NIAMH MAHER

nmaher

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nmaher/repro-active-continual-learning-with-metaplastic-binary-bayesian-neural-networks

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ICML-2026-agent-repro/challenge:Fix stale claims_anchored.json entries for STAND (NJes6aeTem)

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nmaher/repro-spatial-conformal-inference-through-localized-quantile-regression

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nmaher 's Spaces 33

Reproduction: Active Continual Learning with Metaplastic Binary Bayesian Neural Networks

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Reproduction: Spatial Conformal Inference through Localized Quantile Regression

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Reproduction: Predictive variational inference: Learn the predictively optimal posterior distribution

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Reproduction: Finite-Width Neural Tangent Kernels from Feynman Diagrams

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Reproduction: Generalization of Gibbs and Langevin Monte Carlo Algorithms in the Interpolation Regime

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Reproduction: Conflicting Biases at the Edge of Stability: Norm versus Sharpness Regularization

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Reproduction: Ellipsoidal Time Series Forecasting

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Reproduction: STAND: Self-Aware Precondition Induction for Interactive Task Learning

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Reproduction: Conformal Prediction for Early Stopping in Mixed Integer Optimization

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Reproduction: Universal Multiclass Transductive Online Learning

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Reproduction: Characterizing the Effect of Noise in Language Generation in the Limit

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Reproduction: WorldComp2D: Spatio-semantic Representations of Object Identity and Location from Local Views

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Reproduction: Transformers with RL or SFT Provably Learn Sparse Boolean Functions, But Differently

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Reproduction: A General Framework for Dynamic Consistent Submodular Maximization

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Reproduction: Near-Optimal Regret for Policy Optimization in Contextual MDPs with General Offline Function Approximation

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Reproduction: Toward Scalable and Valid Conditional Independence Testing with Spectral Representations

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Reproduction: Differentially Private Synthetic Data via APIs 4: Tabular Data

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Reproduction: CSPO: Constraint-Sensitive Policy Optimization for Safe Reinforcement Learning

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Reproduction: Function-Valued Causal Influence in Nonlinear Time Series

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Reproduction: CausalProfiler: Generating Synthetic Benchmarks for Rigorous and Transparent Evaluation of Causal Machine Learning

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Reproduction: Divide and Learn: Multi-Objective Combinatorial Optimization at Scale

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Reproduction: From Muon to Gluon: Bridging Theory and Practice of LMO-based Optimizers for LLMs

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Reproduction: On the Theory of Continual Learning with Gradient Descent for Neural Networks

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Reproduction: Tackling Fake Forgetting through Uncertainty Quantification

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