https://github.com/cobypenso/local-differential-private-conformal-prediction
공식 코드Privacy-Preserving Conformal Prediction Under Local Differential Privacy
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Privacy-Preserving Conformal Prediction Under Local Differential Privacy
Estimating the Conformal Prediction Threshold from Noisy Labels
Discounted Adaptive Online Learning: Towards Better Regularization
Forecasting CPI inflation under economic policy and geopolitical uncertainties
Robust Bayesian Optimization via Localized Online Conformal Prediction
Conformal Prediction for Image Segmentation Using Morphological Prediction Sets
Efficient Robust Conformal Prediction via Lipschitz-Bounded Networks
A Confidence Machine for Sparse High-Order Interaction Model
coverforest: Conformal Predictions with Random Forest in Python
Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)
Conformal time series decomposition with component-wise exchangeability
Conformal Prediction Regions for Time Series using Linear Complementarity Programming
Multi-View Conformal Learning for Heterogeneous Sensor Fusion
Multi-model Ensemble Conformal Prediction in Dynamic Environments
Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting
Conformalized Adaptive Forecasting of Heterogeneous Trajectories
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.