This paper introduces a server-driven orchestration layer for parameter-server training that adaptively bounds iteration skew among workers at runtime, using recent pushtimestamp telemetry to minimize straggler waiting while preserving convergence in distributed AI systems. The mechanism selects per-iteration, per-worker staleness allowances within a configurable band, turning gradient exchange into a feedbackcontrolled service that balances throughput and consistency under both homogeneous and heterogeneous GPU clusters. A formal analysis establishes convergence guarantees comparable to bounded-staleness methods via an O( √ […]

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We study contextual multi-armed bandit problems where the context comes from a metric space and the payoff satisfies a Lipschitz condition with respect to the metric. Abstractly, a contextual multi-armed bandit problem models a situation where, in a sequence of independent trials, an online algorithm chooses, based on a given context (side information), an action from a set of possible actions so as to maximize the total payoff of the chosen actions. The payoff depends on both the […]

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Raga identification in Indian Art Music (IAM) remains challenging due to the presence of numerous rarely performed Ragas that are not represented in available training datasets. Traditional classification models struggle in this setting, as they assume a closed set of known categories and therefore fail to recognise or meaningfully group previously unseen Ragas. Recent works have tried categorizing unseen Ragas, but they run into a problem of catastrophic forgetting, where the knowledge of previously seen Ragas is diminished. […]

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Suppose you have two n × n matrices, A and B, and you would like to find a rotation matrix Ω that lines up B with A. That is, you’d like to find Ω such that A = ΩB. This is asking too much, except in the trivial case of A and B being 1 × 1 matrices. You could view the matrix equation above as a set of n² equations in real numbers, but the space of orthogonal matrices only has n(n − […]

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arXiv:2602.06110v1 Announce Type: new Abstract: Machine learning in clinical settings must balance predictive accuracy, interpretability, and privacy. Models such as logistic regression (LR) offer transparency, while neural networks (NNs) provide greater predictive power; yet both remain vulnerable to privacy attacks. We empirically assess these risks by designing attacks that identify which public datasets were used to train a model under varying levels of adversarial access, applying them to LORIS, a publicly available LR model for immunotherapy response prediction, […]

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arXiv:2601.19952v1 Announce Type: new Abstract: Real-time voice agents face a dilemma: end-to-end models often lack deep reasoning, while cascaded pipelines incur high latency by executing ASR, LLM reasoning, and TTS strictly in sequence, unlike human conversation where listeners often start thinking before the speaker finishes. Since cascaded architectures remain the dominant choice for complex tasks, existing cascaded streaming strategies attempt to reduce this latency via mechanical segmentation (e.g., fixed chunks, VAD-based splitting) or speculative generation, but they frequently […]

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arXiv:2501.02505v4 Announce Type: replace-cross Abstract: Ranking items based on pairwise comparisons is common, from using match outcomes to rank sports teams to using purchase or survey data to rank consumer products. Statistical inference-based methods such as the Bradley-Terry model, which extract rankings based on an underlying generative model, have emerged as flexible and powerful tools to tackle ranking in empirical data. In situations with limited and/or noisy comparisons, it is often challenging to confidently distinguish the performance of […]

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