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Reproduction: Anytime Detection of Strategic Deviations in Multi-Agent Systems
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Hong Seong Pyo
SabaPivot
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Reproduction: Row-Stochastic Matrices Can Provably Outperform Doubly Stochastic Matrices in Decentralized Learning
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Reproduction: An Odd Estimator for Shapley Values
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Reproduction: Clipping Makes Distributed and Federated Asynchronous SGD Robust to Stragglers
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Reproduction: Unveiling the Structure of Do-Calculus Reasoning via Derivation Graphs
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Reproduction: Non-Uniform Noise-to-Signal Ratio in the REINFORCE Policy-Gradient Estimator
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Reproduction: Discretized Density-Guided Source-Free Domain Adaptation for Regression
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Reproduction: Bridging Time and Frequency: A Joint Modeling Framework for Irregular Multivariate Time Series Forecasting
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Repro - To Grok Grokking: Provable Grokking in Ridge Regression
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Repro - Stability Analysis of Sharpness-Aware Minimization
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Repro - Who Said Neural Networks Aren't Linear?
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Repro - Success-Conditioning as Policy Improvement: The Optimization Problem Solved by Imitating Success
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Repro - Softmax as Linear Attention in the Large-Prompt Regime
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Repro - Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models
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Repro - On Structured State-Space Duality
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Repro - Understanding Generalization and Forgetting in In-Context Continual Learning
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Repro - Flat Minima and Generalization: Insights from Stochastic Convex Optimization
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Reproduction: A Tight Theory of Error Feedback Algorithms in Distributed Optimization
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Reproduction: Finding Most Influential Sets
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Reproduction: CAffNet: Hard Constraint-Affine Neural Networks
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Reproduction: HiPPO Zoo: Explicit Memory Mechanisms for Interpretable State Space Models
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Reproduction: Balancing Learning Rates Across Layers: Exact Two-Step Dynamics and Optimal Scaling in Linear Neural Networks
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Reproduction: Optimal Unconstrained Self-Distillation in Ridge Regression: Strict Improvements, Precise Asymptotics, and One-Shot Tuning
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Reproduction: Well-Posed KL-Regularized Control via Wasserstein and Kalman–Wasserstein KL Divergences
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