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.

168,695 papers · 148 categories

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4 results for “person-level”

Private Mean Estimation with Person-Level Differential Privacycs.DS

Private mean estimation with multiple samples requires a certain number of people to maintain privacy.

problem Private mean estimation with person-level differential privacy for multiple samples.

method The approach involves estimating the mean up to a distance α in ℓ_2-norm under ε-differential privacy, using algorithms based on the clip-and-noise framework and new analyses.

result The necessary and sufficient number of people to estimate the mean up to distance α in ℓ_2-norm is given by a specific formula.

differential privacy mean estimation person-level multiple samples private statistics

2024-05-30abs ↗pdf ↗

Learning With Differential Privacycs.CR

Differential privacy protects data privacy by adding noise to data.

problem Leakage of sensitive data through common methods like encryption and endpoint protection.

method Randomized response technique to add noise to data collection.

result Differential privacy ensures strong privacy with better utility.

differential privacy data protection randomized response privacy leakage data leakage

2020-06-10abs ↗pdf ↗

Reinforcement Learning Measurement Modelstat.ME

RLMM extends psychometric models to larger tasks.

problem Sequential process data from interactive assessments are not well handled by conventional models.

method RLMM decouples person-level choice sensitivity from task-level value representation through a shared parametric action-value function.

result RLMM achieves higher estimation accuracy and lower runtime than MDP-MM in peg-solitaire simulations and AQUALAB gameplay logs.

Reinforcement Learning Measurement Model Interactive Assessments Sequential Data Psychometrics

2026-05-10abs ↗pdf ↗

Partial Information in a Mean-Variance Portfolio Selection Gameq-fin.MF

The paper analyzes how investors' wealth can decline collectively under partial information.

problem Investors' wealth can decline collectively under partial information.

method The paper derives a Nash equilibrium for mean-variance portfolio selection under relative performance criteria, considering both full and partial information.

result Relative performance criteria can lead to downward self-reinforcement of investors' wealth, which is more pronounced under partial information.

portfolio selection mean-variance relative performance partial information Nash equilibrium

2023-12-07abs ↗pdf ↗