The field of machine learning is moving towards developing more robust and reliable methods for learning from noisy and imperfect data. Recent research has focused on improving the calibration of predictors, which is essential for making informed decisions in real-world applications. One of the key directions is the development of new loss functions that are robust to label errors and outliers, which can significantly improve the performance of machine learning models. Another important area of research is the study of fairness and robustness in machine learning, with a focus on developing methods that can mitigate the negative impact of biased data and improve the overall reliability of machine learning systems. Noteworthy papers in this area include: Multicalibration yields better matchings, which proposes a new approach to addressing the problem of imperfect predictors in matching problems. Variation-Bounded Loss for Noise-Tolerant Learning, which introduces a novel property related to the robustness of loss functions and proposes a new family of robust loss functions. On Robustness of Linear Classifiers to Targeted Data Poisoning, which presents a technique for measuring the robustness of linear classifiers to targeted data poisoning attacks. Efficient Calibration for Decision Making, which develops a comprehensive theory of calibration for decision making and introduces new definitions and algorithmic techniques. Observational Auditing of Label Privacy, which introduces a novel observational auditing framework for evaluating privacy guarantees in machine learning systems. Beyond Tsybakov: Model Margin Noise and $\mathcal{H}$-Consistency Bounds, which introduces a new low-noise condition for classification and derives enhanced $\mathcal{H}$-consistency bounds under this condition. Loss Functions Robust to the Presence of Label Errors, which proposes two novel loss functions that are robust to label errors.