Gradient estimation is often necessary for fitting generative models with discrete latent variables, in contexts such as reinforcement learning and variational autoencoder (VAE) training. The DisARM estimator (Yin et al. 2020; Dong, Mnih, and Tucker 2020) achieves state of the art gradient variance for Bernoulli latent variable models in many contexts. However, DisARM and other estimators have potentially exploding variance near the boundary of the parameter space, where solutions tend to lie. To ameliorate this issue, we propose a new gradient estimator \textit{bitflip}-1 that has lower variance at the boundaries of the parameter space. As bitflip-1 has complementary properties to existing estimators, we introduce an aggregated estimator, \textit{unbiased gradient variance clipping} (UGC) that uses either a bitflip-1 or a DisARM gradient update for each coordinate. We theoretically prove that UGC has uniformly lower variance than DisARM. Empirically, we observe that UGC achieves the optimal value of the optimization objectives in toy experiments, discrete VAE training, and in a best subset selection problem.

We study the robust principal component analysis (RPCA) problem in a distributed setting. The goal of RPCA is to find an underlying low-rank estimation for a raw data matrix when the data matrix is subject to the corruption of gross sparse errors. Previous studies have developed RPCA algorithms that provide stable solutions with fast convergence. However, these algorithms are typically hard to scale and cannot be implemented distributedly, due to the use of either SVD or large matrix multiplication. In this paper, we propose the first distributed robust principal analysis algorithm based on consensus factorization, dubbed DCF-PCA. We prove the convergence of DCF-PCA and evaluate DCF-PCA on various problem settings.

Conversational data is essential in psychology because it can help researchers understand individuals' cognitive processes, emotions, and behaviors. Utterance labelling is a common strategy for analyzing this type of data. The development of NLP algorithms allows researchers to automate this task. However, psychological conversational data present some challenges to NLP researchers, including multilabel classification, a large number of classes, and limited available data. This study explored how automated labels generated by NLP methods are comparable to human labels in the context of conversations on adulthood transition. We proposed strategies to handle three common challenges raised in psychological studies. Our findings showed that the deep learning method with domain adaptation (RoBERTa-CON) outperformed all other machine learning methods; and the hierarchical labelling system that we proposed was shown to help researchers strategically analyze conversational data.

We develop two fundamental stochastic sketching techniques; Penalty Sketching (PS) and Augmented Lagrangian Sketching (ALS) for solving consistent linear systems. The proposed PS and ALS techniques extend and generalize the scope of Sketch & Project (SP) method by introducing Lagrangian penalty sketches. In doing so, we recover SP methods as special cases and furthermore develop a family of new stochastic iterative methods. By varying sketch parameters in the proposed PS method, we recover novel stochastic methods such as Penalty Newton Descent, Penalty Kaczmarz, Penalty Stochastic Descent, Penalty Coordinate Descent, Penalty Gaussian Pursuit, and Penalty Block Kaczmarz. Furthermore, the proposed ALS method synthesizes a wide variety of new stochastic methods such as Augmented Newton Descent, Augmented Kaczmarz, Augmented Stochastic Descent, Augmented Coordinate Descent, Augmented Gaussian Pursuit, and Augmented Block Kaczmarz into one framework. Moreover, we show that the developed PS and ALS frameworks can be used to reformulate the original linear system into equivalent stochastic optimization problems namely the Penalty Stochastic Reformulation and Augmented Stochastic Reformulation. We prove global convergence rates for the PS and ALS methods as well as sub-linear $\mathcal{O}(\frac{1}{k})$ rates for the Cesaro average of iterates. The proposed convergence results hold for a wide family of distributions of random matrices, which provides the opportunity of fine-tuning the randomness of the method suitable for specific applications. Finally, we perform computational experiments that demonstrate the efficiency of our methods compared to the existing SP methods.

Alzheimer's disease (AD) is the main cause of dementia which is accompanied by loss of memory and may lead to severe consequences in peoples' everyday life if not diagnosed on time. Very few works have exploited transformer-based networks and despite the high accuracy achieved, little work has been done in terms of model interpretability. In addition, although Mini-Mental State Exam (MMSE) scores are inextricably linked with the identification of dementia, research works face the task of dementia identification and the task of the prediction of MMSE scores as two separate tasks. In order to address these limitations, we employ several transformer-based models, with BERT achieving the highest accuracy accounting for 87.50%. Concurrently, we propose an interpretable method to detect AD patients based on siamese networks reaching accuracy up to 83.75%. Next, we introduce two multi-task learning models, where the main task refers to the identification of dementia (binary classification), while the auxiliary one corresponds to the identification of the severity of dementia (multiclass classification). Our model obtains accuracy equal to 86.25% on the detection of AD patients in the multi-task learning setting. Finally, we present some new methods to identify the linguistic patterns used by AD patients and non-AD ones, including text statistics, vocabulary uniqueness, word usage, correlations via a detailed linguistic analysis, and explainability techniques (LIME). Findings indicate significant differences in language between AD and non-AD patients.

Tensor completion aims to recover the missing entries of a partially observed tensor by exploiting its low-rank structure, and has been applied to visual data recovery. In applications where the data arrives sequentially such as streaming video completion, the missing entries of the tensor need to be dynamically recovered in a streaming fashion. Traditional streaming tensor completion algorithms treat the entire visual data as a tensor, which may not work satisfactorily when there is a big change in the tensor subspace along the temporal dimension, such as due to strong motion across the video frames. In this paper, we develop a novel patch tracking-based streaming tensor ring completion framework for visual data recovery. Given a newly incoming frame, small patches are tracked from the previous frame. Meanwhile, for each tracked patch, a patch tensor is constructed by stacking similar patches from the new frame. Patch tensors are then completed using a streaming tensor ring completion algorithm, and the incoming frame is recovered using the completed patch tensors. We propose a new patch tracking strategy that can accurately and efficiently track the patches with missing data. Further, a new streaming tensor ring completion algorithm is proposed which can efficiently and accurately update the latent core tensors and complete the missing entries of the patch tensors. Extensive experimental results demonstrate the superior performance of the proposed algorithms compared with both batch and streaming state-of-the-art tensor completion methods.

3D posture estimation is important in analyzing and improving ergonomics in physical human-robot interaction and reducing the risk of musculoskeletal disorders. Vision-based posture estimation approaches are prone to sensor and model errors, as well as occlusion, while posture estimation solely from the interacting robot's trajectory suffers from ambiguous solutions. To benefit from the advantages of both approaches and improve upon their drawbacks, we introduce a low-cost, non-intrusive, and occlusion-robust multi-sensory 3D postural estimation algorithm in physical human-robot interaction. We use 2D postures from OpenPose over a single camera, and the trajectory of the interacting robot while the human performs a task. We model the problem as a partially-observable dynamical system and we infer the 3D posture via a particle filter. We present our work in teleoperation, but it can be generalized to other applications of physical human-robot interaction. We show that our multi-sensory system resolves human kinematic redundancy better than posture estimation solely using OpenPose or posture estimation solely using the robot's trajectory. This will increase the accuracy of estimated postures compared to the gold-standard motion capture postures. Moreover, our approach also performs better than other single sensory methods when postural assessment using RULA assessment tool.

Multilingual language models were shown to allow for nontrivial transfer across scripts and languages. In this work, we study the structure of the internal representations that enable this transfer. We focus on the representation of gender distinctions as a practical case study, and examine the extent to which the gender concept is encoded in shared subspaces across different languages. Our analysis shows that gender representations consist of several prominent components that are shared across languages, alongside language-specific components. The existence of language-independent and language-specific components provides an explanation for an intriguing empirical observation we make: while gender classification transfers well across languages, interventions for gender removal, trained on a single language, do not transfer easily to others.

A number of methods have been introduced for the fair ML issue, most of them complex and many of them very specific to the underlying ML moethodology. Here we introduce a new approach that is simple, easily explained, and potentially applicable to a number of standard ML algorithms. Explicitly Deweighted Features (EDF) reduces the impact of each feature among the proxies of sensitive variables, allowing a different amount of deweighting applied to each such feature. The user specifies the deweighting hyperparameters, to achieve a given point in the Utility/Fairness tradeoff spectrum. We also introduce a new, simple criterion for evaluating the degree of protection afforded by any fair ML method.

Crowdsourcing technologies rely on groups of people to input information that may be critical for decision-making. This work examines obfuscation in the context of reporting technologies. We show that widespread use of reporting platforms comes with unique security and privacy implications, and introduce a threat model and corresponding taxonomy to outline some of the many attack vectors in this space. We then perform an empirical analysis of a dataset of call logs from a controversial, real-world reporting hotline and identify coordinated obfuscation strategies that are intended to hinder the platform's legitimacy. We propose a variety of statistical measures to quantify the strength of this obfuscation strategy with respect to the structural and semantic characteristics of the reporting attacks in our dataset.