The goal of fair clustering is to find clusters such that the proportion of sensitive attributes (e.g., gender, race, etc) in each cluster is similar to the proportion of the entire data. Various fair clustering algorithms have been proposed, which modify standard K-means clustering to satisfy a given fairness constraint. A critical limitation of several existing fair clustering algorithms is that the number of parameters to be learned is proportional to the sample size because the cluster assignment of each datum should be optimized simultaneously with the cluster center, and thus scaling up the algorithms is difficult. In this paper, we propose a new fair clustering algorithm based on finite mixture model called Fair Model-based Clustering (FMC). A main advantage of FMC is that the number of learnable parameters is independent to the sample size and thus can be scaled up easily. In particular, a mini-batch learning is possible to obtain clusters that are approximately fair. Moreover, FMC can be applied to non-metric data (e.g., categorical data) as long as the likelihood is well-defined. Theoretical and empirical justifications of the superiority of the proposed algorithm are provided.

Robust domain adaptation against adversarial attacks is a critical research area that aims to develop models capable of maintaining consistent performance across diverse and challenging domains. In this paper, we derive a new generalization bound for robust risk on the target domain using a novel divergence measure specifically designed for robust domain adaptation. Building upon this, we propose a new algorithm named TAROT, which is designed to enhance both domain adaptability and robustness. Through extensive experiments, TAROT not only surpasses state-of-the-art methods in accuracy and robustness but also significantly enhances domain generalization and scalability by effectively learning domain-invariant features. In particular, TAROT achieves superior performance on the challenging DomainNet dataset, demonstrating its ability to learn domain-invariant representations that generalize well across different domains, including unseen ones. These results highlight the broader applicability of our approach in real-world domain adaptation scenarios.

Algorithmic fairness in clustering aims to balance the proportions of instances assigned to each cluster with respect to a given sensitive attribute. While recently developed fair clustering algorithms optimize clustering objectives under specific fairness constraints, their inherent complexity or approximation often results in suboptimal clustering utility or numerical instability in practice. To resolve these limitations, we propose a new fair clustering algorithm based on a novel decomposition of the fair $K$-means clustering objective function. The proposed algorithm, called Fair Clustering via Alignment (FCA), operates by alternately (i) finding a joint probability distribution to align the data from different protected groups, and (ii) optimizing cluster centers in the aligned space. A key advantage of FCA is that it theoretically guarantees approximately optimal clustering utility for any given fairness level without complex constraints, thereby enabling high-utility fair clustering in practice. Experiments show that FCA outperforms existing methods by (i) attaining a superior trade-off between fairness level and clustering utility, and (ii) achieving near-perfect fairness without numerical instability.

The full paper describes secondary students’ experience in a summer STEM camp in which they puzzled through and then designed escape rooms. The theoretical framework used is experiential learning as explained by John Dewey (1938). In this methodology, students learn from being immersed in situations that at least mimic real life, if not fully real-life. Educators are challenged to find materials for students to learn within some experience. As successive experiences are integrated, the learning progresses. If the learning is within the capability of the student, curiosity to learn more may be aroused, and the learning continues. First, the students were told how escape rooms work and were put into teams for the escape room activities. The instructors set up escape rooms for the students to try their hand at escaping, and competing in teams to see which team had the shortest escape time. After the students all had the opportunity to try out an escape room, they worked in teams to create their own escape room. Another camp session involved basic cryptograph with ciphers, primarily shift ciphers. A requirement of their designs was to include a cipher somewhere in the design. Students were given lock boxes, numerical, alphabetic, and emoji locks of various kinds to use. They worked through a process of 1) deciding on a theme, 2) creating various challenges and clues, 3) sequencing the clues, 4) creating hints, 5) and setting up the escape room. Finally, the four teams each had the opportunity to escape the other teams’ rooms. The paper will describe more fully the students’ engagement and reaction to the escape rooms as well as the collaborative discussions and learning. Informal learning experiences are an important part of the pre-college learning that engages students and increases their interest in STEM. Escape rooms are a great way to immerse students in collaborative learning and boost their desire to learn more.