arXiv:2602.09181v1 Announce Type: new Abstract: Exploiting the analogy between Gaussian Distributions and Gaussian Processes’ posterior, we present how the weighted Wasserstein Barycenter of Gaussian Processes (W2BGP) can be used to unify, under a common framework, different exotic Bayesian Optimization (BO) tasks. Specifically, collaborative/federated BO, (synchronous) batch BO, and multi-fidelity BO are considered in this paper. Our empirical analysis proves that each one of these tasks requires just an appropriate weighting schema for the W2BGP, while the entire framework […]

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Legal Intern Alexandra Rhodes contributed to this blog post.  EFF filed an amicus brief urging the Arizona District Court to protect public school students’ freedom of speech and privacy by holding that the use of a school-issued laptop or email account does not categorically mean a student is “on campus.” We argued that students need private digital spaces beyond their school’s reach to speak freely, without the specter of constant school surveillance and punishment.   Surveillance Software Exposed a […]

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Hi guys, I just implemented the RL2 algorithm (https://arxiv.org/abs/1611.02779) with PyTorch. The code is here: https://github.com/fatcatZF/RL2-Torch . I used a shared GRU feature extractor, with separate MLP heads for actor and critic. The neural network was optimized with the PPO algorithm. submitted by /u/ZitaLovesCats [link] [comments]

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Private Companies Are Becoming Strategic Targets in Global Cyber Operations In the shadowed corridors of cyberspace, where digital frontiers stretch beyond the horizon of imagination, a profound transformation has taken root. For much of the past two decades, nation-state cyber operations resembled carefully orchestrated expeditions into the fortress-like domains of government agencies, diplomatic networks, and military systems. Today, that focus has shifted like tectonic plates beneath the digital landscape. Increasingly, private organizations find themselves drawn into the swirling […]

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Offline reinforcement learning (RL) optimizes policies from a previously collected static dataset and is an important branch of RL. A popular and promising approach is to regularize actor-critic methods with behavior cloning (BC), which yields realistic policies and mitigates bias from out-of-distribution actions, but can impose an often-overlooked performance ceiling: when dataset actions are suboptimal, indiscriminate imitation structurally prevents the actor from fully exploiting high-value regions suggested by the critic, especially in later training when imitation is already […]

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arXiv:2601.05302v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as autonomous agents in strategic and social interactions. Although recent studies suggest that assigning personality traits to LLMs can influence their behavior, how personality steering affects cooperation under controlled conditions remains unclear. In this study, we examine the effects of personality steering on cooperative behavior in LLM agents using repeated Prisoner’s Dilemma games. Based on the Big Five framework, we first measure basic personality profiles of […]

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Intro: Stock Price Prediction, Machine Learning, and Quantum ML Today, we’re diving into the intersection of quantum computing and machine learning, exploring quantum machine learning and its application in stock price prediction. Our main goal is to compare the performance of a quantum neural network for stock price time series forecasting with a simple single-layer MLP. To facilitate this project, we’ll be utilizing the Historical API endpoint offered by Financial Modeling Prep (FMP) for reliable and accurate data which is […]

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Modern machine learning models are deployed in diverse, non-stationary environments where they must continually adapt to new tasks and evolving knowledge. Continual fine-tuning and in-context learning are costly and brittle, whereas neural memory methods promise lightweight updates with minimal forgetting. However, existing neural memory models typically assume a single fixed objective and homogeneous information streams, leaving users with no control over what the model remembers or ignores over time. To address this challenge, we propose a generalized neural […]

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