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Found 4 papers, 0 papers with code
TsSHAP: Robust model agnostic feature-based explainability for time series forecasting
The explanations are in terms of the SHAP values obtained by applying the TreeSHAP algorithm on a surrogate model that learns a mapping between the interpretable feature space and the forecast of the black-box model.
HE-PEx: Efficient Machine Learning under Homomorphic Encryption using Pruning, Permutation and Expansion
In this work, we propose a novel set of pruning methods that reduce the latency and memory requirement, thus bringing the effectiveness of plaintext pruning methods to HE.
Efficient Encrypted Inference on Ensembles of Decision Trees
In this work, we propose a framework to transfer knowledge extracted by complex decision tree ensembles to shallow neural networks (referred to as DTNets) that are highly conducive to encrypted inference.
Differentially Private Distributed Data Summarization under Covariate Shift
Our central result is a novel protocol that (a) ensures the curator accesses at most $O(K^{\frac{1}{3}}|D_s| + |D_v|)$ points (b) has formal privacy guarantees on the leakage of information between the data owners and (c) closely matches the best known non-private greedy algorithm.
