On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,341 papers · 148 categories

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7 results for “Rome”

ROME: Robust Multi-Modal Density Estimatorcs.LG

ROME improves density estimation for multi-modal, non-normal data.

problem Robust multi-modal density estimation in non-normal, highly correlated distributions.

method ROME uses clustering to segment multi-modal data into uni-modal clusters, then combines KDE estimates for each cluster.

result ROME outperforms state-of-the-art methods and is more robust to various distributions.

density estimation multi-modal robustness non-normal clustering

2024-01-19abs ↗pdf ↗

Robust Mixture Models for Algorithmic Fairness Under Latent Heterogeneitystat.ML

ROME improves algorithmic fairness by learning latent group structure robustly.

problem Latent subgroup disparities and distribution shifts in machine learning models.

method ROME uses an Expectation-Maximization algorithm for linear models and a neural Mixture-of-Experts for nonlinear settings.

result ROME significantly improves fairness compared to standard methods while maintaining average performance.

algorithmic fairness latent heterogeneity ensemble learning Expectation-Maximization Mixture-of-Experts

2025-09-22abs ↗pdf ↗

RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health Interventionsstat.ML

RoME optimizes mobile health interventions by modeling user and time-specific effects.

problem Challenges in optimizing mobile health interventions due to participant heterogeneity, nonstationarity, and nonlinear relationships.

method RoME uses a Robust Mixed-Effects contextual bandit algorithm with random effects, network cohesion penalties, and debiased machine learning.

result RoME achieves robust regret bounds even with complex baseline rewards, demonstrating superior performance in simulations and studies.

mobile health bandit algorithm contextual bandit random effects nonstationarity

2023-12-11abs ↗pdf ↗

Spatio-temporal analysis of micro economic activities in Rome reveals patterns of mixed-use urban evolutionphysics.soc-ph

Study reveals patterns of mixed-use urban evolution in Rome.

problem Understanding urban growth and societal evolution in Rome.

method Quantitative analysis of historical commercial activities in Rome.

result Double exponential trend in the age of commercial activities, indicating economic effects and crisis periods.

Rome Urban evolution Commercial activities Spatio-temporal analysis Economic effects

2016-02-26abs ↗pdf ↗

Residual Overfit Method of Explorationcs.LG

A new exploration method for bandit and reinforcement learning.

problem Uncertainty quantification in exploration.

method Residual Overfit Method of Exploration (ROME).

result ROME drives exploration towards actions with overfitting.

exploration bandit reinforcement learning overfitting contextual

2021-10-06abs ↗pdf ↗

FAQ on the g-theorem and the hard Lefschetz theorem for face ringsmath.CO

Reviews g-theorem and hard Lefschetz theorem for face rings.

problem Understanding and proving the g-theorem and hard Lefschetz theorem for face rings.

method Perturbations of maps, biased Poincaré pairings, cobordism argument, edge-contractions.

result Alternative and alternative arguments for the Lefschetz property.

g-theorem hard Lefschetz theorem face rings simplicial spheres cobordism

2019-06-14abs ↗pdf ↗

The ROMES method for statistical modeling of reduced-order-model errormath.NA

This work presents a technique for statistically modeling errors introduced by reduced-order models. The method employs Gaussian-process regression to construct a mapping from a small number of computationally inexpensive `error indicators' to a distribution over the true error. The variance of this distribution can be…

2014-05-20abs ↗pdf ↗