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TimeLAVA: Learning-Agnostic Valuation for Time Series Data
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Nate Mauer PRO
ParetoOptimal
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Steady-State Behavior of Constant-Stepsize Stochastic Approximation: Gaussian Approximation and Tail Bounds
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A Decision-Theoretic View of Test-Time Training: When, How Far, and Which Directions to Adapt
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Why Deep Jacobian Spectra Separate: Depth-Induced Scaling and Singular-Vector Alignment
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SHAP-Guided Kernel Actor-Critic for Explainable Reinforcement Learning
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Reproducing PonderLM-2 (ICML 2026)
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Reproduction: Finite-time Convergence Analysis of Actor-Critic with Evolving Reward
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Budget-Feasible Mechanisms for Submodular Welfare Maximization in Procurement Auctions
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Reproducing PlugMem (ICML 2026)
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Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets
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Gradient-Free Approaches is a Key to an Efficient Interaction with Markovian Stochasticity
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Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality
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A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms
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On the Role of Batch Size in Stochastic Conditional Gradient Methods
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Distributionally Robust Markov Games with Average Reward
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A Tale of Two Problems: Multi-Task Bilevel Learning Meets Equality Constrained Multi-Objective Optimization
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AdamO: A Collapse-Suppressed Optimizer for Offline RL
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Algorithmic Recourse of In-Context Learning for Tabular Data
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A Risk Decomposition Framework for Pre-hoc Fine-tuning Prediction
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What Makes a Strong Model? A Unified Spectral Analysis of Knowledge Transfer over High-dimensional Linear Regression
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Adaptive Personalized Federated Learning via Multi-task Averaging of Kernel Mean Embeddings
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Approximation Preserving Coresets
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Why Agentic Theorem Prover Works: A Statistical Provability Theory of Mathematical Reasoning Models
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Are Two Datasets Close Enough With Statistical Significance? A Kernel Distributional Closeness Testing Approach
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