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arXiv:2603.03587v1 Announce Type: cross Abstract: Method validation and study design in causal inference rely on synthetic data with known counterfactuals. Existing simulators trade off distributional...
arXiv:2603.03613v1 Announce Type: cross Abstract: The No Free Lunch (NFL) theorem guarantees equal average performance only under uniform sampling of a function space closed under...
arXiv:2603.03626v1 Announce Type: cross Abstract: Low-dimensional structure in real-world data plays an important role in the success of generative models, which motivates diffusion models defined...
arXiv:2603.03641v1 Announce Type: cross Abstract: Rapid urban population growth drives car travel demand, increasing transport carbon emissions and posing a critical challenge to sustainable development....
arXiv:2603.03682v1 Announce Type: cross Abstract: Accurate polyp segmentation is essential for early colorectal cancer detection, yet achieving reliable boundary localization remains challenging due to low...
arXiv:2603.03684v1 Announce Type: cross Abstract: Recent developments show that AI can prove research-level theorems in mathematics, both formally and informally. This essay urges mathematicians to...
arXiv:2603.03700v1 Announce Type: cross Abstract: Despite the remarkable empirical success of score-based diffusion models, their statistical guarantees remain underdeveloped. Existing analyses often provide pessimistic convergence...
arXiv:2603.03785v1 Announce Type: cross Abstract: Randomized Controlled Trials (RCTs) represent the gold standard for causal inference yet remain a scarce resource. While large-scale observational data...
arXiv:2603.03832v1 Announce Type: cross Abstract: Cardiac arrhythmogenesis is governed by complex electromechanical interactions that are not directly observable in vivo, motivating the development of non-invasive...
arXiv:2603.03843v1 Announce Type: cross Abstract: We consider stochastic non-stationary linear bandits where the linear parameter connecting contexts to the reward changes over time. Existing algorithms...
arXiv:2603.03943v1 Announce Type: cross Abstract: We propose a method to identify nonlinear acyclic networks in continuous time when the dynamics are located on the edges...
arXiv:2603.03998v1 Announce Type: cross Abstract: Quantum Singular Value Transformation (QSVT) provides a unified framework for applying polynomial functions to the singular values of a block-encoded...
arXiv:2603.04049v1 Announce Type: cross Abstract: Rosenbloom and Tsfasman, in their foundational work on the $m$-metric, introduced algebraic-geometric codes defined by multiple points on a smooth...
arXiv:2603.04053v1 Announce Type: cross Abstract: Performance indicators are essential tools for assessing the convergence behavior of multi-objective optimization algorithms, particularly when the true Pareto front...
arXiv:2603.04061v1 Announce Type: cross Abstract: Quantum measurements are the means by which we recover messages encoded into quantum states. They are at the forefront of...
arXiv:2603.04084v1 Announce Type: cross Abstract: Future collider experiments require unprecedented precision in measurements of Higgs, electroweak, and flavour observables, placing stringent demands on event reconstruction....
arXiv:2603.04095v1 Announce Type: cross Abstract: The use of stochastic differential equations in multi-objective optimization has been limited, in practice, by two persistent gaps: incomplete stability...
arXiv:2603.04126v1 Announce Type: cross Abstract: To overcome the physical limitations of scaling monolithic quantum computers, distributed quantum computing (DQC) interconnects multiple smaller-scale quantum processing units...
arXiv:2603.04133v1 Announce Type: cross Abstract: Deep Neural Networks are powerful tools for solving machine learning problems, but their training often involves dense and costly parameter...
arXiv:2603.04172v1 Announce Type: cross Abstract: The Bayesian and Akaike information criteria aim at finding a good balance between under- and over-fitting. They are extensively used...
arXiv:2603.04190v1 Announce Type: cross Abstract: We prove a converse Lyapunov theorem for boundedness of reachability sets for a general class of control systems whose flow...
arXiv:2603.04198v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) are widely used to extract human-interpretable features from neural network activations, but their learned features can vary...
arXiv:2603.04199v1 Announce Type: cross Abstract: Theoretical and applied research into privacy encompasses an incredibly broad swathe of differing approaches, emphasis and aims. This work introduces...
arXiv:2603.04204v1 Announce Type: cross Abstract: Density aggregation is a central problem in machine learning, for instance when combining predictions from a Deep Ensemble. The choice...