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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.

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9 results for “C_m-groups”

Virtually Symmetric representations and marked Gauss diagramsmath.GT

The paper defines new representations and groups related to virtual links.

problem Defining and studying new representations of virtual braid groups and link groups.

method Introducing virtually symmetric representations, virtual link groups, and marked Gauss diagrams.

result Established equivalence of many known representations to virtually symmetric representations.

virtual braid groups virtual link groups marked Gauss diagrams peripheral structure C_m-groups

2020-07-15abs ↗pdf ↗

The Hidden M-Grouphep-th

The hidden M-algebra is integrated into a super-Lie group, allowing for compactification of extra dimensions.

problem Integrating the hidden M-algebra into a super-Lie group to model super-exceptional spacetimes.

method Left-invariant extension of the decomposed M-theory 3-form, providing a computer-checked re-derivation and streamlined conception of super-Lie groups.

result Lattice subgroups of the hidden M-group allow toroidal compactification of hidden dimensions, akin to topological T-duality.

super-Lie groups M-theory super-exceptional geometry compactification hidden dimensions

2024-11-18abs ↗pdf ↗

Deformations of smooth functions on

2

-torusmath.GT

Let $f$ be a Morse function on a smooth compact surface $M$ and $\mathcal{S}'(f)$ be a group of $f$ -preserving diffeomorphisms of $M$ which are isotopic to the identity map. Let also $G(f)$ be a group of automorphisms of the graph of $f$ induced by elements from $\mathcal{S}'(f)$ , and $Δ'$ be a subgroup of $\mathcal{S…

2019-03-05abs ↗pdf ↗

Minimax Optimal Algorithms with Fixed-

k

-Nearest Neighborsmath.ST

The paper optimizes k-NN for distributed learning with minimax optimal performance.

problem Minimizing error rates in classification, regression, and density estimation.

method Optimal aggregation of fixed-k nearest neighbors from multiple subsets of data.

result Achieves minimax optimal error rates up to a logarithmic factor.

k-NN minimax distributed learning optimal aggregation error rates

2022-02-05abs ↗pdf ↗

Distributionally Robust Linear Regression With Block Lewis Weightscs.LG

Algorithm solves robust linear regression with block Lewis weights.

problem Group distributionally robust least squares problem.

method Algorithm based on geometric construction and block Lewis weights, using accelerated proximal methods.

result Improves over known methods for moderate accuracy regimes and matches state-of-the-art guarantees.

robust regression least squares distributionally robust block Lewis weights accelerated proximal methods

2026-06-30abs ↗pdf ↗

Efficient Algorithms for Empirical Group Distributionally Robust Optimization and Beyondcs.LG

Paper develops efficient algorithms for robust optimization across multiple groups.

problem Minimizing maximal empirical risk across distinct groups in robust optimization.

method Develops ALEG and ALEM algorithms for two-level finite-sum convex-concave minimax optimization.

result Achieves ε-accuracy with complexity O(m√(nlnm/ε)) and outperforms state-of-the-art methods.

Empirical GDRO Distributionally Robust Optimization Two-level Minimax Variance Reduction Mirror Prox

2024-03-06abs ↗pdf ↗

A General Framework for Clustering and Distribution Matching with Bandit Feedbackcs.LG

Develops a framework for clustering and distribution matching with bandit feedback.

problem Clustering and distribution matching problems with limited feedback.

method General framework using

K

-armed bandit model, Track-and-Stop method, and Frank--Wolfe algorithm.

result Average number of arm pulls matches lower bound, with asymptotic convergence to fundamental limit.

bandit feedback clustering distribution matching online algorithms Frank-Wolfe algorithm

2024-09-08abs ↗pdf ↗

On the H-type deviation of step two Carnot groupsmath.DG

The H-type deviation measures how close step two Carnot groups are to H-type groups.

problem Quantifying how close step two Carnot groups are to H-type groups.

method Defined and analyzed the H-type deviation for step two Carnot groups.

result Explicitly computed H-type deviation for product of Heisenberg groups and verified the conjectural upper bound.

Carnot groups H-type groups step two groups Laplacian rigidity

2023-12-11abs ↗pdf ↗

q

-Series Invariants of Three-Manifolds and Knots-Quivers Correspondencemath-ph

The thesis examines the relationship between three-manifold invariants and knot theory, finding equalities and patterns.

problem Examining the relationship between three-manifold invariants and knot theory.

method Analytic continuation and quiver representation theory.

result Found equalities and patterns in knot theory and quiver representation.

three-manifold invariants knot theory quiver representation Witten-Reshetikhin-Turaev HOMFLY-PT polynomial

2024-12-14abs ↗pdf ↗