Improved Algorithms for Time Decay Streams

In the time-decay model for data streams, elements of an underlying data set arrive sequentially with the recently arrived elements being more important. A common approach for handling large data sets is to maintain a coreset, a succinct summary of the processed data that allows approximate recovery of a predetermined query. We provide a general framework that takes any offline-coreset and gives a time-decay coreset for polynomial time decay functions. We also consider the exponential time decay model for k-median clustering, where we provide a constant factor approximation algorithm that utilizes the online facility location algorithm. Our algorithm stores O(k log(h Delta)+h) points where h is the half-life of the decay function and Delta is the aspect ratio of the dataset. Our techniques extend to k-means clustering and M-estimators as well

From Adaptive Query Release to Machine Unlearning

We formalize the problem of machine unlearning as design of efficient unlearning algorithms corresponding to learning algorithms which perform a selection of adaptive queries from structured query classes. We give efficient unlearning algorithms for linear and prefix-sum query classes. As applications, we show that unlearning in many problems, in particular, stochastic convex optimization (SCO), can be reduced to the above, yielding improved guarantees for the problem. In particular, for smooth Lipschitz losses and any $\rho>0$, our results yield an unlearning algorithm with excess population risk of $\tilde O\big(\frac{1}{\sqrt{n}}+\frac{\sqrt{d}}{n\rho}\big)$ with unlearning query (gradient) complexity $\tilde O(\rho \cdot \text{Retraining Complexity})$, where $d$ is the model dimensionality and $n$ is the initial number of samples. For non-smooth Lipschitz losses, we give an unlearning algorithm with excess population risk $\tilde O\big(\frac{1}{\sqrt{n}}+\big(\frac{\sqrt{d}}{n\rho}\big)^{1/2}\big)$ with the same unlearning query (gradient) complexity. Furthermore, in the special case of Generalized Linear Models (GLMs), such as those in linear and logistic regression, we get dimension-independent rates of $\tilde O\big(\frac{1}{\sqrt{n}} +\frac{1}{(n\rho)^{2/3}}\big)$ and $\tilde O\big(\frac{1}{\sqrt{n}} +\frac{1}{(n\rho)^{1/3}}\big)$ for smooth Lipschitz and non-smooth Lipschitz losses respectively. Finally, we give generalizations of the above from one unlearning request to \textit{dynamic} streams consisting of insertions and deletions.Comment: Accepted to ICML 202

Private Federated Learning with Autotuned Compression

We propose new techniques for reducing communication in private federated learning without the need for setting or tuning compression rates. Our on-the-fly methods automatically adjust the compression rate based on the error induced during training, while maintaining provable privacy guarantees through the use of secure aggregation and differential privacy. Our techniques are provably instance-optimal for mean estimation, meaning that they can adapt to the ``hardness of the problem" with minimal interactivity. We demonstrate the effectiveness of our approach on real-world datasets by achieving favorable compression rates without the need for tuning.Comment: Accepted to ICML 202

A COMPARISON OF ART PROGRAM EVALUATION RATINGS BY SELF AND VISITING TEAMS IN SELECTED NORTH CENTRAL ASSOCIATION HIGH SCHOOLS (ARIZONA).

The investigation was concerned with 31 Arizona high schools which were members of the North Central Association of Colleges and Schools and which had employed the "Evaluative Criteria," 5th Edition to evaluate themselves and which also had a visiting North Central Team evaluation. The study focused on the art section of each school's evaluation. It sought to ascertain differences in ratings between the schools' self-evaluation and the ratings of the visiting North Central Teams on the 31 evaluation items of the Art Section. A theoretical framework of six categories was constructed. This included: Fundamentals, Self-Actualization, Evaluation, Progress, Support, and General Evaluation. Related literature was reviewed for each category to provide a backdrop of concepts with which to consider the collected data. Each of the 31 evaluation items was subsumed under one or another of these categories for systematic study and reporting. The data were analyzed by use of the product-moment correlation coefficient, and the obtained indices of relationship were examined for their significance at the .05 alpha level. A second analysis involving the statistics was applied to the two sets of obtained ratings across the six categories. The factors of school size, i.e., small and large schools, and geographic location, i.e., rural and urban, were also considered statistically. It was found that there was a significant positive set of relationships across the six categories of the theoretical framework between how the schools rated themselves in the art area and how the visiting North Central teams rated the schools in the art area. School size appeared to be a factor in only two of the six categories, i.e., Self-Evaluation and General Evaluation, while geographic location appeared to be a factor in the three categories of Self-Actualization, Evaluation, and Support

Art education in Afghanistan

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Statistical Learning via Stochastic Optimization under Data Privacy Considerations

In this dissertation, we study statistical learning, formulated as stochastic optimization problems, under modern constraints motivated by data privacy considerations. The goal is to understand the statistical and computational complexity in algorithm design for fundamental classes of problems. The first part concerns differential privacy, which, in recent years, has emerged as the de-facto standard for privacy-preserving data analysis. We study design of differentially private algorithms for (a). supervised learning of linear predictors with convex losses, also known as convex generalized linear models, and (b). non-convex optimization, where the goal is to approximate stationary points of the risk function. Our derived guarantees for the proposed algorithms are, as of yet, the best-known, and in most cases, are shown to be nearly optimal, in the worst case. The second part concerns the problem of machine unlearning. The goal here is to efficiently update a trained model under requests to unlearn a data point in the training dataset. We delve into the problem for widely-studied classes of convex losses: smooth/non-smooth settings and generalized linear models. We propose learning and corresponding unlearning algorithms, which are (non-trivially) accurate and efficient. Further, we extend our techniques, to unlearn general structured iterative procedures, and a streaming setting, where the unlearning requests arrive sequentially