RyeCatcher (Justin Johnson)

Reproduction: Best-of-Both-Worlds for Heavy-Tailed Markov Decision Processes

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Reproduction: Robust Linear Dueling Bandits with Post-serving Context under Unknown Delays and Adversarial Corruptions

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Reproduction: Why Are Linear RNNs More Parallelizable?

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Reproduction: Hallucination is a Consequence of Space-Optimality: A Rate-Distortion Theorem for Membership Testing

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Reproduction: Gaussian Mean Field Variational Inference can Overestimate Predictive Variance

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Reproduction: Stochastic Gradient Methods under Heavy-Tailed Noises in Weakly Convex Optimization

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Reproduction: A Fine-Grained Understanding of Uniform Convergence for Halfspaces

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Reproduction: Exact and Approximate Algorithms for Polytree Learning

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Reproduction: The Optimal Sample Complexity of Linear Contracts

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Reproduction: Near-Optimal Regret for KL-Regularized Multi-Armed Bandits

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Reproduction: Theoretical Challenges in Learning for Branch-and-Cut

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Reproduction: Global Convergence of Adaptive Sensing for Principal Eigenvector Estimation

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Reproduction: Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets

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Reproduction: Convergence of Two-Timescale Markovian Stochastic Approximations with Applications in Reinforcement Learning

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Reproduction: Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States

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Reproduction: Softmax as Linear Attention in the Large-Prompt Regime: a Measure-based Perspective

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Reproduction: Prior Diffusiveness and Regret in the Linear-Gaussian Bandit

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

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Reproduction: On the Sharp Input-Output Analysis of Nonlinear Systems under Adversarial Attacks

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Reproduction: Attention's forward pass and Frank-Wolfe

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Reproduction: Dimension-Independent Convergence of Underdamped Langevin Monte Carlo in KL Divergence

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

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Reproduction: Gradient Flow Through Diagram Expansions: Learning Regimes and Explicit Solutions

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Reproduction: Online Packet Scheduling with Deadlines and Learning

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Explore code, traces, and workspace of a research logbook

Justin Johnson

AI & ML interests

Organizations

RyeCatcher 's Spaces 163

Reproduction: Best-of-Both-Worlds for Heavy-Tailed Markov Decision Processes

Reproduction: Robust Linear Dueling Bandits with Post-serving Context under Unknown Delays and Adversarial Corruptions

Reproduction: Why Are Linear RNNs More Parallelizable?

Reproduction: Hallucination is a Consequence of Space-Optimality: A Rate-Distortion Theorem for Membership Testing

Reproduction: Gaussian Mean Field Variational Inference can Overestimate Predictive Variance

Reproduction: Stochastic Gradient Methods under Heavy-Tailed Noises in Weakly Convex Optimization

Reproduction: A Fine-Grained Understanding of Uniform Convergence for Halfspaces

Reproduction: Exact and Approximate Algorithms for Polytree Learning

Reproduction: The Optimal Sample Complexity of Linear Contracts

Reproduction: Near-Optimal Regret for KL-Regularized Multi-Armed Bandits

Reproduction: Theoretical Challenges in Learning for Branch-and-Cut

Reproduction: Global Convergence of Adaptive Sensing for Principal Eigenvector Estimation

Reproduction: Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets

Reproduction: Convergence of Two-Timescale Markovian Stochastic Approximations with Applications in Reinforcement Learning

Reproduction: Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States

Reproduction: Softmax as Linear Attention in the Large-Prompt Regime: a Measure-based Perspective

Reproduction: Prior Diffusiveness and Regret in the Linear-Gaussian Bandit

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

Reproduction: On the Sharp Input-Output Analysis of Nonlinear Systems under Adversarial Attacks

Reproduction: Attention's forward pass and Frank-Wolfe

Reproduction: Dimension-Independent Convergence of Underdamped Langevin Monte Carlo in KL Divergence

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

Reproduction: Gradient Flow Through Diagram Expansions: Learning Regimes and Explicit Solutions

Reproduction: Online Packet Scheduling with Deadlines and Learning

Justin Johnson

AI & ML interests

Organizations

RyeCatcher 's Spaces 163 Sort: Recently updated

Reproduction: Best-of-Both-Worlds for Heavy-Tailed Markov Decision Processes

Reproduction: Robust Linear Dueling Bandits with Post-serving Context under Unknown Delays and Adversarial Corruptions

Reproduction: Why Are Linear RNNs More Parallelizable?

Reproduction: Hallucination is a Consequence of Space-Optimality: A Rate-Distortion Theorem for Membership Testing

Reproduction: Gaussian Mean Field Variational Inference can Overestimate Predictive Variance

Reproduction: Stochastic Gradient Methods under Heavy-Tailed Noises in Weakly Convex Optimization

Reproduction: A Fine-Grained Understanding of Uniform Convergence for Halfspaces

Reproduction: Exact and Approximate Algorithms for Polytree Learning

Reproduction: The Optimal Sample Complexity of Linear Contracts

Reproduction: Near-Optimal Regret for KL-Regularized Multi-Armed Bandits

Reproduction: Theoretical Challenges in Learning for Branch-and-Cut

Reproduction: Global Convergence of Adaptive Sensing for Principal Eigenvector Estimation

Reproduction: Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets

Reproduction: Convergence of Two-Timescale Markovian Stochastic Approximations with Applications in Reinforcement Learning

Reproduction: Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States

Reproduction: Softmax as Linear Attention in the Large-Prompt Regime: a Measure-based Perspective

Reproduction: Prior Diffusiveness and Regret in the Linear-Gaussian Bandit

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

Reproduction: On the Sharp Input-Output Analysis of Nonlinear Systems under Adversarial Attacks

Reproduction: Attention's forward pass and Frank-Wolfe

Reproduction: Dimension-Independent Convergence of Underdamped Langevin Monte Carlo in KL Divergence

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

Reproduction: Gradient Flow Through Diagram Expansions: Learning Regimes and Explicit Solutions

Reproduction: Online Packet Scheduling with Deadlines and Learning

RyeCatcher 's Spaces 163