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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.

168,982 papers · 148 categories

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4 results for “sub-trajectories”
Statistically Discriminative Sub-trajectory Miningstat.ML

SDSM extracts statistically significant sub-trajectories from large trajectory datasets.

problem Discerning moving patterns that are more characteristic of one group of trajectories than another.
method Statistically Discriminative Sub-trajectory Mining (SDSM) method using tree representation and permutation-based statistical inference.
result SDSM efficiently extracts statistically significant sub-trajectories from massive trajectory datasets.
trajectory miningsub-trajectoriesstatistical significancetree representationpermutation-based inference
2019-05-06abs ↗pdf ↗
Learning an Efficient Optimizer via Hybrid-Policy Sub-Trajectory Balancecs.LG

Lo-Hp decouples weight generation into local and global policies to improve flexibility and efficiency.

problem Over-coupling and long-horizon issues in current optimization methods.
method Hybrid-Policy Sub-Trajectory Balance objective.
result Learning local optimization policies addresses long-horizon issues and enhances global weight generation.
optimizationneural networksweight generationlocal policiesglobal policies
2025-11-01abs ↗pdf ↗
Map Enhanced Route Travel Time Prediction using Deep Neural Networkscs.LG

Enhanced travel time prediction using deep neural networks and road network information.

problem Improving travel time estimation using deep learning models.
method Proposes incorporating road network information into deep learning models for travel time prediction.
result Improved travel time prediction, especially with limited training data.
travel time predictiondeep neural networksroad networkshistorical tripsnode embeddings
2019-11-06abs ↗pdf ↗
Learning and Concentration for High Dimensional Linear Gaussians: an Invariant Subspace Approachstat.ML

Study non-asymptotic bounds on correlation in high-dimensional linear systems, revealing invariant subspaces and bottlenecks.

problem Understanding correlation and mixing in high-dimensional linear systems with Gaussian noise.
method Sampling from sub-trajectories, using Talagrand's inequality, and analyzing invariant subspaces.
result Large discrepancy between algebraic and geometric multiplicity leads to bottlenecks between invariant subspaces.
Linear systemsGaussian noiseInvariant subspacesTalagrand's inequalityHigh dimensions
2023-04-04abs ↗pdf ↗