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.

168,657 papers · 148 categories

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10 results for “FDP”

Continuous-Time Functional Diffusion Processescs.LG

FDPs generalize diffusion models to function spaces, enabling efficient image generation.

problem Efficiently generating images from continuous data.

method Introducing FDPs with new mathematical frameworks and training objectives.

result FDPs achieve high-quality image generation with fewer parameters.

Functional Diffusion Processes Generative Models Continuous Data Infinite-Dimensional Score-Based Models

2023-03-01abs ↗pdf ↗

Characterizing the SLOPE Trade-off: A Variational Perspective and the Donoho-Tanner Limitmath.ST

The paper characterizes SLOPE's trade-off between FDP and TPP, showing its power limit and superiority over Lasso.

problem Characterizing the SLOPE trade-off between FDP and TPP.

method Using variational perspective and Gaussian random designs, the paper derives upper and lower bounds on the optimal trade-off.

result SLOPE outperforms Lasso in terms of FDP, TPP, and l2 estimation risk.

SLOPE trade-off FDP TPP Donoho-Tanner limit

2021-05-27abs ↗pdf ↗

Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variablescs.LG

PH-CS selects test inputs with reliability guarantees, adapting FDR to data.

problem Fixed FDR limits adaptability to downstream needs.

method Post-hoc conformal selection with e-variables, e-BH procedure.

result PH-CS provides reliable FDP estimates and competitive FDR control.

conformal selection post-hoc false discovery rate e-variables e-BH procedure

2026-04-13abs ↗pdf ↗

Learning False Discovery Rate Control via Model-Based Neural Networksstat.ME

Enhances FDR control in variable selection using neural networks.

problem Balancing rigorous error control with statistical power in high-dimensional variable selection.

method Learning-augmented T-Rex Selector framework with a neural network trained on synthetic datasets.

result Achieves superior detection of true variables compared to existing approaches.

false discovery rate variable selection neural networks statistical power high-dimensional data

2026-02-05abs ↗pdf ↗

Everywhere Valid Bounds on False Discovery Proportions in Conformal Inferencestat.ME

The paper provides high-probability bounds on false discovery proportions in conformal inference.

problem Existing methods fail to provide high-probability bounds on the realized false discovery proportion.

method Constructing a high-probability envelope for the empirical distribution function of null conformal p-values by sampling from their joint distribution.

result Establishes finite-sample, distribution-free upper bounds on the FDP that hold simultaneously over all possible rejection thresholds.

conformal inference false discovery proportion post-hoc selection outlier detection distribution-free bounds

2026-05-20abs ↗pdf ↗

Null-Calibrated Conformal Selection via Target-Membership Scoresstat.ML

Null-Calibrated Conformal Selection via Target-Membership Scores

problem Identifying test candidates whose unknown responses fall in a target region while controlling the false discovery rate

method Membership-score-based conformal selection

result Finite-sample valid null p-values

conformal selection target membership null calibration empirical FDP

2026-06-21abs ↗pdf ↗

Sharp Trade-Offs in High-Dimensional Inference via 2-Level SLOPEstat.ME

2-level SLOPE improves high-dimensional inference with fewer hyperparameters.

problem High-dimensional linear regression with complex penalty sequences.

method 2-level SLOPE with three hyperparameters, reducing penalty space.

result Sharp trade-off between TPP and FDP, computationally efficient.

2-level SLOPE high-dimensional inference trade-off TPP FDP

2025-07-12abs ↗pdf ↗

A False Discovery Rate Control Method Using a Fully Connected Hidden Markov Random Field for Neuroimaging Datastat.ML

fcHMRF-LIS controls FDR in neuroimaging data, improving power and scalability.

problem Complex spatial dependencies and high variability in FDR control methods for neuroimaging data.

method fcHMRF-LIS integrates LIS-based testing with fcHMRF to model spatial structures efficiently.

result fcHMRF-LIS achieves accurate FDR control, lower FNR, and higher true positives compared to existing methods.

FDR control neuroimaging hidden Markov random field voxel-wise testing Alzheimer's disease

2025-05-27abs ↗pdf ↗

A Decomposable Forward Process in Diffusion Models for Time-Series Forecastingstat.ML

A new diffusion model improves time-series forecasting by preserving seasonal patterns.

problem Improving time-series forecasting accuracy, especially for seasonal data.

method A forward diffusion process that decomposes signals into spectral components, altering only the diffusion process.

result The method maintains high signal-to-noise ratios for dominant frequencies, improving long-term pattern recovery.

time-series forecasting forward diffusion spectral decomposition seasonality signal preservation

2026-01-29abs ↗pdf ↗

Federated fairness-aware classification under differential privacystat.ML

This paper studies fairness and privacy in federated learning, proposing algorithms to balance both.

problem Joint impact of differential privacy and fairness in federated classification.

method Proposes FDP-Fair and CDP-Fair algorithms for demographic disparity constrained classification under federated differential privacy.

result Established theoretical guarantees on privacy, fairness, and excess risk control.

federated learning fairness privacy differential privacy classification

2026-03-25abs ↗pdf ↗