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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/seanrsinclair/hindsight-learning

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Hindsight Learning for MDPs with Exogenous Inputs

https://github.com/sebastian-east/sos-imitation-learning

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Imitation Learning of Stabilizing Policies for Nonlinear Systems

https://github.com/sebastianrisi/ga-world-models

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Deep Neuroevolution of Recurrent and Discrete World Models

https://github.com/secondmind-labs/trieste

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Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow

https://github.com/sefaburakokcu/finn-quantized-yolo

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LPYOLO: Low Precision YOLO for Face Detection on FPGA

https://github.com/seongwoongcho/meta-controller

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Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control

https://github.com/sepehr-kamahi/faith

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Counterfactuals As a Means for Evaluating Faithfulness of Attribution Methods in Autoregressive Language Models

https://github.com/sephirex-x/latentdriver

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Learning Multiple Probabilistic Decisions from Latent World Model in Autonomous Driving

https://github.com/servicenow/context-is-key-forecasting

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Context is Key: A Benchmark for Forecasting with Essential Textual Information

https://github.com/sesarlab/ensemble-random-forest-robustness-against-poisoning

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On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based Approach

https://github.com/sgiddens/SAFES

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SAFES: Sequential Privacy and Fairness Enhancing Data Synthesis for Responsible AI

https://github.com/sgoodfriend/rl-algo-impls

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A Competition Winning Deep Reinforcement Learning Agent in microRTS

https://github.com/sgttwld/two-kinds-of-free-energy--notebooks

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The Two Kinds of Free Energy and the Bayesian Revolution

https://github.com/shadynasrat/rdmm

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RDMM: Fine-Tuned LLM Models for On-Device Robotic Decision Making with Enhanced Contextual Awareness in Specific Domains

https://github.com/shahin-01/vqa-ad

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Explaining Autonomous Driving Actions with Visual Question Answering

https://github.com/shakty/optimal-design

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Optimal design of experiments to identify latent behavioral types

https://github.com/shamilmamedov/flexible_arm

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Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots

https://github.com/shanghai-digital-brain-laboratory/bdm-db1

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On Realization of Intelligent Decision-Making in the Real World: A Foundation Decision Model Perspective

https://github.com/shaodaqian/DML-IV

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Learning Decision Policies with Instrumental Variables through Double Machine Learning

https://github.com/shaohuiliu/riskawarelearning_voltageopt_distgrid

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Risk-Aware Learning for Scalable Voltage Optimization in Distribution Grids

https://github.com/shaoqian12/ccil

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Imitating Cost-Constrained Behaviors in Reinforcement Learning

https://github.com/shaox192/NeuralGuidance

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Probing Human Visual Robustness with Neurally-Guided Deep Neural Networks

https://github.com/sharif1093/dextron

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End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning

https://github.com/shariqiqbal2810/alma

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ALMA: Hierarchical Learning for Composite Multi-Agent Tasks

https://github.com/sharonlevy/genderbiasscenarios

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Gender Bias in Decision-Making with Large Language Models: A Study of Relationship Conflicts

https://github.com/shashankg7/recsys2024_optimal_baseline

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Optimal Baseline Corrections for Off-Policy Contextual Bandits

https://github.com/shaunyuan22/cfinet

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Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning

https://github.com/shawlen/dtignn

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Modeling Network-level Traffic Flow Transitions on Sparse Data

https://github.com/sheltonliu-n/autodan

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AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models

https://github.com/shen-hhao/Category_Level_Manipulation

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Learning Category-Level Generalizable Object Manipulation Policy via Generative Adversarial Self-Imitation Learning from Demonstrations

https://github.com/shenghaowu/counterfactual-generative-models

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Counterfactual Generative Models for Time-Varying Treatments

https://github.com/shengroup/fedigw

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Harnessing the Power of Federated Learning in Federated Contextual Bandits

https://github.com/shengzhang37/SAVE

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Statistical Inference of the Value Function for Reinforcement Learning in Infinite Horizon Settings

https://github.com/shentao-yang/preference_grounded_guidance

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Preference-grounded Token-level Guidance for Language Model Fine-tuning

https://github.com/sherry-xu1995/ahntp

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Adaptive Hypergraph Network for Trust Prediction

https://github.com/sherrychen1120/MIDAS

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MIDAS: Multi-agent Interaction-aware Decision-making with Adaptive Strategies for Urban Autonomous Navigation

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