Hong Seong Pyo

SabaPivot

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AI & ML interests

AI Safety

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Reproduction: Online Packet Scheduling with Deadlines and Learning

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Reproduction: Certificate-Guided Pruning for Stochastic Lipschitz Optimization

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Reproduction: On the Power of (Approximate) Reward Models for Inference-Time Scaling: Sequential Monte Carlo and Beyond

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

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Reproduction: On the Computational Complexity of Performative Prediction

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Reproduction: Compact Conformal Subgraphs

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Reproduction: Linear Bandits beyond Inner Product Spaces, the case of Bandit Optimal Transport

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Reproduction: Understanding Behavior Cloning with Action Quantization

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Reproduction: Fast Estimation for Forest Matrix of Signed Graphs

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Reproduction: Optimal Design for Multinomial Logit Model with Applications to Best Assortment Identification

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Reproduction: Multivariate Distributional Reinforcement Learning Using Sliced Divergences

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Reproduction: Allocating Variance to Maximize Expectation

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Reproduction: Commit to the Bit: Reactive Reinforcement Learning Done Right

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Reproduction: Linear Regression with Unknown Truncation Beyond Gaussian Features

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Reproduction: Stability beyond Bounded Differences: Sharp Generalization Bounds under Finite $L_p$ Moments

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Reproduction: Semi-knockoffs: a model-agnostic conditional independence testing method with finite-sample guarantees

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

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

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

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Reproduction: Learning, Solving and Optimizing PDEs with TensorGalerkin: an efficient high-performance Galerkin assembly algorithm

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Reproduction: Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo

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

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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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