Time series forecasting estimates how a sequence of observations continues into the future.
In this blog post, we discuss the performance of ensemble methods for time series forecasting. We obtained our insights from conducting an experiment that compared a collection of 12 ensemble methods for time series forecasting, their hyperparameters and the different strategies used to select forecasting models. Furthermore, we will describe our developed meta-learning approach that automatically selects a subset of these ensemble methods (plus their hyperparameter configurations) to run for any given time series dataset.
Graph neural networks (GNNs) are a popular class of machine learning models whose major advantage is their ability to incorporate a sparse and discrete dependency structure between data points. Unfortunately, GNNs can only be used when such a graph-structure is available. In practice, however, real-world graphs are often noisy and incomplete or might not be available at all. With this work, we propose to jointly learn the graph structure and the parameters of graph convolutional networks (GCNs) by approximately solving a bilevel program that learns a discrete probability distribution on the edges of the graph. This allows one to apply GCNs not only in scenarios where the given graph is incomplete or corrupted but also in those where a graph is not available. We conduct a series of experiments that analyze the behavior of the proposed method and demonstrate that it outperforms related methods by a significant margin.
Accepted at Empirical Methods for Natural Language Processing (EMNLP) 2019
NLP experienced a major change in the previous months. Previously, each NLP task defined a neural model and trained this model on the given task. But in recent months, various papers (ELMo [1], ULMFiT [2], GPT [3], BERT [4], GPT2 [5]) showed that it is possible to pre-train a NLP model on a language modelling task (more on this below) and then use this model as a starting point to fine-tune to further tasks. This has been labelled as an important turning point for NLP by many ([6], [7], [8], inter alia).
With the proliferation of Trusted Execution Environments (TEEs) such as Intel SGX, a number of cloud providers will soon introduce TEE capabilities within their offering (e.g., Microsoft Azure). The integration of SGX within the cloud considerably strengthens the threat model for cloud applications. However, cloud deployments depend on the ability of the cloud operator to add and remove application dynamically; this is no longer possible given the current model to deploy and provision enclaves that actively involves the application owner. In this paper, we propose ReplicaTEE, a solution that enables seamless commissioning and decommissioning of TEE-based applications in the cloud. ReplicaTEE leverages an SGX-based provisioning service that interfaces with a Byzantine Fault-Tolerant storage service to securely orchestrate enclave replication in the cloud, without the active intervention of the application owner. Namely, in ReplicaTEE, the application owner entrusts application secret to the provisioning service; the latter handles all enclave commissioning and decommissioning operations throughout the application lifetime. We analyze the security of ReplicaTEE and show that it is secure against attacks by a powerful adversary that can compromise a large fraction of the cloud infrastructure. We implement a prototype of ReplicaTEE in a realistic cloud environment and evaluate its performance. ReplicaTEE moderately increments the TCB by
≈
800
LoC. Our evaluation shows that ReplicaTEE does not add significant overhead to existing SGX-based applications.
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