Neural Information Processing Systems http://nips.cc/

Stochastic gradient methods for machine learning and optimization problems are usually analyzed assuming data points are sampled with replacement. In contrast, sampling without replacement is far less understood, yet in practice it is very common, often easier to implement, and usually performs better. In this paper, we provide competitive convergence guarantees for without-replacement sampling under several scenarios, focusing on the natural regime of few passes over the data. Moreover, we describe a useful application of these results in the context of distributed optimization with randomly-partitioned data, yielding a nearly-optimal algorithm for regularized least squares (in terms of both communication complexity and runtime complexity) under broad parameter regimes. Our proof techniques combine ideas from stochastic optimization, adversarial online learning and transductive learning theory, and can potentially be applied to other stochastic optimization and learning problems.

Area under ROC (AUC) is a metric which is widely used for measuring the classification performance for imbalanced data. It is of theoretical and practical interest to develop online learning algorithms that maximizes AUC for large-scale data. A specific challenge in developing online AUC maximization algorithm is that the learning objective function is usually defined over a pair of training examples of opposite classes, and existing methods achieves on-line processing with higher space and time complexity. In this work, we propose a new stochastic online algorithm for AUC maximization. In particular, we show that AUC optimization can be equivalently formulated as a convex-concave saddle point problem. From this saddle representation, a stochastic online algorithm (SOLAM) is proposed which has time and space complexity of one datum. We establish theoretical convergence of SOLAM with high probability and demonstrate its effectiveness on standard benchmark datasets.

Neural Information Processing Systems http://nips.cc/

What if training a robot to handle dirty, dangerous work on the factory floor was as simple as showing it how? Czech startup RoboTwin is doing exactly that, helping factory workers teach robots new skills by demonstration. Instead of writing complex code, workers perform the job once and RoboTwin's technology turns those movements into a robot programme - opening the door to automation for smaller manufacturers. Founded in Prague in 2021, RoboTwin builds handheld devices and no-code software that capture human movements and translate them into instructions for industrial robots. The aim is to make automation faster, simpler and more accessible to manufacturers that do not have specialist robotics programmers.

The ninth edition of the Artificial Intelligence Index Report was published on 13 April 2026. Released on a yearly basis, the aim of the document is to provide readers with accurate, rigorously validated, and globally-sourced data to give insights into the progress of AI and its potential impact on society. The 2026 AI Index Report comprises nine chapters, covering: research and development, technical performance, responsible AI, economy, science, medicine, education, policy and governance, and public opinion. AI capability is accelerating and reaching more people than ever. Model performance continues to improve against benchmarks, and 80% of university students now use generative AI.

This post contains a list of the AI-related seminars that are scheduled to take place between 2 April and 31 May 2026. All events detailed here are free and open for anyone to attend virtually. What Do Our Benchmarks Actually Measure? Vukosi Marivate (University of Pretoria) University of Michigan Zoom link is here . Optimization Over Trained Neural Networks: What, Why, and How? Thiago Serra Azevedo Silva (University of Iowa) Association of European Operational Research Societies To receive the seminar link, sign up to the mailing list .

How do you go about integrating causal knowledge into decision systems or agents? We sat down with Matteo Ceriscioli to find out about his research in this space. This interview is the latest in our series featuring the AAAI/SIGAI Doctoral Consortium participants. Could you start by telling us a bit about your PhD - where are you studying, and what's the broad topic of your research? The idea is to integrate causal knowledge into agents or decision systems to make them more reliable.

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. We sat down with Abdelrahman Sayed Sayed to chat about his work on formal verification applied to autonomous vehicles. Could you tell us a bit about where you're studying and the broad topic of your research? My PhD topic is formal verification of neural ODE (ordinary differential equations) for safety evaluation in autonomous vehicles. Could you say something about formal verification and why it's such an important topic?

This Scammer Used an AI-Generated MAGA Girl to Grift'Super Dumb' Men A med student says he's made thousands of dollars selling photos and videos of a young conservative woman he created using generative tools. Like many medical school students, Sam was broke. The 22-year-old aspiring orthopedic surgeon from northern India got some money from his parents, but he says he spent most of it subsidizing his licensing exams, and he's still saving up to hopefully emigrate to the US after graduation. So he started searching for ways to make additional money online. Sam, who requested a pseudonym to avoid jeopardizing his medical career and immigration status, tried a few things, with varying degrees of legitimacy and success.