Y[n.total_condition] Slices DRL behavior dataset, one could then apply 8: IF Entropy(Y It is an indicator of the population of genetic programs' performances. The fitness metric driving our evolution is simply the MSE between the predicted action and the "expert" action (teacher model's action). These individuals are mutated before proceeding to following evolution rounds. We specifically follow 5 different evolution schemes, either one picked stochastically. They are: Crossover: Requires a parent and a donor from two different evolution tournamets.

Recent advances in TCP congestion control (CC) have achieved tremendous success with deep reinforcement learning (RL) approaches, which use feedforward neural networks (NN) to learn complex environment conditions and make better decisions. However, such "black-box" policies lack interpretability and reliability, and often, they need to operate outside the traditional TCP datapath due to the use of complex NNs. This paper proposes a novel two-stage solution to achieve the best of both worlds: first to train a deep RL agent, then distill its (over-)parameterized NN policy into white-box, light-weight rules in the form of symbolic expressions that are much easier to understand and to implement in constrained environments. At the core of our proposal is a novel symbolic branching algorithm that enables the rule to be aware of the context in terms of various network conditions, eventually converting the NN policy into a symbolic tree. The distilled symbolic rules preserve and often improve performance over state-of-the-art NN policies while being faster and simpler than a standard neural network. We validate the performance of our distilled symbolic rules on both simulation and emulation environments.

In recent years, Google Search's shopping features have evolved to make Search a one-stop shop for consumers searching for specific products, deals, and retailers. Shoppers on a budget can scour Search's Shopping tab during major sale events to see which retailer offered the best deal and where. But often, consumers miss out on a product's most productive discount, paying more later because they don't want to wait again. During this year's Google I/O developer conference, Google aims to solve this problem with AI. Shopping in Google's new AI Mode integrates Gemini's capabilities into Google's existing online shopping features, allowing consumers to use conversational phrases to find the perfect product.

Putting on Project Moohan, an upcoming XR headset developed by Google, Samsung, and Qualcomm, for the first time felt strangely familiar. From twisting the head strap knob on the back to slipping the standalone battery pack into my pants pocket, my mind was transported back to February of 2024, when I tried on the Apple Vision Pro during launch day. Also: Xreal's Project Aura are the Google smart glasses we've all been waiting for Only this time, the headset was powered by Android XR, Google's newest operating system built around Gemini, the same AI model that dominated the Google I/O headlines this week. The difference in software was immediately noticeable, from the starting home grid of Google apps like Photos, Maps, and YouTube (which VisionOS still lacks) to prompting for Gemini instead of Siri with a long press of the headset's multifunctional key. While my demo with Project Moohan lasted only about ten minutes, it gave me a fundamental understanding of how it's challenging Apple's Vision Pro and how Google, Samsung, and Qualcomm plan to convince the masses that the future of spatial computing does, in fact, live in a bulkier, space helmet-like device. For starters, there's no denying that the industrial designers of Project Moohan drew some inspiration from the Apple Vision Pro.

We perform volume analysis of Multiplicative Weights Updates (MWU) and its optimistic variant (OMWU) in zero-sum as well as coordination games. Our analysis provides new insights into these game/dynamical systems, which seem hard to achieve via the classical techniques within Computer Science and ML. First, we examine these dynamics not in their original space (simplex of actions) but in a dual space (aggregate payoffs of actions). Second, we explore how the volume of a set of initial conditions evolves over time when it is pushed forward according to the algorithm. This is reminiscent of approaches in evolutionary game theory where replicator dynamics, the continuous-time analogue of MWU, is known to preserve volume in all games. Interestingly, when we examine discrete-time dynamics, the choices of the game and the algorithm both play a critical role. So whereas MWU expands volume in zero-sum games and is thus Lyapunov chaotic, we show that OMWU contracts volume, providing an alternative understanding for its known convergent behavior. Yet, we also prove a no-free-lunch type of theorem, in the sense that when examining coordination games the roles are reversed. Using these tools, we prove two novel, rather negative properties of MWU in zero-sum games.

We address your questions/suggestions below. If the paper is accepted to NeurIPS 2020, we will have one extra page. There are three players, each with two strategies Head and Tail. Indeed, we create more figures and combine them as a video. We also run with Optimistic MWU.

If you're one of the 8% of Americans who say they're willing to pay for AI, Google has a deal for you -- a 250 per month AI subscription. The company unveiled Google AI Ultra today, a plan with the biggest usage limits for Google's suite of AI tools and access to the highest versions of those tools. Google AI Ultra is intended for filmmakers, developers, and creative professionals and gives users access to tools like Veo, Imagen, Whisk, NotebookLM, and a new tool called Flow. Also: Google's popular AI tool gets its own Android app - how to use NotebookLM on your phone Subscribers also get a massive expansion in storage across Google platforms, plus YouTube Premium ( 13.99 per month on its own). Here's a full breakdown of what the new plan includes: Google said the current AI Premium plan is also getting an upgrade -- to Gemini AP Pro.

It was the first time an electric vehicle led the field at NASCAR's most famous race. Chevrolet made history at the 67th Daytona 500 by introducing the 2025 Blazer EV SS as the official pace car. This marked the first time an electric vehicle led the field at NASCAR's most iconic race, a striking symbol of how the automotive world is shifting toward electrification while still honoring its racing heritage. The Blazer EV SS isn't just any electric SUV; it's the quickest SS model Chevrolet has ever built, and it turned heads both on and off the track. JOIN THE FREE "CYBERGUY REPORT": GET MY EXPERT TECH TIPS, CRITICAL SECURITY ALERTS AND EXCLUSIVE DEALS, PLUS INSTANT ACCESS TO MY FREE "ULTIMATE SCAM SURVIVAL GUIDE" WHEN YOU SIGN UP!

Graph diffusion, which iteratively propagates real-valued substances among the graph, is used in numerous graph/network-involved applications. However, releasing diffusion vectors may reveal sensitive linking information in the data such as transaction information in financial network data. Protecting the privacy of graph data is challenging due to its interconnected nature. This work proposes a novel graph diffusion framework with edge-level differential privacy guarantees by using noisy diffusion iterates. The algorithm injects Laplace noise per diffusion iteration and adopts a degree-based thresholding function to mitigate the high sensitivity induced by low-degree nodes. Our privacy loss analysis is based on Privacy Amplification by Iteration (PABI), which to our best knowledge, is the first effort that analyzes PABI with Laplace noise and provides relevant applications. We also introduce a novel -Wasserstein distance tracking method, which tightens the analysis of privacy leakage and makes PABI practically applicable. We evaluate this framework by applying it to Personalized Pagerank computation for ranking tasks. Experiments on real-world network data demonstrate the superiority of our method under stringent privacy conditions.