Geometric Probing for Algorithm Selection in Continuous Black-Box Optimization
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Tree SAE: Learning Hierarchical Feature Structures in Sparse Autoencoders
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Exact Convex Reformulations of Linear Neural Networks via Completely Positive Lifting
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Set-Valued Policy Learning
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Fourier Feature Pyramids for Physics-Informed Neural Networks
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Open-Weight LLM Fine-Tuning Defenses are Susceptible to Simple Attacks
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Towards Regret Guarantees for One-Step Lookahead Bayesian Optimization
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Gradient-based optimization of nuclear criticality experiments using neural surrogate eigenvalue sensitivities
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A prism hierarchy of learning regimes in large linear autoencoders
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Detecting Control and Response Events for AI-Enabled Radio Access Networks
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GRADE: Graph Representation of LLM Agent Dependency and Execution
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In-Context Residual Calibration for Uncertainty Quantification of Energy Time Series over Graphs
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