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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.

168,742 papers · 148 categories

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5 results for “min-max-regret”

Distribution Free Uncertainty for the Minimum Norm Solution of Over-parameterized Linear Regressioncs.LG

The paper explores how over-parameterized linear regression models generalize without violating learning theory principles.

problem Understanding how over-parameterized linear regression models generalize without violating learning theory principles.

method The paper uses the predictive normalized maximum likelihood (pNML) learner to investigate the minimum norm solution of over-parameterized linear regression models.

result The model generalizes well when the test sample lies in a subspace spanned by eigenvectors associated with large eigenvalues of the training data.

over-parameterized linear regression minimum norm solution predictive normalized maximum likelihood generalization distribution-free uncertainty

2021-02-14abs ↗pdf ↗

Minimax Regret Learning for Data with Heterogeneous Subgroupsstat.ME

Develops robust learning methods for datasets with sub-populations.

problem Robust performance and generalization to unseen testing populations in datasets with sub-populations.

method Min-max-regret (MMR) formulation for distribution-free robust hierarchical model.

result Empirical MMR enjoys regret guarantees on training and unseen testing populations.

min-max-regret hierarchical model robust learning sub-populations generalization

2024-05-02abs ↗pdf ↗

Collaborative Min-Max Regret in Grouped Multi-Armed Banditscs.LG

New algorithm balances exploration cost between groups in multi-armed bandits.

problem Balancing exploration cost between groups in multi-armed bandits.

method Introducing Col-UCB algorithm that dynamically coordinates exploration across groups.

result Achieves optimal minimax and instance-dependent collaborative regret up to logarithmic factors.

multi-armed bandits grouped setting collaborative regret UCB algorithm exploration cost

2025-06-12abs ↗pdf ↗

Deep pNML: Predictive Normalized Maximum Likelihood for Deep Neural Networkscs.LG

The Predictive Normalized Maximum Likelihood (pNML) scheme has been recently suggested for universal learning in the individual setting, where both the training and test samples are individual data. The goal of universal learning is to compete with a ``genie'' or reference learner that knows the data values, but is res…

2019-04-28abs ↗pdf ↗

Understanding Bandits with Graph Feedbackcs.LG

New graph feedback model for bandits with improved regret bounds.

problem Understanding how graph structure affects regret in bandit problems.

method Introduced fractional weak domination number and

k

-packing independence number to capture upper and lower bounds on regret. Used strong duality theorem to derive upper and lower bounds.

result Proved general upper and lower bounds on regret for various graph structures, showing tightness up to a logarithmic factor.

bandit problem graph feedback regret bounds fractional weak domination packing independence number

2021-05-29abs ↗pdf ↗