Search Results for author:
Found 9 papers, 0 papers with code
A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series
Foundation models have recently gained attention within the field of machine learning thanks to its efficiency in broad data processing.
An End-to-End Time Series Model for Simultaneous Imputation and Forecast
Time series forecasting using historical data has been an interesting and challenging topic, especially when the data is corrupted by missing values.
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.
AutoAI-TS: AutoAI for Time Series Forecasting
We present AutoAI for Time Series Forecasting (AutoAI-TS) that provides users with a zero configuration (zero-conf ) system to efficiently train, optimize and choose best forecasting model among various classes of models for the given dataset.
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.
Trusted Multi-Party Computation and Verifiable Simulations: A Scalable Blockchain Approach
Large-scale computational experiments, often running over weeks and over large datasets, are used extensively in fields such as epidemiology, meteorology, computational biology, and healthcare to understand phenomena, and design high-stakes policies affecting everyday health and economy.
Similarity Preserving Representation Learning for Time Series Clustering
A considerable amount of clustering algorithms take instance-feature matrices as their inputs.
