimization in Autonomous Driving

https://github.com/saferoboticslab/opinion_game

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Emergent Coordination through Game-Induced Nonlinear Opinion Dynamics

https://github.com/sagar-pa/crystalbox

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CrystalBox: Future-Based Explanations for Input-Driven Deep RL Systems

https://github.com/sahandfer/manipulation-susceptibility

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Human Decision-making is Susceptible to AI-driven Manipulation

https://github.com/saifkhichi96/situational-llm

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SituationalLLM: Proactive language models with scene awareness for dynamic, contextual task guidance

https://github.com/sail-sg/ild

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Imitation Learning via Differentiable Physics

https://github.com/sail-sg/patchail

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Visual Imitation Learning with Patch Rewards

https://github.com/sailrit/risk-averse-td-learning

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Risk-Averse Reinforcement Learning: An Optimal Transport Perspective on Temporal Difference Learning

https://github.com/saimihirj/NKC-Multi-Agent-Models

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Modeling AI-Human Collaboration as a Multi-Agent Adaptation

https://github.com/saitcakmak/BoRisk

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Bayesian Optimization of Risk Measures

https://github.com/sajjadmzf/earlylcpred

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Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-based Convolutional Neural Networks

https://github.com/sakshiudeshi/Aequitas

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Automated Directed Fairness Testing

https://github.com/sakshiudeshi/Neo

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Model Agnostic Defence against Backdoor Attacks in Machine Learning

https://github.com/salesforce/bolaa

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BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents

https://github.com/salesforce/omnixai

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OmniXAI: A Library for Explainable AI

https://github.com/salesforce/strategically-robust-ai

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Learning to Play General-Sum Games Against Multiple Boundedly Rational Agents

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.