Privacy-preserving neural inference helps protect both the user input data and the model weights from being leaked to others during the inference of a deep learning model. To achieve data protection, the inference is often performed within a secure domain, and the final result is revealed in plaintext. Nevertheless, performing the computations in the secure domain incurs about a thousandfold overhead compared with the insecure version, especially when the involved operations of the entire model are mapped to the secure domain, which is the computation scheme adopted by the existing works. This work is inspired by the transfer learning technique, where the weights of some parts of the model layers are transferred from a publicly available, pre-built deep learning model, and it opens a door to further boost the execution efficiency by allowing us to do the secure computations selectively on parts of the transferred model. We have built a compiler framework, SecureTVM, to automatically translate a trained model into the secure version, where the model layers to be protected can be selectively configured by its model provider. As a result, SecureTVM outperforms the state of the art, CrypTFlow2, by a factor of 55 for the transfer learning model. We believe that this work takes a step forward toward the practical uses of privacy-preserving neural inference for real-world applications.

Multi-label classification (MLC) is an important class of machine learning problems that come with a wide spectrum of applications, each demanding a possibly different evaluation criterion. When solving the MLC problems, we generally expect the learning algorithm to take the hidden correlation of the labels into account to improve the prediction performance. Extracting the hidden correlation is generally a challenging task. In this work, we propose a novel deep learning framework to better extract the hidden correlation with the help of the memory structure within recurrent neural networks. The memory stores the temporary guesses on the labels and effectively allows the framework to rethink about the goodness and correlation of the guesses before making the final prediction. Furthermore, the rethinking process makes it easy to adapt to different evaluation criteria to match real-world application needs. In particular, the framework can be trained in an end-to-end style with respect to any given MLC evaluation criteria. The end-to-end design can be seamlessly combined with other deep learning techniques to conquer challenging MLC problems like image tagging. Experimental results across many real-world data sets justify that the rethinking framework indeed improves MLC performance across different evaluation criteria and leads to superior performance over state-of-the-art MLC algorithms.

Multi-label learning is an important machine learning problem with a wide range of applications. The variety of criteria for satisfying different application needs calls for cost-sensitive algorithms, which can adapt to different criteria easily. Nevertheless, because of the sophisticated nature of the criteria for multi-label learning, cost-sensitive algorithms for general criteria are hard to design, and current cost-sensitive algorithms can at most deal with some special types of criteria. In this work, we propose a novel cost-sensitive multi-label learning model for any general criteria. Our key idea within the model is to iteratively estimate a surrogate loss that approximates the sophisticated criterion of interest near some local neighborhood, and use the estimate to decide a descent direction for optimization. The key idea is then coupled with deep learning to form our proposed model. Experimental results validate that our proposed model is superior to existing cost-sensitive algorithms and existing deep learning models across different criteria.

We propose a novel method, Cyclic Classifier Chain (CCC), for multilabel classification. CCC extends the classic Classifier Chain (CC) method by cyclically training multiple chains of labels. Three benefits immediately follow the cyclic design. First, CCC resolves the critical issue of label ordering in CC, and therefore reaches more stable performance. Second, CCC matches the task of cost-sensitive multilabel classification, an important problem for satisfying application needs. The cyclic aspect of CCC allows estimating all labels during training, and such estimates makes it possible to embed the cost information into weights of labels. Experimental results justify that cost-sensitive CCC can be superior to state-of-the-art cost-sensitive multilabel classification methods. Third, CCC can be easily coupled with gradient boosting to inherit the advantages of ensemble learning. In particular, gradient boosted CCC efficiently reaches promising performance for both linear and non-linear base learners. The three benefits, stability, cost-sensitivity and efficiency make CCC a competitive method for real-world applications.

We are at the beginning of the multicore era. Computers will have increasingly many cores (processors), but there is still no good programming framework for these architectures, and thus no simple and unified way for machine learning to take advantage of the potential speed up. In this paper, we develop a broadly applicable parallel programming method, one that is easily applied to many different learning algorithms. Our work is in distinct contrast to the tradition in machine learning of designing (often ingenious) ways to speed up a single algorithm at a time. Specifically, we show that algorithms that fit the Statistical Query model [15] can be written in a certain "summation form," which allows them to be easily parallelized on multicore computers. We adapt Google's map-reduce [7] paradigm to demonstrate this parallel speed up technique on a variety of learning algorithms including locally weighted linear regression (LWLR), k-means, logistic regression (LR), naive Bayes (NB), SVM, ICA, PCA, gaussian discriminant analysis (GDA), EM, and backpropagation (NN). Our experimental results show basically linear speedup with an increasing number of processors.