Ryan Wiley PRO

rdubwiley

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rdubwiley/repro-understanding-catastrophic-forgetting-in-lora-via-mean-field-attention-dynamics

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Repro - Convergence Rate of the Last Iterate of Stochastic Proximal Algorithms

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Repro - Fair Decisions from Calibrated Scores: Achieving Optimal Classification While Satisfying Sufficiency

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Repro - Prediction-Powered Risk Monitoring of Deployed Models for Detecting Harmful Distribution Shifts

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Repro - Feature Resemblance: Towards a Theoretical Understanding of Analogical Reasoning in Transformers

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Repro - A Tight Theory of Error Feedback Algorithms in Distributed Optimization

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Repro - Adversarially Robust Control of Conditional Value-at-Risk via Rockafellar-Uryasev Conformal Inference

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Repro - Differentiable Optimization Layers for Guaranteed Fairness in Deep Learning

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Repro - Depth over Fidelity in Fixed-Budget Noisy Evolution Strategies

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Repro - Provably Data-driven Multiple Hyper-parameter Tuning with Structured Loss Function

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Repro - Muon in Associative Memory Learning: Training Dynamics and Scaling Laws

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Repro - Rank-Learner: Orthogonal Ranking of Treatment Effects

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

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Repro - A Two-Layer Framework for Joint Online Configuration Selection and Admission Control

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Repro - MIRA: A Score for Conditional Distribution Accuracy and Model Comparison

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Repro - Operator Splitting with Hamilton-Jacobi-based Proximals

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Mira Repro Bundle

Repro - On Densest $k$-Subgraph Mining and Diagonal Loading: Optimization Landscape and Finite-Step Exact Convergence Analysis

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Repro - Welfare-Optimal Classification with Accuracy Auctions

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Repro - Optimal Anytime Algorithms for Online Convex Optimization with Adversarial Constraints

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Repro - Minimum Distance Summaries for Robust Neural Posterior Estimation

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Repro - Optimal and Scalable MAPF via Multi-Marginal Optimal Transport and Schrödinger Bridges

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Repro - Randomized Feasibility Methods for Constrained Optimization with Adaptive Step Sizes

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Repro - Improved Analysis of the Accelerated Noisy Power Method with Applications to Decentralized PCA

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Repro - Curated Synthetic Data Doesn’t Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences

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