Neural text generation models are likely to suffer from the low-diversity problem. Various decoding strategies and training-based methods have been proposed to promote diversity only by exploiting contextual features, but rarely do they consider incorporating syntactic structure clues. In this work, we propose using linguistic annotation, i.e., part-of-speech (POS), to guide the text generation. In detail, we introduce POS Guided Softmax (POSG-Softmax) to explicitly model two posterior probabilities: (i) next-POS, and (ii) next-token from the vocabulary of the target POS. A POS guided sampling strategy is further proposed to address the low-diversity problem by enriching the diversity of POS. Extensive experiments and human evaluations demonstrate that, compared with existing state-of-the-art methods, our proposed methods can generate more diverse text while maintaining comparable quality.

Hawkes Processes have undergone increasing popularity as default tools for modeling self- and mutually exciting interactions of discrete events in continuous-time event streams. A Maximum Likelihood Estimation (MLE) unconstrained optimization procedure over parametrically assumed forms of the triggering kernels of the corresponding intensity function are a widespread cost-effective modeling strategy, particularly suitable for data with few and/or short sequences. However, the MLE optimization lacks guarantees, except for strong assumptions on the parameters of the triggering kernels, and may lead to instability of the resulting parameters .In the present work, we show how a simple stabilization procedure improves the performance of the MLE optimization without these overly restrictive assumptions.This stabilized version of the MLE is shown to outperform traditional methods over sequences of several different lengths.

The classical contrastive loss (Oord et al., 2018; Poole et al., 2018) has achieved remarkable success in representation learning, benefiting downstream tasks in a variety of areas (Misra & Maaten, 2020; He et al., 2020; Chen et al., 2020a; Fang & Xie, 2020; Giorgi et al., 2020). The intuition of the contrastive loss is that given a query, its positive sample needs to be close, while the negative samples need to be far away in the representation space, for which the unit hypersphere is the most common assumption (Wang et al., 2017; Davidson et al., 2018). This learning scheme encourages the encoder to learn representations that are invariant to unnecessary details, and uniformly distributed on the hypersphere to maximally preserve relevant information (Hjelm et al., 2018; Tian et al., 2019; Bachman et al., 2019; Wang & Isola, 2020). A notable concern of the conventional contrastive loss is that the query's positive and negative samples are often uniformly sampled and equally treated in the comparison, which results in an inefficient estimation and limits the performance of learned representations (Saunshi et al., 2019b; Chuang et al., 2020). As illustrated in Figure 1, given a query, the conventional CL methods usually randomly take one positive sample to form the positive pair and equally treat all the other negative pairs, regardless of how informative a sample is to the query.

Studies related to fairness in machine learning have recently gained traction due to its ever-expanding role in high-stakes decision making. For example, it may be desirable to ensure that all clusters discovered by an algorithm have high gender diversity. Previously, these problems have been studied under a setting where sensitive attributes, with respect to which fairness conditions impose diversity across clusters, are assumed to be observable; hence, protected groups are readily available. Most often, this may not be true, and diversity or individual interests can manifest as an intrinsic or latent feature of a social network. For example, depending on latent sensitive attributes, individuals interact with each other and represent each other's interests, resulting in a network, which we refer to as a representation graph. Motivated by this, we propose an individual fairness criterion for clustering a graph $\mathcal{G}$ that requires each cluster to contain an adequate number of members connected to the individual under a representation graph $\mathcal{R}$. We devise a spectral clustering algorithm to find fair clusters under a given representation graph. We further propose a variant of the stochastic block model and establish our algorithm's weak consistency under this model. Finally, we present experimental results to corroborate our theoretical findings.

A recent line of work has shown that deep networks are highly susceptible to backdoor data poisoning attacks. Specifically, by injecting a small amount of malicious data into the training distribution, an adversary gains the ability to control the model's behavior during inference. In this work, we propose an iterative training procedure for removing poisoned data from the training set. Our approach consists of two steps. We first train an ensemble of weak learners to automatically discover distinct subpopulations in the training set. We then leverage a boosting framework to recover the clean data. Empirically, our method successfully defends against several state-of-the-art backdoor attacks, including both clean and dirty label attacks. We also present results from an independent third-party evaluation including a recent \textit{adaptive} poisoning adversary. The results indicate our approach is competitive with existing defenses against backdoor attacks on deep neural networks, and significantly outperforms the state-of-the-art in several scenarios.

This paper studies early-stopped mirror descent applied to noisy sparse phase retrieval, which is the problem of recovering a $k$-sparse signal $\mathbf{x}^\star\in\mathbb{R}^n$ from a set of quadratic Gaussian measurements corrupted by sub-exponential noise. We consider the (non-convex) unregularized empirical risk minimization problem and show that early-stopped mirror descent, when equipped with the hyperbolic entropy mirror map and proper initialization, achieves a nearly minimax-optimal rate of convergence, provided the sample size is at least of order $k^2$ (modulo logarithmic term) and the minimum (in modulus) non-zero entry of the signal is on the order of $\|\mathbf{x}^\star\|_2/\sqrt{k}$. Our theory leads to a simple algorithm that does not rely on explicit regularization or thresholding steps to promote sparsity. More generally, our results establish a connection between mirror descent and sparsity in the non-convex problem of noisy sparse phase retrieval, adding to the literature on early stopping that has mostly focused on non-sparse, Euclidean, and convex settings via gradient descent. Our proof combines a potential-based analysis of mirror descent with a quantitative control on a variational coherence property that we establish along the path of mirror descent, up to a prescribed stopping time.

Deep Learning (DL) has been utilized pervasively in the Internet of Things (IoT). One typical application of DL in IoT is device identification from wireless signals, namely Non-cryptographic Device Identification (NDI). However, learning components in NDI systems have to evolve to adapt to operational variations, such a paradigm is termed as Incremental Learning (IL). Various IL algorithms have been proposed and many of them require dedicated space to store the increasing amount of historical data, and therefore, they are not suitable for IoT or mobile applications. However, conventional IL schemes can not provide satisfying performance when historical data are not available. In this paper, we address the IL problem in NDI from a new perspective, firstly, we provide a new metric to measure the degree of topological maturity of DNN models from the degree of conflict of class-specific fingerprints. We discover that an important cause for performance degradation in IL enabled NDI is owing to the conflict of devices' fingerprints. Second, we also show that the conventional IL schemes can lead to low topological maturity of DNN models in NDI systems. Thirdly, we propose a new Channel Separation Enabled Incremental Learning (CSIL) scheme without using historical data, in which our strategy can automatically separate devices' fingerprints in different learning stages and avoid potential conflict. Finally, We evaluated the effectiveness of the proposed framework using real data from ADS-B (Automatic Dependent Surveillance-Broadcast), an application of IoT in aviation. The proposed framework has the potential to be applied to accurate identification of IoT devices in a variety of IoT applications and services. Data and code available at IEEE Dataport (DOI: 10.21227/1bxc-ke87) and \url{https://github.com/pcwhy/CSIL}}

Many machine learning problems can be formulated as minimax problems such as Generative Adversarial Networks (GANs), AUC maximization and robust estimation, to mention but a few. A substantial amount of studies are devoted to studying the convergence behavior of their stochastic gradient-type algorithms. In contrast, there is relatively little work on their generalization, i.e., how the learning models built from training examples would behave on test examples. In this paper, we provide a comprehensive generalization analysis of stochastic gradient methods for minimax problems under both convex-concave and nonconvex-nonconcave cases through the lens of algorithmic stability. We establish a quantitative connection between stability and several generalization measures both in expectation and with high probability. For the convex-concave setting, our stability analysis shows that stochastic gradient descent ascent attains optimal generalization bounds for both smooth and nonsmooth minimax problems. We also establish generalization bounds for both weakly-convex-weakly-concave and gradient-dominated problems.

Gradient is a commonly used term in optimization and machine learning. For example, deep learning neural networks are fit using stochastic gradient descent, and many standard optimization algorithms used to fit machine learning algorithms use gradient information. In order to understand what a gradient is, you need to understand what a derivative is from the field of calculus. This includes how to calculate a derivative and interpret the value. An understanding of the derivative is directly applicable to understanding how to calculate and interpret gradients as used in optimization and machine learning.

In human-level NLP tasks, such as predicting mental health, personality, or demographics, the number of observations is often smaller than the standard 768+ hidden state sizes of each layer within modern transformer-based language models, limiting the ability to effectively leverage transformers. Here, we provide a systematic study on the role of dimension reduction methods (principal components analysis, factorization techniques, or multi-layer auto-encoders) as well as the dimensionality of embedding vectors and sample sizes as a function of predictive performance. We first find that fine-tuning large models with a limited amount of data pose a significant difficulty which can be overcome with a pre-trained dimension reduction regime. RoBERTa consistently achieves top performance in human-level tasks, with PCA giving benefit over other reduction methods in better handling users that write longer texts. Finally, we observe that a majority of the tasks achieve results comparable to the best performance with just $\frac{1}{12}$ of the embedding dimensions.