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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.

169,042 papers · 148 categories

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4 results for “DPD”

Minimizing robust density power-based divergences for general parametric density modelsstat.ME

New method minimizes robust density power-based divergences for general parametric densities.

problem Computational complexity of minimizing DPD for general parametric densities.

method Stochastic approach to minimize DPD for general parametric density models.

result Proposed method can be applied to minimize other density power-based γ-divergences.

density power divergence robust estimation general parametric densities stochastic approach γ-divergences

2023-07-11abs ↗pdf ↗

A Unified Representation of Density-Power-Based Divergences Reducible to M-Estimationcs.IT

Unified representation of density-power-based divergences simplifies estimation to M-estimation.

problem Outliers in density estimation.

method Define a norm-based Bregman density power divergence (NB-DPD) that reduces to M-estimation.

result NB-DPD connects and generalizes existing divergences, highlighting robustness properties.

density-power-divergences M-estimation robustness Bregman divergences outliers

2025-01-27abs ↗pdf ↗

Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometerscs.HC

Automated GMA using accelerometers detects abnormal infant movements with human-level accuracy.

problem Undetected perinatal stroke leads to lifelong disability.

method Wearable accelerometers and Discriminative Pattern Discovery (DPD) for automated GMA.

result Automated method correctly identifies abnormal movements with human-level accuracy.

perinatal stroke infant movement assessment wearable sensors automated screening DPD

2019-02-21abs ↗pdf ↗

What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Biascs.LG

Distillation affects some classes more than others, impacting fairness and bias.

problem Distillation affects some classes more than others, impacting fairness and bias.

method Examined class-wise accuracy and fairness metrics (DPD, EOD) on models trained with different datasets.

result Increasing the distillation temperature improves the distilled student model's fairness and individual fairness.

Knowledge Distillation Fairness Bias Class Bias Demographic Parity Difference

2024-10-10abs ↗pdf ↗