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Reproduction: General Analysis of LMO-based Optimizers: Beyond Bounded Variance
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Abdullah Mujeeb Khawaja
amkkk
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amkkk/repro-a-fine-grained-understanding-of-uniform-convergence-for-halfspacesOrganizations
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Reproduction: A Tight Theory of Error Feedback Algorithms in Distributed Optimization
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Reproduction: Near-Optimal Regret for KL-Regularized Multi-Armed Bandits
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Reproduction: Complexity Bounds for Dirichlet Process Slice Samplers
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Reproduction: Commit to the Bit: Reactive Reinforcement Learning Done Right
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Reproduction: Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regression
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Reproduction: Understanding the Gaps in Satisficing Bandits
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Reproduction: Split Group Knockoffs: Controlling False Discovery Rate in Transformational Group Sparsity
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Reproduction: Near-Optimal Private Linear Regression via Iterative Hessian Mixing
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Reproduction: Signal Strength Estimation in Logistic Regression Using Data Splitting
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Reproduction: Fixed Budget is No Harder Than Fixed Confidence in Best-Arm Identification up to Logarithmic Factors
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Reproduction: Clustering in Deep Stochastic Transformers
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Reproduction: A Task-centric Theory for Iterative Self-Improvement with Easy-to-Hard Curricula
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Reproduction: Dataset Distillation Efficiently Encodes Low-Dimensional Representations from Gradient-Based Learning of Non-Linear Tasks
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Reproduction: Dropout Universality: Scaling Laws and Optimal Scheduling at the Edge-of-Chaos
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Repro - S3GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning
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Repro - Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression
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Repro - Positive Distribution Shift as a Framework for Tractable Learning
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Repro - Approximate Proportionality in Online Fair Division
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Repro - Flat Minima and Generalization: Insights from Stochastic Convex Optimization
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Repro - Rethinking Neural Network Learning Rates: A Stackelberg Perspective
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Repro - On Regret Bounds of Thompson Sampling for Bayesian Optimization
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Reproduction: Revisiting ML Training under Fully Homomorphic Encryption: Convergence Guarantees, Differential Privacy, and Efficient Algorithms
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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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