automl / lcpfn
☆10Updated 3 weeks ago
Related projects: ⓘ
- A learning curve benchmark on OpenML data☆29Updated 3 years ago
- [ICLR2024] Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How☆25Updated 3 months ago
- A framework for QuickTune☆11Updated this week
- The official implementation of PFNs4BO: In-Context Learning for Bayesian Optimization☆22Updated 6 months ago
- Code for the Population-Based Bandits Algorithm, presented at NeurIPS 2020.☆20Updated 3 years ago
- [ICLR 2023] Deep Ranking Ensembles for Hyperparameter Optimization☆13Updated 5 months ago
- Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control☆61Updated 4 months ago
- Our maintained PFN repository. Come here to train SOTA PFNs.☆43Updated 3 months ago
- ☆21Updated last year
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆39Updated 10 months ago
- Model-agnostic posthoc calibration without distributional assumptions☆42Updated 11 months ago
- Code accompanying paper: Meta-Learning to Improve Pre-Training☆36Updated 2 years ago
- Official PyTorch implementation of "Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error"☆30Updated last year
- Adaptive and Reliable Classification: efficient conformity scores for multi-class classification problems☆31Updated last year
- Contains code for the NeurIPS 2020 paper by Pan et al., "Continual Deep Learning by FunctionalRegularisation of Memorable Past"☆43Updated 3 years ago
- ☆40Updated last year
- Explores the ideas presented in Deep Ensembles: A Loss Landscape Perspective (https://arxiv.org/abs/1912.02757) by Stanislav Fort, Huiyi …☆61Updated 4 years ago
- The first collection of surrogate benchmarks for Joint Architecture and Hyperparameter Search.☆15Updated last year
- Official repository for the paper "Zero-Shot AutoML with Pretrained Models"☆41Updated 8 months ago
- Official code for our paper "einspace: Searching for Neural Architectures from Fundamental Operations"☆20Updated 3 months ago
- Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".☆34Updated 2 years ago
- Laplace Redux -- Effortless Bayesian Deep Learning☆35Updated last year
- ☆19Updated 2 years ago
- [KDD 2023] Deep Pipeline Embeddings for AutoML☆15Updated 4 months ago
- ☆32Updated 11 months ago
- Official repository for "Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars" (NeurIPS 2023)☆14Updated 10 months ago
- Coresets via Bilevel Optimization☆66Updated 3 years ago
- Code for the paper "Bayesian Neural Network Priors Revisited"☆55Updated 3 years ago
- Model Zoos published at the NeurIPS 2022 Dataset & Benchmark track: "Model Zoos: A Dataset of Diverse Populations of Neural Network Model…☆51Updated last year
- Deep Learning & Information Bottleneck☆45Updated last year