datamllab / autokaggle
Automated Machine Learning (AutoML) for Kaggle Competition
☆32Updated last year
Related projects ⓘ
Alternatives and complementary repositories for autokaggle
- An AutoML pipeline selection system to quickly select a promising pipeline for a new dataset.☆82Updated 3 years ago
- ☆124Updated 3 years ago
- ☆42Updated 4 years ago
- How to calibrate your neural network classifier: Getting accurate probabilities from a classification model☆52Updated 4 years ago
- ☆49Updated last year
- Data and code related to the paper "Probabilistic matrix factorization for automated machine learning", NIPS, 2018.☆40Updated 2 years ago
- Feature Interaction Interpretability via Interaction Detection☆34Updated last year
- Supervised Local Modeling for Interpretability☆28Updated 6 years ago
- ☆26Updated 5 years ago
- This repository contains the full code for the "Towards fairness in machine learning with adversarial networks" blog post.☆117Updated 3 years ago
- (ICML 2021) Mandoline: Model Evaluation under Distribution Shift☆31Updated 3 years ago
- code for https//research.fb.com/publications/towards-automated-neural-interaction-discovery-for-click-through-rate-prediction/☆17Updated 3 years ago
- AutoBazaar: An AutoML System from the Machine Learning Bazaar☆32Updated 3 years ago
- PyTorch code for WWW 19 paper: On Attribution of Recurrent Neural Network Predictions via Additive Decomposition☆10Updated 3 years ago
- Deep Neural Decision Trees☆159Updated 2 years ago
- Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University☆45Updated last year
- AutoGBT is used for AutoML in a lifelong machine learning setting to classify large volume high cardinality data streams under concept-dr…☆114Updated 4 years ago
- Python codes for weakly-supervised learning☆122Updated 4 years ago
- To Trust Or Not To Trust A Classifier. A measure of uncertainty for any trained (possibly black-box) classifier which is more effective t…☆173Updated last year
- Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks [NeurIPS 2019]☆50Updated 4 years ago
- This is a public collection of papers related to machine learning model interpretability.☆25Updated 2 years ago
- GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model’s Prediction. Thai Le, Suhang Wang, Dongwon …☆22Updated 3 years ago
- Codebase for "Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains Its Predictions" (to appear in AA…☆73Updated 7 years ago
- Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"☆27Updated 3 years ago
- Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)☆125Updated 3 years ago
- Code and data for the experiments in "On Fairness and Calibration"☆50Updated 2 years ago
- ☆32Updated 3 years ago
- Code for the NeurIPS 2018 paper "On Controllable Sparse Alternatives to Softmax"☆22Updated 5 years ago
- Tensorflow implementation of a Tree☆36Updated 5 years ago
- Public solution for AutoSeries competition☆72Updated 4 years ago