This work proposes an Efficiency-Aware Surrogate-adjusted Estimator (EASE) that directly chooses the sampling law and surrogate to minimize the leading mean squared error (MSE), and demonstrates that EASE consistently outperforms existing estimators for various probabilistic values.
SIGMA converts natural-language emergency commands into priority vectors for a multi-objective actor-critic controller, avoiding manual reward engineering and offers a reliable, language-guided, multi-objective traffic control system with statistical reliability assurance.
Pratham Payra, B. Jagadish, T. Sen et al.· 0 citations
This framework identifies which law each loss in the literature actually optimizes, recovering MDM, UDM, SEDD, and GIDD as special cases; explains why denoiser and cavity coincide for masked diffusion but not for uniform diffusion; proves that a denoiser parameterization makes the uniform ELBO diverge at initialization...
Rodrigo Casado Noguerales, B. Schölkopf, Thomas Hofmann et al.· arXiv.org· 1 citation
Posterior sampling with a pretrained diffusion prior is governed by a conditional score whose intermediate likelihood component is generally intractable. We begin from an ideal one-parameter posterior SDE family in which a stochasticity parameter controls probability-flow transport and stochastic exploration without ch...
Zhaoqiang Liu, T. Pang, Ruibing Wang et al.· 0 citations
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These technologies offer promising alternatives to failing antibiotics, especially against pathogens with complex resistance mechanisms, however, limitations include toxicity concerns, immunological variability, immunological variability, lack of clinical validation, and cost barriers.
Rajeshwar Vodeti, V. Bakshi, Somasundaram Indirabanu et al.· Anti-Infective Agents· 0 citations
The empirical gap between these method families is identified as a variance-reduction effect rather than a difference in RL principle, and a multi-sample KDE value-gradient estimator that reuses rollout groups, together with scale-bounded weight families that retain stable existing recipes while excluding singular ones...
Yi-Xian Xu, Yuanrui Zhang, Sheng-Jie Luo et al.· 3 citations
The findings suggest that AI-powered anomaly detection significantly strengthens observability and security in cloud-based applications, enabling proactive threat mitigation and operational optimization in increasingly complex distributed environments.
Harsh Verma· International Journal of Sci...· 1 citation
A framework for automated theoretical research in causal inference built on the Lean proof assistant, where a proof is checked by a program rather than read by a referee is presented, where a proof is checked by a program rather than read by a referee.
Goal-conditioned reinforcement learning hinges on how the goal is encoded. Contrastive, metric, temporal-distance, and information-theoretic encoders differ in objective. They still share one trait. None of them sees the current state. Such a state-independent embedding cannot mark which part of the goal still needs ac...
Xing Lei, Wenyan Yang, Xuetao Zhang et al.· 0 citations
Geometry-constrained KANs are introduced, a family of edge activations derived from Banach duality maps in which the geometry itself is learned through a scalar exponent $p>1$ per edge, which controls the qualitative response.
An ensemble of LoRA adapters induces a credal set whose lower and upper probabilities expose the spread of plausible predictive distributions rather than collapsing to a single softmax output, and derives two complementary commitment scores.
S. K. Manchingal, S. Nikolenko, Fabio Cuzzolin· 1 citation
A matching lower bound is proved, up to logarithmic factors, across both dense-tail and sparse-tail regimes under a mild nondegeneracy condition, which enables a high-dimensional Gaussian approximation for the general-rank subspace estimation error with an explicit limiting covariance.
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
