AIgen / df-posthoc-calibrationLinks
Model-agnostic posthoc calibration without distributional assumptions
☆42Updated last year
Alternatives and similar repositories for df-posthoc-calibration
Users that are interested in df-posthoc-calibration are comparing it to the libraries listed below
Sorting:
- ☆109Updated 2 years ago
- Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control☆67Updated 7 months ago
- Welcome to Uncertainty Metrics! The goal of this library is to provide an easy-to-use interface for both measuring uncertainty across Goo…☆24Updated 4 years ago
- A benchmark for distribution shift in tabular data☆54Updated last year
- Calibration library and code for the paper: Verified Uncertainty Calibration. Ananya Kumar, Percy Liang, Tengyu Ma. NeurIPS 2019 (Spotlig…☆150Updated 2 years ago
- Reusable BatchBALD implementation☆79Updated last year
- A repo for transfer learning with deep tabular models☆104Updated 2 years ago
- Code for the paper "Calibrating Deep Neural Networks using Focal Loss"☆160Updated last year
- Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)☆77Updated 3 years ago
- NumPy library for calibration metrics☆73Updated 4 months ago
- Code for Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty☆142Updated 2 years ago
- Code for "On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty".☆112Updated 3 years ago
- Repository for the NeurIPS 2023 paper "Beyond Confidence: Reliable Models Should Also Consider Atypicality"☆13Updated last year
- Active and Sample-Efficient Model Evaluation☆24Updated last month
- Conformal prediction for controlling monotonic risk functions. Simple accompanying PyTorch code for conformal risk control in computer vi…☆67Updated 2 years ago
- Combating hidden stratification with GEORGE☆63Updated 4 years ago
- Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"☆35Updated last year
- Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning (AISTATS 2022 Oral)☆41Updated 2 years ago
- An Empirical Framework for Domain Generalization In Clinical Settings☆31Updated 3 years ago
- Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true cla…☆242Updated 2 years ago
- Wrap around any model to output differentially private prediction sets with finite sample validity on any dataset.☆18Updated last year
- Code implementation of our ICLR'21 paper "Calibration of Neural Networks using Splines"☆22Updated 2 years ago
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆42Updated last year
- ☆37Updated 4 years ago
- Last-layer Laplace approximation code examples☆82Updated 3 years ago
- For calculating Shapley values via linear regression.☆68Updated 4 years ago
- ☆32Updated 7 years ago
- Repository for Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification (NeurIPS 2024)☆43Updated 7 months ago
- Code for experiments to learn uncertainty☆30Updated 2 years ago
- Quantile risk minimization☆24Updated 11 months ago