Robert Francis PRO

MarxistLeninist

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MarxistLeninist/AGILLM-GB10-1PF

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AGILLM 3 Chat ZeroGPU

Chat with AGILLM 3 Polish‑language assistant

AGILLM 3 Chat

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Repro - Row-Stochastic Matrices Can Provably Outperform Doubly Stochastic Matrices in Decentralized Learning

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Repro - Online Learning with Recency: Algorithms for Sliding-window Streaming Multi-armed Bandits

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Repro - All ERMs Can Fail in Stochastic Convex Optimization: Lower Bounds in Linear Dimension

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Repro - Near-Optimal Convergence of Accelerated Gradient Methods under Generalized and (L0,L1)-Smoothness

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Repro - SPEED-Bench: A Unified and Diverse Benchmark for Speculative Decoding

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Reproduction: Revenue Guarantees of No-Swap-Regret Dynamics in First Price Auctions

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Repro - Combinatorial Sparse PCA Beyond the Spiked Identity Model

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

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Repro - Towards Optimal Robustness in Learning-Augmented Paging

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Repro - Learning to Theorize the World from Observation

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Repro - Reinforcement Learning with Pairwise Preferences in Long-Term Decision Problems

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Repro - Learning-Augmented Online Covering Problems

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Repro - Approximating f-Divergences with Rank Statistics

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Repro - Balancing Understanding and Generation in Discrete Diffusion Models

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Repro - Weak Diffusion Priors Can Still Achieve Strong Inverse-Problem Performance

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Repro - Minimum Distance Summaries for Robust Neural Posterior Estimation

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Repro - FlashOptim: Optimizers for Memory-Efficient Training

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Repro - Diffusion Bridge or Flow Matching? A Unifying Framework and Comparative Analysis

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Repro - Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains

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Repro - Transformers Efficiently Perform In-Context Logistic Regression via Normalized Gradient Descent

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Repro - Sharp description of local minima in the loss landscape of high-dimensional two-layer ReLU neural networks

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Repro - Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling

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