Federated learning (FL) is a distributed learning paradigm that allows multiple decentralized clients to collaboratively learn a common model without sharing local data. Although local data is not exposed directly, privacy concerns nonetheless exist as clients' sensitive information can be inferred from intermediate computations. Moreover, such information leakage accumulates substantially over time as the same data is repeatedly used during the iterative learning process. As a result, it can be particularly difficult to balance the privacy-accuracy trade-off when designing privacy-preserving FL algorithms. In this paper, we introduce Upcycled-FL, a novel federated learning framework with first-order approximation applied at every even iteration. Under this framework, half of the FL updates incur no information leakage and require much less computation. We first conduct the theoretical analysis on the convergence (rate) of Upcycled-FL, and then apply perturbation mechanisms to preserve privacy. Experiments on real-world data show that Upcycled-FL consistently outperforms existing methods over heterogeneous data, and significantly improves privacy-accuracy trade-off while reducing 48% of the training time on average.

SGD and AdamW are the two most used optimizers for fine-tuning large neural networks in computer vision. When the two methods perform the same, SGD is preferable because it uses less memory (12 bytes/parameter with momentum and 8 bytes/parameter without) than AdamW (16 bytes/parameter). However, on a suite of downstream tasks, especially those with distribution shifts, we find that fine-tuning with AdamW performs substantially better than SGD on modern Vision Transformer and ConvNeXt models. We find that large gaps in performance between SGD and AdamW occur when the fine-tuning gradients in the first "embedding" layer are much larger than in the rest of the model. Our analysis suggests an easy fix that works consistently across datasets and models: freezing the embedding layer (less than 1% of the parameters) leads to SGD with or without momentum performing slightly better than AdamW while using less memory (e.g., on ViT-L, SGD uses 33% less GPU memory). Our insights result in state-of-the-art accuracies on five popular distribution shift benchmarks: WILDS-FMoW, WILDS-Camelyon, BREEDS-Living-17, Waterbirds, and DomainNet.

To increase revenue, news websites often resort to using deceptive news titles, luring users into clicking on the title and reading the full news. Clickbait detection is the task that aims to automatically detect this form of false advertisement and avoid wasting the precious time of online users. Despite the importance of the task, to the best of our knowledge, there is no publicly available clickbait corpus for the Romanian language. To this end, we introduce a novel Romanian Clickbait Corpus (RoCliCo) comprising 8,313 news samples which are manually annotated with clickbait and non-clickbait labels. Furthermore, we conduct experiments with four machine learning methods, ranging from handcrafted models to recurrent and transformer-based neural networks, to establish a line-up of competitive baselines. We also carry out experiments with a weighted voting ensemble. Among the considered baselines, we propose a novel BERT-based contrastive learning model that learns to encode news titles and contents into a deep metric space such that titles and contents of non-clickbait news have high cosine similarity, while titles and contents of clickbait news have low cosine similarity. Our data set and code to reproduce the baselines are publicly available for download at https://github.com/dariabroscoteanu/RoCliCo.

Pairwise learning is essential in machine learning, especially for problems involving loss functions defined on pairs of training examples. Online gradient descent (OGD) algorithms have been proposed to handle online pairwise learning, where data arrives sequentially. However, the pairwise nature of the problem makes scalability challenging, as the gradient computation for a new sample involves all past samples. Recent advancements in OGD algorithms have aimed to reduce the complexity of calculating online gradients, achieving complexities less than $O(T)$ and even as low as $O(1)$. However, these approaches are primarily limited to linear models and have induced variance. In this study, we propose a limited memory OGD algorithm that extends to kernel online pairwise learning while improving the sublinear regret. Specifically, we establish a clear connection between the variance of online gradients and the regret, and construct online gradients using the most recent stratified samples with a limited buffer of size of $s$ representing all past data, which have a complexity of $O(sT)$ and employs $O(\sqrt{T}\log{T})$ random Fourier features for kernel approximation. Importantly, our theoretical results demonstrate that the variance-reduced online gradients lead to an improved sublinear regret bound. The experiments on real-world datasets demonstrate the superiority of our algorithm over both kernelized and linear online pairwise learning algorithms.

Students' migration from public to private schools, due to lack of school performance of public schools, is one of the major issues faced by the Government of Punjab to provide compulsory and quality education at low cost. Due to complex adaptive nature of educational system, interdependencies with society, constant feedback loops conventional linear regression methods, for evaluation of effective performance, are ineffective or costly to solve the issue. Linear regression techniques present the static view of the system, which are not enough to understand the complex dynamic nature of educational paradigm. We have presented a Cognitive Agent Computing-Based Model for the School Student Migration Problem Using a Descriptive Agent-Based Modeling approach to understand the causes-effects relationship of student migration. We have presented the primary school students' migration model using descriptive modeling approach along with exploratory modeling. Our research, in the context of Software Engineering of Simulation & Modeling, and exploring the Complex Adaptive nature of school system, is two folds. Firstly, the cause-effect relationship of students' migration is being investigated using Cognitive Descriptive Agent-Based Modeling. Secondly, the formalization extent of Cognitive Agent-Based Computing framework is analyzed by performing its comparative analysis with exploratory modeling protocol 'Overview, Design, and Detail'.

Multi-view clustering has attracted growing attention owing to its capabilities of aggregating information from various sources and its promising horizons in public affairs. Up till now, many advanced approaches have been proposed in recent literature. However, there are several ongoing difficulties to be tackled. One common dilemma occurs while attempting to align the features of different views. We dig out as well as deploy the dependency amongst views through hierarchical feature descent, which leads to a common latent space( STAGE 1). This latent space, for the first time of its kind, is regarded as a 'resemblance space', as it reveals certain correlations and dependencies of different views. To be exact, the one-hot encoding of a category can also be referred to as a resemblance space in its terminal phase. Moreover, due to the intrinsic fact that most of the existing multi-view clustering algorithms stem from k-means clustering and spectral clustering, this results in cubic time complexity w.r.t. the number of the objects. However, we propose Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent(MVSC-HFD) to further reduce the computing complexity to linear time cost through a unified sampling strategy in resemblance space( STAGE 2), followed by subspace clustering to learn the representation collectively( STAGE 3). Extensive experimental results on public benchmark datasets demonstrate that our proposed model consistently outperforms the state-of-the-art techniques.

This paper proposes a new procedure called quantum shadow gradient descent (QSGD) that addresses these key challenges. Our method has the benefits of a one-shot approach, in not requiring any sample duplication while having a convergence rate comparable to the ideal update rule using exact gradient computation. We propose a new technique for generating quantum shadow samples (QSS), which generates quantum shadows as opposed to classical shadows used in existing works. With classical shadows, the computations are typically performed on classical computers and, hence, are prohibitive since the dimension grows exponentially. Our approach resolves this issue by measurements of quantum shadows. As the second main contribution, we study more general non-product ansatz of the form $\exp\{i\sum_j \theta_j A_j\}$ that model variational Hamiltonians. We prove that the gradient can be written in terms of the gradient of single-parameter ansatzes that can be easily measured. Our proof is based on the Suzuki-Trotter approximation; however, our expressions are exact, unlike prior efforts that approximate non-product operators. As a result, existing gradient measurement techniques can be applied to more general VQAs followed by correction terms without any approximation penalty. We provide theoretical proofs, convergence analysis and verify our results through numerical experiments.

Characterizing and understanding the stability of Stochastic Gradient Descent (SGD) remains an open problem in deep learning. A common method is to utilize the convergence of statistical moments, esp. the variance, of the parameters to quantify the stability. We revisit the definition of stability for SGD and propose using the $\textit{convergence in probability}$ condition to define the $\textit{probabilistic stability}$ of SGD. The probabilistic stability sheds light on a fundamental question in deep learning theory: how SGD selects a meaningful solution for a neural network from an enormous number of possible solutions that may severely overfit. We show that only through the lens of probabilistic stability does SGD exhibit rich and practically relevant phases of learning, such as the phases of the complete loss of stability, incorrect learning where the model captures incorrect data correlation, convergence to low-rank saddles, and correct learning where the model captures the correct correlation. These phase boundaries are precisely quantified by the Lyapunov exponents of the dynamics. The obtained phase diagrams imply that SGD prefers low-rank saddles in a neural network when the underlying gradient is noisy, thereby influencing the learning performance.

DBSCAN is a fundamental density-based clustering technique that identifies any arbitrary shape of the clusters. However, it becomes infeasible while handling big data. On the other hand, centroid-based clustering is important for detecting patterns in a dataset since unprocessed data points can be labeled to their nearest centroid. However, it can not detect non-spherical clusters. For a large data, it is not feasible to store and compute labels of every samples. These can be done as and when the information is required. The purpose can be accomplished when clustering act as a tool to identify cluster representatives and query is served by assigning cluster labels of nearest representative. In this paper, we propose an Incremental Prototype-based DBSCAN (IPD) algorithm which is designed to identify arbitrary-shaped clusters for large-scale data. Additionally, it chooses a set of representatives for each cluster.

Machine learning (ML) - based software systems are rapidly gaining adoption across various domains, making it increasingly essential to ensure they perform as intended. This report presents best practices for the Test and Evaluation (T&E) of ML-enabled software systems across its lifecycle. We categorize the lifecycle of ML-enabled software systems into three stages: component, integration and deployment, and post-deployment. At the component level, the primary objective is to test and evaluate the ML model as a standalone component. Next, in the integration and deployment stage, the goal is to evaluate an integrated ML-enabled system consisting of both ML and non-ML components. Finally, once the ML-enabled software system is deployed and operationalized, the T&E objective is to ensure the system performs as intended. Maintenance activities for ML-enabled software systems span the lifecycle and involve maintaining various assets of ML-enabled software systems. Given its unique characteristics, the T&E of ML-enabled software systems is challenging. While significant research has been reported on T&E at the component level, limited work is reported on T&E in the remaining two stages. Furthermore, in many cases, there is a lack of systematic T&E strategies throughout the ML-enabled system's lifecycle. This leads practitioners to resort to ad-hoc T&E practices, which can undermine user confidence in the reliability of ML-enabled software systems. New systematic testing approaches, adequacy measurements, and metrics are required to address the T&E challenges across all stages of the ML-enabled system lifecycle.