We consider the penalized distributionally robust optimization (DRO) problem with a closed, convex uncertainty set, a setting that encompasses learning using f -DRO and spectral/ L-risk minimization.

We consider the penalized distributionally robust optimization (DRO) problem with a closed, convex uncertainty set, a setting that encompasses learning using $f$-DRO and spectral/$L$-risk minimization. We present Drago, a stochastic primal-dual algorithm which combines cyclic and randomized components with a carefully regularized primal update to achieve dual variance reduction. Owing to its design, Drago enjoys a state-of-the-art linear convergence rate on strongly convex-strongly concave DRO problems witha fine-grained dependency on primal and dual condition numbers. The theoretical results are supported with numerical benchmarks on regression and classification tasks.

We consider the penalized distributionally robust optimization (DRO) problem with a closed, convex uncertainty set, a setting that encompasses learning using f -DRO and spectral/ L -risk minimization. We present Drago, a stochastic primal-dual algorithm which combines cyclic and randomized components with a carefully regularized primal update to achieve dual variance reduction. Owing to its design, Drago enjoys a state-of-the-art linear convergence rate on strongly convex-strongly concave DRO problems witha fine-grained dependency on primal and dual condition numbers. The theoretical results are supported with numerical benchmarks on regression and classification tasks.

We propose DRAGO, a novel approach for continual model-based reinforcement learning aimed at improving the incremental development of world models across a sequence of tasks that differ in their reward functions but not the state space or dynamics. DRAGO comprises two key components: Synthetic Experience Rehearsal, which leverages generative models to create synthetic experiences from past tasks, allowing the agent to reinforce previously learned dynamics without storing data, and Regaining Memories Through Exploration, which introduces an intrinsic reward mechanism to guide the agent toward revisiting relevant states from prior tasks. Together, these components enable the agent to maintain a comprehensive and continually developing world model, facilitating more effective learning and adaptation across diverse environments. Empirical evaluations demonstrate that DRAGO is able to preserve knowledge across tasks, achieving superior performance in various continual learning scenarios.

In this work, we provide a refined analysis of the UCBVI algorithm (Azar et al., 2017), improving both the bonus terms and the regret analysis. Additionally, we compare our version of UCBVI with both its original version and the state-of-the-art MVP algorithm. Our empirical validation demonstrates that improving the multiplicative constants in the bounds has significant positive effects on the empirical performance of the algorithms.

We consider the penalized distributionally robust optimization (DRO) problem with a closed, convex uncertainty set, a setting that encompasses the $f$-DRO, Wasserstein-DRO, and spectral/$L$-risk formulations used in practice. We present Drago, a stochastic primal-dual algorithm that achieves a state-of-the-art linear convergence rate on strongly convex-strongly concave DRO problems. The method combines both randomized and cyclic components with mini-batching, which effectively handles the unique asymmetric nature of the primal and dual problems in DRO. We support our theoretical results with numerical benchmarks in classification and regression.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Sylvester Stallone isn't finished with the "Rocky" franchise just yet. On Sunday, the 74-year-old actor took to Instagram to announce that a director's cut of the film "Rocky IV" is on the way. He shared the news alongside a painted image from the film featuring his own character, Rocky Balboa, and Dolph Lundgren's Ivan Drago who has the famous line: "If he dies, he dies." "For the 35th anniversary Rocky 4 Is getting a new DIRECTORS cut by me," Stallone announced.

Drascalita Haut: Today we're going to talk about functions, and in particular Functions as a Service. It applies it to AI in order to present a solution that seems to bring strategic advantages when deploying AI services at scale. During this session, it may feel like we're dancing a bit, moving through tools, new technologies, maybe you might even see some new steps like workflows or methods to work with AI. And for those of you that know salsa, you know that it starts with a step forward. So today, I'm going to start with some bold statements, but bear with us, I'm going to take a step back, and then me and AK are going to rehearse something through a live demo, which hopefully is going to go just fine, to illustrate what we're talking about. Let me start with a step forward, FaaS value prop. What does FaaS bring that more and more people are talking about? I came with three reasons. Number one is FaaSter to prototype, FaaSter to create services, because we work with code, with functions, just code, and we just push the code as it is. Second, never pay for idle. FaaS platforms have the capability to shut down the parts of the system that are not used, so we don't incur any cost. And the third one is a low maintenance overhead. That's because FaaS platforms usually take away the burden to create containers, keeping them up to date, apply security updates, auto-scaling the functions, deploy them in multiple regions. In other words, FaaS boldly claims that you will find it easier to build more services, and you're going to pay less. Now, this is a pretty bold statement, isn't it? So allow me to take a step back and look at how developers are producing microservices today. A few years ago, we realized that microservices are better than monoliths because in essence, they add flexibility, and they simplify the experience. At the same time, it's also less risky to independently update parts of the system. And I would assume that many of us know what microservices are. A very high-level microservice architecture is in this slide. So the final solution basically consists of isolated pieces, with its own independent deployment lifecycle. Now, microservices used to be deployed in their own VMs, and then containers came and it was such a revolution because we're able to correctly run multiple services in isolation in the same VM.