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The private collection of multiple statistics from a population is a fundamental statistical problem. One possible approach to realize this is to rely on the local model of differential privacy (LDP). Numerous LDP protocols have been developed for the task of frequency estimation of single and multiple attributes. These studies mainly focused on improving the utility of the algorithms to ensure the server performs the estimations accurately. In this paper, we investigate privacy threats (re-identification and attribute inference attacks) against LDP protocols for multidimensional data following two state-of-the-art solutions for frequency estimation of multiple attributes. To broaden the scope of our study, we have also experimentally assessed five widely used LDP protocols, namely, generalized randomized response, optimal local hashing, subset selection, RAPPOR and optimal unary encoding. Finally, we also proposed a countermeasure that improves both utility and robustness against the identified threats. Our contributions can help practitioners aiming to collect users' statistics privately to decide which LDP mechanism best fits their needs.

We explain how to use Kolmogorov Superposition Theorem (KST) to break the curse of dimension when approximating continuous multivariate functions. We first show that there is a class of functions called $K$-Lipschitz continuous and can be approximated by a ReLU neural network of two layers to have an approximation order $O(d^2/n)$, and then we introduce the K-modulus of continuity of multivariate functions and derive the approximation rate for any continuous function $f\in C([0,1]^d)$ based on KST. Next we introduce KB-splines by using uniform B-splines to replace the K-outer function and their smooth version called LKB-splines to approximate high dimensional functions. Our numerical evidence shows that the curse of dimension is broken in the following sense. When using the standard discrete least squares method (DLS) to approximate a continuous function $f$ over $[0, 1]^d$, one expects to use a dense data set $P$, a large number of data locations and function values over the locations. Based on the LKB splines, the structure of K-inner functions leads to a sparse solution of the linear system associated with the DLS. Furthermore, there exists a magic set of points from $P$ with much small in size such that the rooted mean squares error (RMSE) from the DLS based on the magic set is similar to the RMSE of the DLS based on the original set $P$. In addition, the number of LKB-splines used for approximation is the same as the size of the magic data set. Hence we not need a lot of basis functions and a lot of data locations and function values to approximate a high dimensional continuous function $f$ when $f$ is not very oscillated.

This paper presents a construction of a proper and stable labelled sample compression scheme of size $O(\VCD^2)$ for any finite concept class, where $\VCD$ denotes the Vapnik-Chervonenkis Dimension. The construction is based on a well-known model of machine teaching, referred to as recursive teaching dimension. This substantially improves on the currently best known bound on the size of sample compression schemes (due to Moran and Yehudayoff), which is exponential in $\VCD$. The long-standing open question whether the smallest size of a sample compression scheme is in $O(\VCD)$ remains unresolved, but our results show that research on machine teaching is a promising avenue for the study of this open problem. As further evidence of the strong connections between machine teaching and sample compression, we prove that the model of no-clash teaching, introduced by Kirkpatrick et al., can be used to define a non-trivial lower bound on the size of stable sample compression schemes.

Multiversioning is widely used in databases, transactional memory, and concurrent data structures. It can be used to support read-only transactions that appear atomic in the presence of concurrent update operations. Any system that maintains multiple versions of each object needs a way of efficiently reclaiming them. We experimentally compare various existing reclamation techniques by applying them to a multiversion tree and a multiversion hash table. Using insights from these experiments, we develop two new multiversion garbage collection (MVGC) techniques. These techniques use two novel concurrent version list data structures. Our experimental evaluation shows that our fastest technique is competitive with the fastest existing MVGC techniques, while using significantly less space on some workloads. Our new techniques provide strong theoretical bounds, especially on space usage. These bounds ensure that the schemes have consistent performance, avoiding the very high worst-case space usage of other techniques.

Interactive data exploration (IDE) is an effective way of comprehending big data, whose volume and complexity are beyond human abilities. The main goal of IDE is to discover user interest regions from a database through multi-rounds of user labelling. Existing IDEs adopt active-learning framework, where users iteratively discriminate or label the interestingness of selected tuples. The process of data exploration can be viewed as the process of training a classifier, which determines whether a database tuple is interesting to a user. An efficient exploration thus takes very few iterations of user labelling to reach the data region of interest. In this work, we consider the data exploration as the process of few-shot learning, where the classifier is learned with only a few training examples, or exploration iterations. To this end, we propose a learning-to-explore framework, based on meta-learning, which learns how to learn a classifier with automatically generated meta-tasks, so that the exploration process can be much shortened. Extensive experiments on real datasets show that our proposal outperforms existing explore-by-example solutions in terms of accuracy and efficiency.

Variable selection has been played a critical role in contemporary statistics and scientific discoveries. Numerous regularization and Bayesian variable selection methods have been developed in the past two decades for variable selection, but they mainly target at only one response. As more data being collected nowadays, it is common to obtain and analyze multiple correlated responses from the same study. Running separate regression for each response ignores their correlation thus multivariate analysis is recommended. Existing multivariate methods select variables related to all responses without considering the possible heterogeneous sparsity of different responses, i.e. some features may only predict a subset of responses but not the rest. In this paper, we develop a novel Bayesian indicator variable selection method in multivariate regression model with a large number of grouped predictors targeting at multiple correlated responses with possibly heterogeneous sparsity patterns. The method is motivated by the multi-trait fine mapping problem in genetics to identify the variants that are causal to multiple related traits. Our new method is featured by its selection at individual level, group level as well as specific to each response. In addition, we propose a new concept of subset posterior inclusion probability for inference to prioritize predictors that target at subset(s) of responses. Extensive simulations with varying sparsity and heterogeneity levels and dimension have shown the advantage of our method in variable selection and prediction performance as compared to existing general Bayesian multivariate variable selection methods and Bayesian fine mapping methods. We also applied our method to a real data example in imaging genetics and identified important causal variants for brain white matter structural change in different regions.

Classic algorithms and machine learning systems like neural networks are both abundant in everyday life. While classic computer science algorithms are suitable for precise execution of exactly defined tasks such as finding the shortest path in a large graph, neural networks allow learning from data to predict the most likely answer in more complex tasks such as image classification, which cannot be reduced to an exact algorithm. To get the best of both worlds, this thesis explores combining both concepts leading to more robust, better performing, more interpretable, more computationally efficient, and more data efficient architectures. The thesis formalizes the idea of algorithmic supervision, which allows a neural network to learn from or in conjunction with an algorithm. When integrating an algorithm into a neural architecture, it is important that the algorithm is differentiable such that the architecture can be trained end-to-end and gradients can be propagated back through the algorithm in a meaningful way. To make algorithms differentiable, this thesis proposes a general method for continuously relaxing algorithms by perturbing variables and approximating the expectation value in closed form, i.e., without sampling. In addition, this thesis proposes differentiable algorithms, such as differentiable sorting networks, differentiable renderers, and differentiable logic gate networks. Finally, this thesis presents alternative training strategies for learning with algorithms.

Sequential recommendation as an emerging topic has attracted increasing attention due to its important practical significance. Models based on deep learning and attention mechanism have achieved good performance in sequential recommendation. Recently, the generative models based on Variational Autoencoder (VAE) have shown the unique advantage in collaborative filtering. In particular, the sequential VAE model as a recurrent version of VAE can effectively capture temporal dependencies among items in user sequence and perform sequential recommendation. However, VAE-based models suffer from a common limitation that the representational ability of the obtained approximate posterior distribution is limited, resulting in lower quality of generated samples. This is especially true for generating sequences. To solve the above problem, in this work, we propose a novel method called Adversarial and Contrastive Variational Autoencoder (ACVAE) for sequential recommendation. Specifically, we first introduce the adversarial training for sequence generation under the Adversarial Variational Bayes (AVB) framework, which enables our model to generate high-quality latent variables. Then, we employ the contrastive loss. The latent variables will be able to learn more personalized and salient characteristics by minimizing the contrastive loss. Besides, when encoding the sequence, we apply a recurrent and convolutional structure to capture global and local relationships in the sequence. Finally, we conduct extensive experiments on four real-world datasets. The experimental results show that our proposed ACVAE model outperforms other state-of-the-art methods.

Recent advances in maximizing mutual information (MI) between the source and target have demonstrated its effectiveness in text generation. However, previous works paid little attention to modeling the backward network of MI (i.e., dependency from the target to the source), which is crucial to the tightness of the variational information maximization lower bound. In this paper, we propose Adversarial Mutual Information (AMI): a text generation framework which is formed as a novel saddle point (min-max) optimization aiming to identify joint interactions between the source and target. Within this framework, the forward and backward networks are able to iteratively promote or demote each other's generated instances by comparing the real and synthetic data distributions. We also develop a latent noise sampling strategy that leverages random variations at the high-level semantic space to enhance the long term dependency in the generation process. Extensive experiments based on different text generation tasks demonstrate that the proposed AMI framework can significantly outperform several strong baselines, and we also show that AMI has potential to lead to a tighter lower bound of maximum mutual information for the variational information maximization problem.

We propose a new method for event extraction (EE) task based on an imitation learning framework, specifically, inverse reinforcement learning (IRL) via generative adversarial network (GAN). The GAN estimates proper rewards according to the difference between the actions committed by the expert (or ground truth) and the agent among complicated states in the environment. EE task benefits from these dynamic rewards because instances and labels yield to various extents of difficulty and the gains are expected to be diverse -- e.g., an ambiguous but correctly detected trigger or argument should receive high gains -- while the traditional RL models usually neglect such differences and pay equal attention on all instances. Moreover, our experiments also demonstrate that the proposed framework outperforms state-of-the-art methods, without explicit feature engineering.