Justin Johnson

RyeCatcher

·

https://www.jandsgroupllc.com

AI & ML interests

Im a builder

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updated a Space 9 days ago

RyeCatcher/repro-quantifying-error-propagation-and-model-collapse-in-diffusion-models

published a Space 9 days ago

RyeCatcher/repro-quantifying-error-propagation-and-model-collapse-in-diffusion-models

updated a Space 9 days ago

RyeCatcher/repro-scalora-optimally-scaled-low-rank-adaptation-for-efficient-high-rank-fine-tuning

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Reproduction: Convergence Rate of the Last Iterate of Stochastic Proximal Algorithms

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Reproduction: Proximal-IMH: Proximal Posterior Proposals for Independent Metropolis–Hastings with Approximate Operators

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Reproduction: Sinkhorn Treatment Effects: A Causal Optimal Transport Measure

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Reproduction: Last-iterate Convergence of ADMM on Multi-affine Quadratic Equality Constrained Problem

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Reproduction: The Implicit Bias of Depth: From Neural Collapse to Softmax Codes

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Reproduction: Adaptively Robust Resettable Streaming

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Reproduction: Evaluating Bivariate Causal Statements Based on Mutual Compatibility

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Reproduction: A Perturbation Approach to Unconstrained Linear Bandits

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

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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: A Stronger Benchmark for Online Bilateral Trade: From Fixed Prices to Distributions

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Reproduction: Learning-Augmented Online Minimization with Dual Predictions

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Reproduction: MiniMax Learning of Interpretable Factored Stochastic Policies from Conjoint Data, with Uncertainty Quantification

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Reproduction: Belief Propagation Converges to Gaussian Distributions in Sparsely-Connected Factor Graphs

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Reproduction: Positive Distribution Shift as a Framework for Understanding Tractable Learning

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Reproduction: Approximation Preserving Coresets

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Reproduction: Efficient Preference Poisoning Attack on Offline RLHF

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Reproduction: Context-free Recognition with Transformers

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Reproduction: Floating-Point Networks with Automatic Differentiation Can Represent Almost All Floating-Point Functions and Their Gradients

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Reproduction: A theory of learning data statistics in diffusion models, from easy to hard

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Reproduction: Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality

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Reproduction: Bridging the Gap Between Average and Discounted TD Learning

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Reproduction: Anytime Detection of Strategic Deviations in Multi-Agent Systems

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Reproduction: Sinkhorn Normalization of Diffusion Kernels

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