Research
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.

168,695 papers · 148 categories

Trend · papers per month

4489133177 · Jun 202019922001200920172026
48 results for Out(FN)

We define lines of minima in the thick part of Outer space for the free group Fn with n>2 generators. We show that these lines of minima are contracting for the Lipschitz metric. Every fully irreducible outer automorphism of Fn defines such a line a minima. Now let G be a subgroup of the outer automorphism group of Fn …

2009-11-18abs ↗pdf ↗

The outer automorphism group Out(F_2g) of a free group on 2g generators naturally contains the mapping class group of a punctured surface as a subgroup. We define a subsurface projection of the sphere complex of the connected sum of n copies of S^1 x S^2 into the arc complex of the surface and use this to show that thi…

2011-09-13abs ↗pdf ↗

We relate ergodic-theoretic properties of a very small tree or lamination to the behavior of folding and unfolding paths in Outer space that approximate it, and we obtain a criterion for unique ergodicity in both cases. Our main result is that non-unique ergodicity gives rise to a transverse decomposition of the foldin…

2014-10-31abs ↗pdf ↗

We study very small trees from the point of view of reducing systems of free factors, which are analogues of reducing systems of curves for a surface lamination; a non-trivial, proper free factor $F \leq \FN$ reduces TT if and only if FF acts on some subtree of TT with dense orbits. We characterize those trees, call…

2012-11-14abs ↗pdf ↗

We show that the Gromov boundary of the free factor graph for the free group Fn with n>2 generators is the space of equivalence classes of minimal very small indecomposable projective Fn-trees without point stabilizer containing a free factor equipped with a quotient topology. Here two such trees are equivalent if the …

2012-11-07abs ↗pdf ↗

ESE-FN improves elderly activity recognition accuracy.

problem Recognizing individual actions and human-object interactions in elderly activities.
method Exploits multi-modal features from RGB videos and skeleton sequences using ESE attentions and a new Multi-modal Loss.
result ESE-FN achieves best accuracy on ETRI-Activity3D dataset.

Layer normalization improves federated learning with skewed labels.

problem Label skewness in federated learning datasets.
method Identified feature normalization as key mechanism; applied to latent features before classifier.
result Normalization accelerates global training and improves convergence under extreme label shift.

New optimization method improves AUC for binary classification and changepoint detection.

problem Non-convex AUC and sub-optimal points in ROC curves.
method AUM (Area Under Min(FP, FN)) surrogate loss function based on sorting and summing ROC curve points.
result AUM minimization learning algorithm improves AUC and speeds up compared to previous methods.

In this paper, we study local solutions F=(F1,..,Fn) of a general functional equation of the form F1(U1(x,y))+....+Fn(Un(x,y))=0. A such equation will be called an ``abelian functional equation'' (Afe). We will restrict ourselves to the case when the inner functions Ui's are real rational functions. First we prove that…

2002-12-10abs ↗pdf ↗

For an element ΨΨ in the graded vector space Ω(M,TM)Ω^*(M, TM) of tangent bundle valued forms on a smooth manifold MM, a ΨΨ-submanifold is defined as a submanifold NN of MM such that ΨNΩ(N,TN)Ψ_{|N} \in Ω^*(N, TN). The class of ΨΨ-submanifolds encompasses calibrated submanifolds, complex submanifolds and all Lie subgroups in…

2018-04-16abs ↗pdf ↗

We prove that a monomorphic functor F:CompCompF:Comp\to Comp with finite supports is epimorphic, continuous, and its maximal \emptyset-modification FF^\circ preserves intersections. This implies that a monomorphic functor F:CompCompF:Comp\to Comp of finite degree degFndeg F\le n preserves (finite-dimensional) compact ANR's if the spac…

2010-04-03abs ↗pdf ↗

Enhances stability ranges for Torelli and congruence subgroup homologies.

problem Improving stability ranges for specific subgroup homologies.
method Analyzes H2(Torelli subgroup of Aut(Fn)'s), H2(Torelli subgroup of mapping class groups), and Hk(congruence subgroups of GL_n(R)'s).
result Improved central stability ranges for various subgroup homologies.

Study of groups and their quasi-isometrically embedded subgroups.

problem Understanding the structure and properties of groups and their subgroups.
method Abstracting the notion of A/QI triples and using methods from geometric group theory.
result Stability of quasi-isometrically embedded subgroups in finitely generated groups.

We propose a hierarchy for approximate inference based on the Dobrushin, Lanford, Ruelle (DLR) equations. This hierarchy includes existing algorithms, such as belief propagation, and also motivates novel algorithms such as factorized neighbors (FN) algorithms and variants of mean field (MF) algorithms. In particular, w…

2012-07-04abs ↗pdf ↗

Study geometric properties and spectral estimates on warped products.

problem Investigate Ricci curvature and spectral estimates in warped products.
method Establish integral inequalities and sufficient conditions for geometric properties.
result Sufficient conditions for intersection of warped products with totally geodesic hypersurfaces.

We start by studying the distribution of (cyclically reduced) elements of the free groups Fn with respect to their abelianization (or equivalently, their integer homology class. We derive an explicit generating function, and a limiting distribution, by means of certain results (of independent interest) on Chebyshev pol…

2011-06-24abs ↗pdf ↗

Smart Close-out Netting aims to automate close-out netting processes.

problem Inefficiencies in close-out netting processes for financial institutions.
method Standardisation and automation of legal and regulatory processes using a data-driven framework and controlled natural language.
result Standardisation and automation can improve close-out netting processes for prudentially regulated financial institutions.

Simple methods combine statistical tests for out-of-distribution detection.

problem Detecting data points not following the training distribution.
method Combining classical parametric tests (Rao's score test) and a typicality test.
result Combining Fisher's method of test statistics improves out-of-distribution detection accuracy.

We study the behaviour of quasi-geodesics in Out(F_n). Given an element f in Out(F_n) there are several natural paths connecting the origin to f in Out(F_n); for example, paths associated to sequences of Stallings folds and paths induced by the shadow of greedy folding paths in Outer Space. We show that none of these p…

2018-06-26abs ↗pdf ↗

Method enhances anomaly detection using contrastive learning and out-of-distribution data.

problem Improving anomaly detection in datasets with limited out-of-distribution data.
method Proposes a contrastive learning method that incorporates out-of-distribution data to enhance anomaly detection performance.
result The method significantly improves anomaly detection performance, even with limited out-of-distribution data.

A theorem of Farb and Handel asserts that for N4N\ge 4, the natural inclusion from Out(FN)\mathrm{Out}(F_N) into its abstract commensurator is an isomorphism. We give a new proof of their result, which enables us to generalize it to the case where N=3N=3. More generally, we give sufficient conditions on a subgroup ΓΓ of $\m…

2019-01-22abs ↗pdf ↗

New framework infers causal shifts in event sequences under out-of-domain interventions.

problem Inferring causal relationships in event sequences without considering out-of-domain interventions.
method Proposes a new causal framework to define ATE, designs an unbiased ATE estimator, and uses a Transformer-based neural network model.
result Demonstrates superior performance in ATE estimation and goodness-of-fit under out-of-domain-augmented point processes.

Aggregated hold-out (Agghoo) is a method which averages learning rules selected by hold-out (that is, cross-validation with a single split). We provide the first theoretical guarantees on Agghoo, ensuring that it can be used safely: Agghoo performs at worst like the hold-out when the risk is convex. The same holds true…

2019-09-11abs ↗pdf ↗

The paper improves ALO for 1\ell_1-regularized models.

problem Estimating out-of-sample error for 1\ell_1-regularized models.
method Developed a novel theory for 1\ell_1-regularized problems, bounding ALO error.
result For 1\ell_1-regularized problems, ALO error goes to zero as p goes to infinity.

The virtually cyclic dimension of Out(F_N) is finite and related properties are established.

problem Understanding the virtually cyclic dimension of Out(F_N) and related properties.
method Proving properties of finite index congruence subgroups and using exact sequences.
result The virtually cyclic dimension of Out(F_N) is finite.

In-N-Out improves model robustness to out-of-distribution data.

problem Learning robust models with few in-distribution labeled examples.
method Pre-training with auxiliary information and self-training with pseudolabels.
result In-N-Out outperforms auxiliary inputs or outputs alone on both in-distribution and OOD error.

Deep learning models are known to be overconfident in their predictions on out of distribution inputs. This is a challenge when a model is trained on a particular input dataset, but receives out of sample data when deployed in practice. Recently, there has been work on building classifiers that are robust to out of dis…

2018-12-01abs ↗pdf ↗

Paper analyzes high-dimensional portfolio risks and finds empirical out-of-sample relative loss is more reliable.

problem Analyzing risks in high-dimensional portfolios using empirical variance.
method Derives asymptotic behavior of out-of-sample variance and relative loss in high-dimensional settings.
result Empirical out-of-sample relative loss is more reliable than variance in high-dimensional portfolios.

We prove that Out(FN)Out(F_N) is boundary amenable. This also holds more generally for Out(G)Out(G), where GG is either a toral relatively hyperbolic group or a finitely generated right-angled Artin group. As a consequence, all these groups satisfy the Novikov conjecture on higher signatures.

2017-05-19abs ↗pdf ↗

Bayesian Gaussian Processes layer detects out-of-distribution data in medical imaging.

problem Detecting out-of-distribution data in medical imaging tasks.
method Parameter-efficient hierarchical convolutional Gaussian Processes in Wasserstein-2 space.
result Uncertainty estimates enable superior out-of-distribution detection compared to previous methods.