Hong Seong Pyo

SabaPivot

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Reproduction: The Implicit Bias of Steepest Descent with Mini-batch Stochastic Gradient

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Reproduction: On the Effect of Misspecifying the Embedding Dimension in Low-rank Network Models

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Reproduction: Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness, and Safety

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Reproduction: Allocating Variance to Maximize Expectation

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Reproduction: Understanding Behavior Cloning with Action Quantization

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Reproduction: Discovering Scaling Exponents with Physics-Informed Müntz-Szász Networks

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Reproduction: Quantifying the noise sensitivity of the Wasserstein metric for images

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Reproduction: Accuracy-First Rényi Differential Privacy and Post-Processing Immunity

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Reproduction: Diffusion Flow Matching: Dimension-Improved KL Bounds and Wasserstein Guarantees

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Reproduction: Riemannian Dueling Optimization

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Reproduction: Causal Modeling of Selection in Evolution

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Reproduction: Certificate-Guided Pruning for Stochastic Lipschitz Optimization

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Reproduction: A Perturbation Approach to Unconstrained Linear Bandits

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Reproduction: Online Packet Scheduling with Deadlines and Learning

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Reproduction: Any-dimensional invariant universality

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Reproduction: On the Computational Complexity of Performative Prediction

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Reproduction: Floating-Point Networks with Automatic Differentiation Can Represent Almost All Floating-Point Functions and Their Gradients

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Reproduction: Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality

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Reproduction: The Interplay Between Interpolation and Aggregation in Regression: Optimal Sample Complexity

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Reproduction: Adalina: Adaptive Linear Approximation for the Shapley Value and Beyond

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Reproduction: Reinforcement Learning with Verifiable Rewards: GRPO's Loss, Dynamics, and Success Amplification

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Reproduction: Variational inference via Gaussian interacting particles in the Bures-Wasserstein geometry

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Reproduction: Two-Layer Linear Auto-Regressive Models Estimate Latent States

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Reproduction: The Implicit Bias of Adam and Muon on Smooth Homogeneous Neural Networks

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