Every story across every category, newest first. Each card links to the original publisher; daily-brief posts open as editorial pages.
arXiv:2607.08979v1 Announce Type: cross Abstract: We study the active learning problem of fixed-confidence top-$k$ identification from noisy pairwise comparisons. In this problem, an algorithm sequentially chooses…
arXiv:2509.03373v2 Announce Type: replace-cross Abstract: Dimensionality reduction methods such as t-SNE and UMAP are popular methods for visualizing data with a potential (latent) clustered structure. They…
arXiv:2607.08971v1 Announce Type: cross Abstract: The stochastic linear bandit, where actions are represented as vectors and rewards are linear, is a central paradigm for sequential decision…
arXiv:2607.09371v1 Announce Type: new Abstract: Flexible machine-learning methods can be sensitive to hidden confounding: they may learn associations induced by unobserved confounders rather than stable signals.…
arXiv:2607.09593v1 Announce Type: new Abstract: We study the problem of multi-snapshot spike deconvolution, where the goal is to recover the locations of sparse impulses from their…
arXiv:2607.08951v1 Announce Type: cross Abstract: From medicine to marketing to social sciences, the promise of tailoring interventions to individuals is undeniable. However, practical applications force weighing…
arXiv:2510.03056v2 Announce Type: replace-cross Abstract: Spectral methods, also known as chaos expansions, are widely used in global sensitivity analysis (GSA), as they leverage orthogonal bases of…
arXiv:2607.08954v1 Announce Type: cross Abstract: We study nonasymptotic convergence of primal-dual methods for a class of nonconvex constrained optimization problems with a convex-composite structure. In this…
arXiv:2604.16238v2 Announce Type: replace-cross Abstract: Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes. Today, such…
arXiv:2602.20578v2 Announce Type: replace-cross Abstract: We study online maximization of non-monotone Diminishing-Return(DR)-submodular functions over down-closed convex sets, a regime where existing projection-free online methods suffer from…
arXiv:2602.21479v3 Announce Type: replace Abstract: Across many risk-sensitive areas, it is critical to continuously audit machine learning systems as we receive more data to quickly determine…
arXiv:2506.22228v2 Announce Type: replace Abstract: Single-cell sequencing is revolutionizing biology by enabling detailed investigations of cell-state transitions. Many biological processes unfold along continuous trajectories, yet it…
arXiv:2401.05330v3 Announce Type: replace-cross Abstract: Causal questions often arise in settings where data are hierarchical: subunits are nested within units. Consider students in schools, cells in…
arXiv:2607.08791v1 Announce Type: new Abstract: Designing effective multi-objective Bayesian optimization (MOBO) algorithms requires balancing many interdependent design choices whose optimal configuration is problem-dependent and typically demands…
arXiv:2607.09025v1 Announce Type: new Abstract: Artificial intelligence (AI) is shifting scientific discovery from task-specific workflows towards autonomous systems that organize exploration with experimental and human feedback…
arXiv:2607.09211v1 Announce Type: new Abstract: While traditional evolutionary algorithms hard-code reproduction, self-replication can emerge spontaneously within digital ``primordial soups''. This paper investigates the co-evolution of this…
arXiv:2607.09202v1 Announce Type: cross Abstract: Continual learning commonly relies on post-hoc mechanisms such as replay, elastic regularization, or distillation. This work argues that forgetting should instead…
arXiv:2607.09480v1 Announce Type: cross Abstract: The human visual system (HVS) employs foveated sampling and eye movements to achieve efficient perception, conserving both metabolic energy and computational…
arXiv:2606.20858v2 Announce Type: replace-cross Abstract: The temporal structure of reward composition in reinforcement learning (RL) is typically hand-designed and held fixed throughout training, leaving the progression…
arXiv:2510.21742v3 Announce Type: replace-cross Abstract: The statistics of correlations are central quantities characterizing the collective dynamics of recurrent neural networks. We derive exact expressions for the…
arXiv:2607.08883v1 Announce Type: new Abstract: Behavioral alignment in large language models often masks fragile internal safety representations. Recent work suggests that refusal behavior is mediated by…
arXiv:2607.08843v1 Announce Type: new Abstract: In artificial and biological neural networks, concepts are often encoded as consistent linear directions in representation space. In deep learning, this…
arXiv:2606.18853v3 Announce Type: replace-cross Abstract: A recent line of work has reframed individual decision trees as linear models on engineered features associated with their splits, opening…
arXiv:2607.08837v1 Announce Type: new Abstract: Exploration is essential to RL since a policy cannot improve by repeatedly sampling the behaviors it already prefers. Standard methods inject…