We derive a novel active learning algorithm in the streaming setting for binary classification tasks. The algorithm leverages weak labels to minimize the number of label requests, and trains a model to optimize a surrogate loss on a resulting set of labeled and weak-labeled points. Our algorithm jointly admits two crucial properties: theoretical guarantees in the general agnostic setting and a strong empirical performance. Our theoretical analysis shows that the algorithm attains favorable generalization and label complexity bounds, while our empirical study on 18 real-world datasets demonstrate that the algorithm outperforms standard baselines, including the Margin Algorithm, or Uncertainty Sampling, a highperforming active learning algorithm favored by practitioners.

The Sparse Vector Technique (SVT) is one of the most fundamental algorithmic tools in differential privacy (DP). It also plays a central role in the state-of-the-art algorithms for adaptive data analysis and model-agnostic private learning. In this paper, we revisit SVT from the lens of Renyi differential privacy, which results in new privacy bounds, new theoretical insight and new variants of SVT algorithms. A notable example is a Gaussian mechanism version of SVT, which provides better utility over the standard (Laplace-mechanism-based) version thanks to its more concentrated noise. Extensive empirical evaluation demonstrates the merits of Gaussian SVT over the Laplace SVT and other alternatives, which encouragingly suggests that using Gaussian SVT as a drop-in replacement could make SVT-based algorithms more practical in downstream tasks.

Anuradha Ranasinghe earned her PhD in robotics from King's College London in 2015, focusing on haptic-based human control in low-visibility conditions. She is now a senior lecturer in robotics at Liverpool Hope University, researching haptics, miniaturized sensors, and perception. Her work has received national and international media attention, including features by EPSRC, CBS Radio, Liverpool Echo, and Techxplore. She has published in leading robotics conferences and journals, and she has presented her findings at various international conferences.

Claire chatted to Robert Siddall from the University of Surrey about novel robot designs inspired by the way real animals move. Robert Siddall is an aerospace engineer with an enthusiasm for unconventional robotics. He is interested in understanding animal locomotion for the benefit of synthetic locomotion, particularly flight. Before becoming a Lecturer at the University of Surrey, he worked at the Max Planck Institute for Intelligent Systems in Stuttgart, Germany, where he studied the arboreal acrobatics of rainforest-dwelling reptiles. His work focuses on the design of novel robots that can tackle important environmental problems.

Claire chatted to Dan Nicholson from Maker Forge about creating open source robotics projects you can do at home. Dan Nicholson is a seasoned Software Engineering Manager with over 20 years of experience as a software engineer and architect. Four years ago, he began exploring robotics as a hobby, which quickly evolved into a large-scale bipedal robotics project that has inspired a wide audience. After making the project open-source and 3D printable, Dan built a vibrant community around it, with over 25k followers. Dan shares insights and project details while collaborating with partners and fellow makers to continue expanding the project's impact.

Claire chatted to Patrick Meier from the Climate Robotics Network about how robots can help scale action on climate change. Patrick Meier has 15 years of leadership and field experience at the intersection of emerging tech, innovation, and social impact. He founded the Climate Robotics Network and currently leads the UK FCDO project on Robotics for Global Development in low- and middle-income countries. Previously, he served as Strategy Lead for Robotics at the Swiss Institute of Technology (EPFL) and Innovation Booster Robotics. He also co-founded and led WeRobotics, an international technology nonprofit with labs in 40 countries.

Catherine Menon is a principal lecturer at the University of Hertfordshire. Her research explores the ethics and safety of autonomous systems, and she has a particular interest in the interaction between safety requirements, ethical imperatives and trust constraints in public-facing AI including assistive robots. She has previously worked as a safety-critical systems engineer in the defence and nuclear sectors, and has been involved in producing and validating several international standards for these domains.

Finding approximate Nash equilibria in zero-sum imperfect-information games is challenging when the number of information states is large. Policy Space Response Oracles (PSRO) is a deep reinforcement learning algorithm grounded in game theory that is guaranteed to converge to an approximate Nash equilibrium. However, PSRO requires training a reinforcement learning policy at each iteration, making it too slow for large games. We show through counterexamples and experiments that DCH and Rectified PSRO, two existing approaches to scaling up PSRO, fail to converge even in small games. We introduce Pipeline PSRO (P2SRO), the first scalable PSRO-based method for finding approximate Nash equilibria in large zero-sum imperfect-information games. P2SRO is able to parallelize PSRO with convergence guarantees by maintaining a hierarchical pipeline of reinforcement learning workers, each training against the policies generated by lower levels in the hierarchy. We show that unlike existing methods, P2SRO converges to an approximate Nash equilibrium, and does so faster as the number of parallel workers increases, across a variety of imperfect information games.