Search Results for author:
Found 17 papers, 5 papers with code
Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach
Federated Learning (FL) is a novel paradigm for the shared training of models based on decentralized and private data.
Certified Federated Adversarial Training
We model an attacker who poisons the model to insert a weakness into the adversarial training such that the model displays apparent adversarial robustness, while the attacker can exploit the inserted weakness to bypass the adversarial training and force the model to misclassify adversarial examples.
Automated Robustness with Adversarial Training as a Post-Processing Step
Adversarial training is a computationally expensive task and hence searching for neural network architectures with robustness as the criterion can be challenging.
The Devil is in the GAN: Backdoor Attacks and Defenses in Deep Generative Models
Deep Generative Models (DGMs) are a popular class of deep learning models which find widespread use because of their ability to synthesize data from complex, high-dimensional manifolds.
FAT: Federated Adversarial Training
Federated learning (FL) is one of the most important paradigms addressing privacy and data governance issues in machine learning (ML).
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume.
Exploring the Hyperparameter Landscape of Adversarial Robustness
We present a sensitivity analysis that illustrates that the effectiveness of adversarial training hinges on the settings of a few salient hyperparameters.
Castor: Contextual IoT Time Series Data and Model Management at Scale
The main features of Castor are: (1) an efficient pipeline for ingesting IoT time series data in real time; (2) a scalable, hybrid data management service for both time series and contextual data; (3) a versatile semantic model for contextual information which can be easily adopted to different application domains; (4) an abstract framework for developing and storing predictive models in R or Python; (5) deployment services which automatically train and/or score predictive models upon user-defined conditions.
Computation Other Statistics
Adversarial Robustness Toolbox v1.0.0
Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging runtime detection methods to flag any inputs that might have been modified by an adversary.
Automated Image Data Preprocessing with Deep Reinforcement Learning
Data preparation, i. e. the process of transforming raw data into a format that can be used for training effective machine learning models, is a tedious and time-consuming task.
Learning Correlation Space for Time Series
We propose an approximation algorithm for efficient correlation search in time series data.
Neural Feature Learning From Relational Database
To the best of our knowledge, this is the first time an automated data science system could win medals in Kaggle competitions with complex relational database.
One button machine for automating feature engineering in relational databases
Feature engineering is one of the most important and time consuming tasks in predictive analytics projects.
Non-parametric estimation of Jensen-Shannon Divergence in Generative Adversarial Network training
Generative Adversarial Networks (GANs) have become a widely popular framework for generative modelling of high-dimensional datasets.
Multi-task additive models with shared transfer functions based on dictionary learning
Additive models form a widely popular class of regression models which represent the relation between covariates and response variables as the sum of low-dimensional transfer functions.
Mixing Properties of Conditional Markov Chains with Unbounded Feature Functions
Conditional Markov Chains (also known as Linear-Chain Conditional Random Fields in the literature) are a versatile class of discriminative models for the distribution of a sequence of hidden states conditional on a sequence of observable variables.
Adaptive Learning of Smoothing Functions: Application to Electricity Load Forecasting
In order to quickly track changes in the model and put more weight on recent data, the RLS filter uses a forgetting factor which exponentially weights down observations by the order of their arrival.
