Conformal Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 0
- 보존된 평가 기록
- 0
Conformal Prediction is a machine learning framework that provides valid measures of confidence for individual predictions. It offers a principled approach to quantify uncertainty in predictions without assuming any specific distribution for the data. This section features papers that explore various aspects of conformal prediction, including theoretical advancements, algorithmic developments, and applications across different domains.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AIgen/QOOB
공식 코드Nested conformal prediction and quantile out-of-bag ensemble methods
https://github.com/Annie983284450-1/CPGapBandit
공식 코드https://github.com/Annie983284450-1/NeuralLCB_C
공식 코드https://github.com/Baran-phys/DynamicFormer
공식 코드https://github.com/ElSacho/MVCS
공식 코드https://github.com/EtashGuha/R2CCP
공식 코드https://github.com/EugeneNdiaye/homotopy_conformal_prediction
공식 코드Computing Full Conformal Prediction Set with Approximate Homotopy
https://github.com/EugeneNdiaye/rootCP
공식 코드Root-finding Approaches for Computing Conformal Prediction Set
https://github.com/EugeneNdiaye/stable_conformal_prediction
공식 코드https://github.com/Gaochenyin/SurrConformalDR
공식 코드On the Role of Surrogates in Conformal Inference of Individual Causal Effects
https://github.com/HuayuLiArizona/Conformalized-Multiple-Instance-Learning-For-MedTS
공식 코드https://github.com/Jayaos/TCPTS
공식 코드https://github.com/JialeHan22/Conformal-Online-Auction-Design
공식 코드https://github.com/JomaMinoza/Ocean-Floor-Mapping-with-Uncertainty-Aware-Deep-Learning
공식 코드Learning Enhanced Structural Representations with Block-Based Uncertainties for Ocean Floor Mapping
https://github.com/KiljaeL/LOO-StabCP
공식 코드https://github.com/Monoxido45/CSI
공식 코드Distribution-Free Calibration of Statistical Confidence Sets
https://github.com/Monoxido45/EPICSCORE
공식 코드Epistemic Uncertainty in Conformal Scores: A Unified Approach
https://github.com/MrJUNHUIZHANG/CBF_NN_inference
공식 코드Safety-Critical Control with Offline-Online Neural Network Inference
https://github.com/NKI-AI/kandinsky-calibration
공식 코드Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithms
https://github.com/Networks-Learning/improve-expert-predictions-conformal-prediction
공식 코드https://github.com/Networks-Learning/towards-human-ai-complementarity-predictions-sets
공식 코드https://github.com/OliverHennhoefer/cross-conformal-anomaly-detection
공식 코드Uncertainty Quantification in Anomaly Detection with Cross-Conformal $p$-Values
https://github.com/SAIDS-Lab/Robust-Conformal-Prediction-for-STL-Runtime-Verification-under-Distribution-Shift
공식 코드Robust Conformal Prediction for STL Runtime Verification under Distribution Shift
https://github.com/Shai128/pcp
공식 코드https://github.com/Shai128/ui
공식 코드Conformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weighting
https://github.com/TalSchuster/CATs
공식 코드Consistent Accelerated Inference via Confident Adaptive Transformers
https://github.com/Zhendong-Wang/Probabilistic-Conformal-Prediction
공식 코드Probabilistic Conformal Prediction Using Conditional Random Samples
https://github.com/aangelopoulos/conformal-classification
공식 코드Uncertainty Sets for Image Classifiers using Conformal Prediction
https://github.com/aangelopoulos/conformal-prediction
공식 코드A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
https://github.com/aangelopoulos/conformal-risk
공식 코드https://github.com/aangelopoulos/conformal-time-series
공식 코드https://github.com/aangelopoulos/conformal_classification
공식 코드Uncertainty Sets for Image Classifiers using Conformal Prediction
https://github.com/aangelopoulos/im2im-uq
공식 코드https://github.com/aangelopoulos/online-conformal-decaying
공식 코드https://github.com/aangelopoulos/private_prediction_sets
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.