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 , ULMFiT , GPT , BERT , GPT2 ) 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 (, , , 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.
Many real-world large-scale regression problems can be formulated as Multi-task Learning (MTL) problems with a massive number of tasks, as in retail and transportation domains. However, existing MTL methods still fail to offer both the generalization performance and the scalability for such problems. Scaling up MTL methods to problems with a tremendous number of tasks is a big challenge. Here, we propose a novel algorithm, named Convex Clustering Multi-Task regression Learning (CCMTL), which integrates with convex clustering on the k -nearest neighbor graph of the prediction models. Further, CCMTL efficiently solves the underlying convex problem with a newly proposed optimization method. CCMTL is accurate, efficient to train, and empirically scales linearly in the number of tasks. On both synthetic and real-world datasets, the proposed CCMTL outperforms seven state-of-the-art (SoA) multi-task learning methods in terms of prediction accuracy as well as computational efficiency. On a real-world retail dataset with 23 , 812 tasks, CCMTL requires only around 30 seconds to train on a single thread, while the SoA methods need up to hours or even days.
Methods for learning heterogeneous regression ensembles have not yet been proposed on a large scale. Hitherto, in classical ML literature, stacking, cascading and voting are mostly restricted to classification problems. Regression poses distinct learning challenges that may result in poor performance, even when using well established homogeneous ensemble schemas such as bagging or boosting. In this paper, we introduce MetaBags, a novel stacking framework for regression. MetaBags learns a set of meta-decision trees designed to select one base model (i.e. expert) for each query, and focuses on inductive bias reduction. Finally, these predictions are aggregated into a single prediction through a bagging procedure at meta-level. MetaBags is designed to learn a model with a fair bias-variance trade-off, and its improvement over base model performance is correlated with the prediction diversity of different experts on specific input space subregions. An exhaustive empirical testing of the method was performed, evaluating both generalization error and scalability of the approach on open, synthetic and real-world application datasets. The obtained results show that our method outperforms existing state-of-the-art approaches.