This paper introduces the notion of upper-linearizable/quadratizable functions, a class that extends concavity and DR-submodularity in various settings, including monotone and non-monotone cases. A general meta-algorithm is devised to convert algorithms for linear/quadratic maximization into ones that optimize upper-li...
It is derived that the asymptotic bias of nonsmooth contractive stochastic approximation (SA) with constant stepsize is proportional to the square root of the stepsize, which stands in sharp contrast to smooth SA.
By leveraging interpolating orthogonal projections in RKHSs, this work uses finitely many point evaluations in place of integration-based basis function expansions to lead to a simpler and more flexible neural-network architecture that remains applicable even when the data domain or the underlying kernel is not explici...
This study investigates the use of stochastic gradient descent to learn operators between general Hilbert spaces and establishes upper bounds for convergence rates of the SGD algorithm and derives a minimax lower bound analysis, illustrating that convergence analysis and regularity conditions quantitatively characteriz...
Lei Shi, Jia-Qi Yang· 2 citations
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This work suggests that fairness can be directly analyzed as a property of the utility function, and suggests modifying utility functions such that they satisfy value of information fairness.
F. H. Jørgensen, S. Weichwald, J. Peters· Journal of Causal Inference· 0 citations
In held-out CodeRM pools, a shared audit reduces the 95th-percentile maximum error across 100 widths by 58% and 40% relative to uniform labeling, turning structural ambiguity into a quantitative prescription for reusable validation.
A per-layer critical update strength is derived, computed from the measured spectrum of $W$ alone through the rectangular spiked-deformation transform, together with an exact secular-equation characterization of the updated spectrum, with no fitted parameters.
Damped sinusoidal oscillations are widely observed in many physical systems, and their analysis provides access to underlying physical properties. However, parameter estimation becomes difficult when the signal decays rapidly, multiple components are superposed, and observational noise is present. In this study, we dev...
Approximate Nearest Neighbor (ANN) search and Approximate Kernel Density Estimation (A-KDE) are fundamental problems at the core of modern machine learning, with broad applications in data analysis, information systems, and large-scale decision making. In massive and dynamic data streams, a central challenge is to desi...
This paper identifies both the easiest and hardest to poison examples based on only clean model information, enabling both rigorous worst-case evaluation and proactive vulnerability-aware defense.
W. Xu, Chenyu Zhang, Yi-Han Wang et al.· 0 citations
This paper introduces N$^2$, a unified Python package and testbed that consolidates a broad class of NN-based methods through a modular, extensible interface and introduces a new NN variant that achieves state-of-the-art results in several settings.
Caleb Chin, Aashish Khubchandani, Harshvardhan Maskara et al.· 0 citations
Some of the sharpest challenges in sampling from the energy functions of physical systems arise at phase transitions, where the density of states changes abruptly and many sampling algorithms stall. Nested sampling is a particle method that traverses the density of states under a hard energy constraint and is known to...
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
Is your company’s AI sovereign? A practical guide for CIOs on AI vendor lock-in, EU Data Act switching, cloud sovereignty, and testing your exit plan before you need it. The post Sovereignty Is the Option to Leave appeared first on GPT-Lab.
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