csinva / disentangled-attribution-curves
Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"
☆27Updated 4 years ago
Alternatives and similar repositories for disentangled-attribution-curves:
Users that are interested in disentangled-attribution-curves are comparing it to the libraries listed below
- Investigate the speed of adaptation of structural causal models☆16Updated 4 years ago
- Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding☆22Updated 2 years ago
- Feature Interaction Interpretability via Interaction Detection☆34Updated last year
- ☆29Updated 6 years ago
- (ICML 2021) Mandoline: Model Evaluation under Distribution Shift☆31Updated 3 years ago
- Interpreting neural networks via the STREAK algorithm (streaming weak submodular maximization)☆23Updated 7 years ago
- Uncertainty in Conditional Average Treatment Effect Estimation☆31Updated 4 years ago
- Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling☆31Updated 4 years ago
- Repository containing code for getting statistical guarantees on properties of BNNs☆13Updated 6 years ago
- Code for Augment & Reduce, a scalable stochastic algorithm for large categorical distributions☆10Updated 6 years ago
- Automatic and Simultaneous Adjustment of Learning Rate and Momentum for Stochastic Gradient Descent☆45Updated 4 years ago
- MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".☆18Updated last year
- Statistical adaptive stochastic optimization methods☆32Updated 5 years ago
- ☆17Updated 4 years ago
- Stochastic Gradient Riemannian Langevin Dynamics☆33Updated 9 years ago
- Supervised Local Modeling for Interpretability☆28Updated 6 years ago
- Implementation of the paper "Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory", Ron Amit and Ron Meir, ICML 2018☆18Updated 4 years ago
- A supplementary code for Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs.☆47Updated 5 years ago
- GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model’s Prediction. Thai Le, Suhang Wang, Dongwon …☆21Updated 4 years ago
- ☆61Updated 2 years ago
- ☆35Updated last year
- ☆24Updated 3 years ago
- A simple algorithm to identify and correct for label shift.☆21Updated 7 years ago
- Material for the practical of the DS3 course on "Representing and comparing probabilities with kernels"☆26Updated 6 years ago
- Python code for implementing embeddings in the Wasserstein space of elliptical distributions☆11Updated 4 years ago
- ☆26Updated 6 years ago
- Flexible Reinforcement Learning Framework with PyTorch☆22Updated 4 years ago
- Computing various norms/measures on over-parametrized neural networks☆49Updated 6 years ago
- Python implementation of projection losses.☆26Updated 5 years ago
- Bounding causal effects in general (continuous, non-additive) instrumental variable models.☆14Updated last year