M³C replaces the hard hyperparameter optimization with a sequence of simpler problems using a majorant for the log-determinant approximated via Monte Carlo, with proven high-probability convergence to a critical point under assumptions.
The annals of mathematical statistics , volume=
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A discrete-time constant flux condition on the heat equation forces the domain to be a ball under suitable regularity.
SWE-RL uses RL on software evolution data to train LLMs achieving 41% on SWE-bench Verified with generalization to other reasoning tasks.
A scale-free gain rule applied to variational ELBO paths recovers true factor dimensionality in partially exploratory factor analysis where raw information criteria over-factor.
Macro uses DPO on composite preference pairs to raise validity of multilingual self-generated counterfactual explanations by 12.55% on average over chain-of-thought while preserving minimality.
An information-theoretic DII framework extracts low-dimensional nuclear modes governing conical intersection access and non-radiative decay from high-dimensional nonadiabatic dynamics simulations across multiple molecular systems.
A new adaptive variance estimator for relative sparsity coefficients is introduced that fully utilizes the prior asymptotic normality theorem and incorporates variable selection effects.
Annotation disagreement on toxic language can be moderately predicted from textual features, with high-opposition items proving harder for models to estimate accurately.
Discriminator-informed resampling via normalizing flows reduces error in the EnGMF for low-ensemble regimes on the Ikeda map and Lorenz '63 system.
A novel CVAE with mixture scheduling achieves fine-grained structural control in graph generation, showing high quality and controllability on five datasets.
citing papers explorer
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A Majorization-Minimization with Monte Carlo Approach for Hyperparameter Estimation
M³C replaces the hard hyperparameter optimization with a sequence of simpler problems using a majorant for the log-determinant approximated via Monte Carlo, with proven high-probability convergence to a critical point under assumptions.
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A discrete-time overdetermined problem for the heat equation
A discrete-time constant flux condition on the heat equation forces the domain to be a ball under suitable regularity.
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SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution
SWE-RL uses RL on software evolution data to train LLMs achieving 41% on SWE-bench Verified with generalization to other reasoning tasks.
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Recovering Latent Structures after Variational Bayesian Variable Selection: Fit Assessment and Factor-Number Selection in Partially Exploratory Factor Analysis
A scale-free gain rule applied to variational ELBO paths recovers true factor dimensionality in partially exploratory factor analysis where raw information criteria over-factor.
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Macro: Enhancing Multilingual Counterfactual Explanations through Alignment-as-Preference Optimization
Macro uses DPO on composite preference pairs to raise validity of multilingual self-generated counterfactual explanations by 12.55% on average over chain-of-thought while preserving minimality.
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Machine learning the non-radiative decay modes in photochemical processes
An information-theoretic DII framework extracts low-dimensional nuclear modes governing conical intersection access and non-radiative decay from high-dimensional nonadiabatic dynamics simulations across multiple molecular systems.
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An adaptive variance estimator for relative sparsity
A new adaptive variance estimator for relative sparsity coefficients is introduced that fully utilizes the prior asymptotic normality theorem and incorporates variable selection effects.
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Quantifying and Predicting Disagreement in Graded Human Ratings
Annotation disagreement on toxic language can be moderately predicted from textual features, with high-opposition items proving harder for models to estimate accurately.
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Learning Discriminators for Resampling in the Ensemble Gaussian Mixture Filter through a Normalizing Flow Approach
Discriminator-informed resampling via normalizing flows reduces error in the EnGMF for low-ensemble regimes on the Ikeda map and Lorenz '63 system.
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Fine-Grained Graph Generation through Latent Mixture Scheduling
A novel CVAE with mixture scheduling achieves fine-grained structural control in graph generation, showing high quality and controllability on five datasets.