The field of algorithms and estimation techniques is witnessing significant advancements, driven by the need for efficient and accurate methods to process and analyze large datasets. A key direction in this field is the development of fast and reliable algorithms for estimating various parameters, such as distances, sums, and probabilities, in different contexts. Notable progress is being made in improving the efficiency of algorithms for geometric problems, stochastic probing, and distributed optimization. Researchers are also exploring new approaches to adaptivity gaps, stochastic uncertainty, and event-triggered mechanisms to enhance the performance of these algorithms. Some papers are making notable contributions to the field, including:

• The paper on all-pairs Hamming distances and 0-1 matrix multiplication, which presents a fast randomized algorithm for approximate all-pairs distances in a Hamming space.
• The paper on range counting oracles for geometric problems, which shows the existence of an estimator that approximates the cost of Earth Mover Distance with O(log Δ)-relative error.
• The paper on adaptivity gaps for stochastic probing with subadditive functions, which resolves an open question and shows that the adaptivity gap is O(log^2 n) for general monotone norms.