In many industrial applications, obtaining labeled observations is not straightforward as it often requires the intervention of human experts or the use of expensive testing equipment. In these circumstances, active learning can be highly beneficial in suggesting the most informative data points to be used when fitting a model. Reducing the number of observations needed for model development alleviates both the computational burden required for training and the operational expenses related to labeling. Online active learning, in particular, is useful in high-volume production processes where the decision about the acquisition of the label for a data point needs to be taken within an extremely short time frame. However, despite the recent efforts to develop online active learning strategies, the behavior of these methods in the presence of outliers has not been thoroughly examined. In this work, we investigate the performance of online active linear regression in contaminated data streams. Our study shows that the currently available query strategies are prone to sample outliers, whose inclusion in the training set eventually degrades the predictive performance of the models. To address this issue, we propose a solution that bounds the search area of a conditional D-optimal algorithm and uses a robust estimator. Our approach strikes a balance between exploring unseen regions of the input space and protecting against outliers. Through numerical simulations, we show that the proposed method is effective in improving the performance of online active learning in the presence of outliers, thus expanding the potential applications of this powerful tool.

For decades, best subset selection (BSS) has eluded statisticians mainly due to its computational bottleneck. However, until recently, modern computational breakthroughs have rekindled theoretical interest in BSS and have led to new findings. Recently, \cite{guo2020best} showed that the model selection performance of BSS is governed by a margin quantity that is robust to the design dependence, unlike modern methods such as LASSO, SCAD, MCP, etc. Motivated by their theoretical results, in this paper, we also study the variable selection properties of best subset selection for high-dimensional sparse linear regression setup. We show that apart from the identifiability margin, the following two complexity measures play a fundamental role in characterizing the margin condition for model consistency: (a) complexity of \emph{residualized features}, (b) complexity of \emph{spurious projections}. In particular, we establish a simple margin condition that depends only on the identifiability margin and the dominating one of the two complexity measures. Furthermore, we show that a margin condition depending on similar margin quantity and complexity measures is also necessary for model consistency of BSS. For a broader understanding, we also consider some simple illustrative examples to demonstrate the variation in the complexity measures that refines our theoretical understanding of the model selection performance of BSS under different correlation structures.

The first-principles-based effective Hamiltonian is widely used to predict and simulate the properties of ferroelectrics and relaxor ferroelectrics. However, the parametrization method of the effective Hamiltonian is complicated and hardly can resolve the systems with complex interactions and/or complex components. Here, we developed an on-the-fly machine learning approach to parametrize the effective Hamiltonian based on Bayesian linear regression. The parametrization is completed in molecular dynamics simulations, with the energy, forces and stress predicted at each step along with their uncertainties. First-principles calculations are executed when the uncertainties are large to retrain the parameters. This approach provides a universal and automatic way to compute the effective Hamiltonian parameters for any considered systems including complex systems which previous methods can not handle. BaTiO3 and Pb(Sc,Ta)O3 are taken as examples to show the accurateness of this approach comparing with conventional first-principles parametrization method.

Causality has been defined with the identification of the cause or causes of a phenomenon by establishing covariation of cause and effect, a time-order relationship with the cause preceding the effect, and the elimination of plausible alternative causes; see Shaughnessy et al. (2000). To claim a specific causal effect between two variables is quite a strong claim. First, there needs to be well-defined treatment and outcome with an established covariance. Second, the treatment must proceed the observed outcome. Third, there must be no other present confounders, i.e., other "treatments" that could have their own causal effect; see Judea (2010). While these conditions are not perfect parameters for inferring a causal relationship between a treatment and outcome, they help researchers remove strong bias from their studies; see Hammerton and Munafò (2021). A causal effect found in a causal inference study is almost never the true causal effect, rather a less-biased estimate that is significantly closer to the true causal effect of the treatment on the outcome. To calculate a true causal effect would require "counterfactual" outcomes that cannot be measured; see Judea (2010). To describe a counterfactual outcome, let us define some treatment Z and an outcome Y.

Representation multi-task learning (MTL) and transfer learning (TL) have achieved tremendous success in practice. However, the theoretical understanding of these methods is still lacking. Most existing theoretical works focus on cases where all tasks share the same representation, and claim that MTL and TL almost always improve performance. However, as the number of tasks grows, assuming all tasks share the same representation is unrealistic. Also, this does not always match empirical findings, which suggest that a shared representation may not necessarily improve single-task or target-only learning performance. In this paper, we aim to understand how to learn from tasks with \textit{similar but not exactly the same} linear representations, while dealing with outlier tasks. With a known intrinsic dimension, we propose two algorithms that are \textit{adaptive} to the similarity structure and \textit{robust} to outlier tasks under both MTL and TL settings. Our algorithms outperform single-task or target-only learning when representations across tasks are sufficiently similar and the fraction of outlier tasks is small. Furthermore, they always perform no worse than single-task learning or target-only learning, even when the representations are dissimilar. We provide information-theoretic lower bounds to show that our algorithms are nearly \textit{minimax} optimal in a large regime. We also propose an algorithm to adapt to the unknown intrinsic dimension. We conduct two simulation studies to verify our theoretical results.

"Mens sana in corpore sano" (Juvenal, 100-127 AD) Mental training has long been part of human culture, appearing in several different forms ranging from meditation to particular games or cognitive exercises aimed at various purposes. The past decades, however, have seen it losing its cardinal role in the well-roundedness of an individual and becoming more of a side hustle, confined to particular hobbies or to specific techniques needed for mental-health purposes. By contrast, recent years have seen an exponential investment in and development of artificial intelligence and machine learning technologies, which seem to be successfully tackling increasingly difficult tasks and problems. This work, then, aims to combine the two fields together by presenting a practical implementation of machine learning to the particular form of mental training that is the art of memory, taken in its competitive version called "Memory Sports". Such a fusion, on the one hand, strives to raise awareness about both realms, while on the other it seeks to encourage research in this mixed field as a way to, ultimately, drive forward the development of this seemingly underestimated sport. After first introducing the topic of mental training and its particular branch of Memory Sports, in the first chapter, the machine learning involved in the project is explained in the second chapter. The third chapter, then, presents two practical implementations of machine learning in Memory Sports, the results of which are discussed in the final chapter, together with several potential directions for future research. Ultimately, as well as stimulating interest and inspiration regarding the two fields involved in this research and exploring their points of contact, the aim here is also to investigate potential developments of human-machine collaborations, which are likely to be the focus of the next advances in technology and society overall. Starting to think in this view can help better prepare for the abrupt changes that might come and even be part of them, so as to drive their aim and scope toward a more responsible, and thus better, outcome.

High-dimensional data analysis plays an important role in modern scientific discoveries. There has been extensive work on high-dimensional variable selection and estimation using penalized regressions, such as Lasso (Tibshirani, 1996), SCAD (Fan and Li, 2001), MCP (Zhang et al., 2010), and selection by partitioning solution paths (Liu and Wang, 2018). In recent years, inference for the true regression coefficients and the true model began to attract attention. A major challenge of high-dimensional inference is how to quantify the uncertainty of the coefficient estimate because such uncertainty depends on two components, the uncertainty in parameter estimation given the selected model, the uncertainty in selecting the model, both of which are difficult to estimate and are actively studied. For inference of the regression coefficients, Scheffé (1953) introduces the notion of simultaneous confidence intervals, which is a sequence of intervals containing the true coefficients at a given probability. For the high-dimensional linear models, Dezeure et al. (2017) and Zhang and Cheng (2017) construct the simultaneous confidence intervals using the debiased Lasso approach (van de Geer et al., 2014; Zhang and Zhang, 2014).

Cricket is the second most popular sport after soccer in terms of viewership. However, the assessment of individual player performance, a fundamental task in team sports, is currently primarily based on aggregate performance statistics, including average runs and wickets taken. We propose Context-Aware Metric of player Performance, CAMP, to quantify individual players' contributions toward a cricket match outcome. CAMP employs data mining methods and enables effective data-driven decision-making for selection and drafting, coaching and training, team line-ups, and strategy development. CAMP incorporates the exact context of performance, such as opponents' strengths and specific circumstances of games, such as pressure situations. We empirically evaluate CAMP on data of limited-over cricket matches between 2001 and 2019. In every match, a committee of experts declares one player as the best player, called Man of the M}atch (MoM). The top two rated players by CAMP match with MoM in 83\% of the 961 games. Thus, the CAMP rating of the best player closely matches that of the domain experts. By this measure, CAMP significantly outperforms the current best-known players' contribution measure based on the Duckworth-Lewis-Stern (DLS) method.

Wireless indoor localization has attracted significant amount of attention in recent years. Using received signal strength (RSS) obtained from WiFi access points (APs) for establishing fingerprinting database is a widely utilized method in indoor localization. However, the time-variant problem for indoor positioning systems is not well-investigated in existing literature. Compared to conventional static fingerprinting, the dynamicallyreconstructed database can adapt to a highly-changing environment, which achieves sustainability of localization accuracy. To deal with the time-varying issue, we propose a skeleton-assisted learning-based clustering localization (SALC) system, including RSS-oriented map-assisted clustering (ROMAC), cluster-based online database establishment (CODE), and cluster-scaled location estimation (CsLE). The SALC scheme jointly considers similarities from the skeleton-based shortest path (SSP) and the time-varying RSS measurements across the reference points (RPs). ROMAC clusters RPs into different feature sets and therefore selects suitable monitor points (MPs) for enhancing location estimation. Moreover, the CODE algorithm aims for establishing adaptive fingerprint database to alleviate the timevarying problem. Finally, CsLE is adopted to acquire the target position by leveraging the benefits of clustering information and estimated signal variations in order to rescale the weights fromweighted k-nearest neighbors (WkNN) method. Both simulation and experimental results demonstrate that the proposed SALC system can effectively reconstruct the fingerprint database with an enhanced location estimation accuracy, which outperforms the other existing schemes in the open literature.

Identifying and understanding implicit attitudes toward food can help efforts to mitigate social prejudice due to food's pervasive role as a marker of cultural and ethnic identity. Stereotypes about food are a form of microaggression that contribute to harmful public discourse that may in turn perpetuate prejudice toward ethnic groups and negatively impact economic outcomes for restaurants. Through careful linguistic analyses, we evaluate social theories about attitudes toward immigrant cuisine in a large-scale study of framing differences in 2.1M English language Yelp reviews of restaurants in 14 US states. Controlling for factors such as restaurant price and neighborhood racial diversity, we find that immigrant cuisines are more likely to be framed in objectifying and othering terms of authenticity (e.g., authentic, traditional), exoticism (e.g., exotic, different), and prototypicality (e.g., typical, usual), but that non-Western immigrant cuisines (e.g., Indian, Mexican) receive more othering than European cuisines (e.g., French, Italian). We further find that non-Western immigrant cuisines are framed less positively and as lower status, being evaluated in terms of affordability and hygiene. Finally, we show that reviews generated by large language models (LLMs) reproduce many of the same framing tendencies. Our results empirically corroborate social theories of taste and gastronomic stereotyping, and reveal linguistic processes by which such attitudes are reified.