AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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18,068 papers
We study constrained contextual bandits with adversarial contexts, where each action yields a random reward and incurs a random cost. The conditional mean reward and cost belong to known function classes and are learned through online regression oracles. The benchmark is a stationary randomized policy whose expected co...
Auto-regressive token generation in large language models is memory-bound because it requires "attending to" key and value tensors (KV cache) of all previous tokens. Prior work aims to improve the efficiency of this decode process by batching multiple requests together, and maximizing batch size subject to GPU memory c...
Fitted Q-iteration (FQI) is well understood when the data come from a fixed distribution and the action set is finite, whereas value-based continuous control collects its data adaptively and maximizes over continuous actions. Both features break the standard analysis: the regression data are dependent, and every densit...
Koopman-based neural MPC models generate time-varying dynamics from historical data, but preserve convexity by enforcing that the system operator is independent of the current control input. This conditional independence constraint limits adaptation to changing dynamics within a single MPC horizon, particularly under t...
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Negative samples play a crucial role in graph contrastive learning (GCL), yet existing methods mainly focus on which negatives should be selected, mined, filtered, or progressively introduced. Less attention has been given to separating the negative candidates available to an anchor from the subset that actively partic...
CircuITS, a novel architecture for probabilistic IMTS forecasting based on sum-product networks (SPNs), is proposed, which is flexible in capturing intricate dependencies between time series channels while structurally guaranteeing valid joint distributions.
A simple hedging principle shows how splitting initial wealth between two strategies achieves a best-of-both-worlds guarantee by retaining the smaller of their regrets and the larger of their growth rates.
The paper proposes new algorithms for computing the regularization path of linear-model fitting (i) with L1 costs for the independent variables and L2 costs for the dependent variables, and (ii) with L2 costs for the independent variables and L1 costs for the dependent variables. Unlike previously proposed algorithms,...
Stability assessment is central to safety-critical systems governed by differential-algebraic equations (DAEs), whose dynamics are constrained by physical laws and admissibility conditions. These systems operate under stochastically varying environmental inputs, so stability must be reassessed as the context distributi...
The transition to electric mobility hinges on maximising aggregate adoption while also facilitating equitable access. This study examines whether the 'charging divide' between households with and without off-street parking reflects a genuine infrastructure constraint or a by-product of socio-economic disparity. Moving...
World models are a promising path to zero-shot embodied control through planning. However, existing world model planners struggle on long-horizon, multi-stage tasks: prediction errors compound and naive search is exponential in the planning horizon. Hierarchy mitigates both by decomposing tasks into shorter, tractable...
Reinforcement learning (RL) and model predictive control (MPC) offer complementary strengths, yet existing MPC-RL frameworks commonly rely on gradient-based optimization and therefore cannot handle the non-differentiable dynamics and objectives common in RL. We propose Soft MPCritic, an MPC-RL framework that learns a s...
The new approach, known as FLEX, could lead to more intuitive, interactive household and assistive robots.
By learning how matter moves, the WorldParticle system could assist with developing soft robots, medicines, and more.
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
A Blog post by Ai2 on Hugging Face
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
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