Papers with Code 아카이브

Decision Making

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

2

보존된 평가 기록

2

코드

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

https://github.com/stanfordmlgroup/cdr-mimic

공식 코드

Countdown Regression: Sharp and Calibrated Survival Predictions

https://github.com/stanfordvl/mini_behavior

공식 코드

Mini-BEHAVIOR: A Procedurally Generated Benchmark for Long-horizon Decision-Making in Embodied AI

https://github.com/stanl1y/tdil

공식 코드

Expert Proximity as Surrogate Rewards for Single Demonstration Imitation Learning

https://github.com/stanlazic/ML_uncertainty_quantification

공식 코드

Quantifying sources of uncertainty in drug discovery predictions with probabilistic models

https://github.com/start2020/awesome-timeseries-llm-fm

공식 코드

A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model

https://github.com/statmlben/dnn-inference

공식 코드

Significance tests of feature relevance for a black-box learner

https://github.com/statmlben/dnn-locate

공식 코드

Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed Interpretation

https://github.com/stealth-py/ultimatumtom

공식 코드

Effects of Theory of Mind and Prosocial Beliefs on Steering Human-Aligned Behaviors of LLMs in Ultimatum Games

https://github.com/stelios30/aamas23-responsibility-attribution-mcts

공식 코드

Towards Computationally Efficient Responsibility Attribution in Decentralized Partially Observable MDPs

https://github.com/stelios30/agent-specific-effects

공식 코드

Agent-Specific Effects: A Causal Effect Propagation Analysis in Multi-Agent MDPs

https://github.com/stelios30/cf-effect-decomposition

공식 코드

Counterfactual Effect Decomposition in Multi-Agent Sequential Decision Making

https://github.com/stephaniedroop/av_explanations

공식 코드

People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AI

https://github.com/stephba/diff-fnn

공식 코드

Differentiable Fuzzy Neural Networks for Recommender Systems

https://github.com/stephlong614/causal-disco

공식 코드

Causal Discovery with Language Models as Imperfect Experts

https://github.com/stepjam/PyRep

공식 코드

PyRep: Bringing V-REP to Deep Robot Learning

https://github.com/stepjam/RLBench

공식 코드

RLBench: The Robot Learning Benchmark & Learning Environment

https://github.com/stepjam/TecNets

공식 코드

Task-Embedded Control Networks for Few-Shot Imitation Learning

https://github.com/stevenboys/cigl

공식 코드

Calibrating the Rigged Lottery: Making All Tickets Reliable

https://github.com/stevenyangyj/emma-alfworld

공식 코드

Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld

https://github.com/stevezhangza/adverserial_density_regression

공식 코드

Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted Regression

https://github.com/sthowling/alioth

공식 코드

Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy Applications in Public Cloud

https://github.com/stratosphereips/netsecgame

공식 코드

Out of the Cage: How Stochastic Parrots Win in Cyber Security Environments

https://github.com/stxyfu/stress_gnn

공식 코드

An FEA surrogate model with Boundary Oriented Graph Embedding approach

https://github.com/success-muri/counterfactual_human_irl

공식 코드

Reasoning about Counterfactuals to Improve Human Inverse Reinforcement Learning

https://github.com/success-muri/counterfactual_human_irl_study

공식 코드

Reasoning about Counterfactuals to Improve Human Inverse Reinforcement Learning

https://github.com/sufe-aiflm-lab/fin-r1

공식 코드

Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning

https://github.com/sujoyp/subgoal-discovery

공식 코드

Learning from Trajectories via Subgoal Discovery

https://github.com/sul217/MOO_Fairness

공식 코드

Accuracy and Fairness Trade-offs in Machine Learning: A Stochastic Multi-Objective Approach

https://github.com/sul217/SAfairKM

공식 코드

A Stochastic Alternating Balance $k$-Means Algorithm for Fair Clustering

https://github.com/sulam-group/shaplit

공식 코드

SHAP-XRT: The Shapley Value Meets Conditional Independence Testing

https://github.com/suletekkesinoglu/xai_llm_rag

공식 코드

From Feature Importance to Natural Language Explanations Using LLMs with RAG

https://github.com/sullychen/smolk

공식 코드

Sparse learned kernels for interpretable and efficient medical time series processing

https://github.com/sumonbis/ML-Fairness

공식 코드

Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness

https://github.com/sumonbis/farify

공식 코드

Fairify: Fairness Verification of Neural Networks

https://github.com/sumugit/fastdcflow

공식 코드

Model-Based Counterfactual Explanations Incorporating Feature Space Attributes for Tabular Data

https://github.com/sun-hailong/cvpr24-ease

공식 코드

Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning

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