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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.

168,695 papers · 148 categories

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114228341455 · Jun 202019922001200920172026
48 results for gradient outer product

Paper improves SDR estimation speed and conditions.

problem Improving sufficient dimension reduction for multi-index models.
method Estimating expected smoothed gradient outer product.
result Achieves fast parametric convergence rate of Cdn1/2C_d \cdot n^{-1/2}.

In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering. The idea is to use an outer product to explicitly model the pairwise correlations between the dimensions of the embedding space. In contrast to existing neural recommender models that combine user em…

2018-08-12abs ↗pdf ↗

Recursive Feature Machines show grokking in modular arithmetic without neural networks.

problem Grokking in modular arithmetic tasks.
method Recursive Feature Machines (RFM) with Average Gradient Outer Product (AGOP).
result RFM and neural networks learn block-circulant features to solve modular arithmetic.

We show that the horoboundary of outer space for the Lipschitz metric is a quotient of Culler and Morgan's classical boundary, two trees being identified whenever their translation length functions are homothetic in restriction to the set of primitive elements of FNF_N. We identify the set of Busemann points with the s…

2014-07-14abs ↗pdf ↗

SMAVE optimizes SDR by projecting onto a low-dimensional subspace on a Riemannian manifold.

problem High-dimensional regression challenges due to the curse of dimensionality.
method SMAVE combines nearest-neighbor localization and Riemannian stochastic gradient ascent.
result SMAVE achieves almost-sure convergence and matches RMAVE's synthetic subspace recovery rate.

Outer automorphism group of hyperbolic groups is HHG under certain conditions.

problem Characterizing the outer automorphism group of hyperbolic groups.
method Proving finite-index subgroups are central extensions of orbifold mapping class groups with bounded Euler class.
result Outer automorphism group of a one-ended hyperbolic group is virtually a hierarchically hyperbolic group.

New insights into optimizing Local SGD's outer optimizer for faster convergence.

problem Understanding the impact of outer optimizer and its hyperparameters in Local SGD.
method Analyzing convergence guarantees with new outer learning rates and momentum.
result Tuning the outer learning rate can improve convergence and handle inner learning rate ill-tuning.

In this paper we develop the metric theory for the outer space of a free product of groups. This generalizes the theory of the outer space of a free group, and includes its relative versions. The outer space of a free product is made of GG-trees with possibly non-trivial vertex stabilisers. The strategies are the same…

2013-12-15abs ↗pdf ↗

Improves MARS for nonparametric multivariate regression with dimension reduction.

problem High number of basis functions in MARS for high-order interactions.
method Linear combinations of covariates for dimension reduction, facilitating gradient calculation and eigen-analysis for estimation.
result Asymptotic theory and numerical studies show improved performance over MARS.

The paper shows that for Coxeter groups, the commensurator of outer automorphisms is rigid.

problem The rigidity of commensurator of outer automorphisms of Coxeter groups.
method Study of the abstract commensurator of the outer automorphism group of a universal Coxeter group.
result For n5n \geq 5, the natural map is an isomorphism and every isomorphism between finite index subgroups is conjugation.

In this paper, we show that feedforward and recurrent neural networks exhibit an outer product derivative structure but that convolutional neural networks do not. This structure makes it possible to use higher-order information without needing approximations or infeasibly large amounts of memory, and it may also provid…

2018-10-09abs ↗pdf ↗

The study examines how automorphism growth rates of a group can be deduced from its simpler decompositions.

problem Determine automorphism growth rates of a group from its simpler decompositions.
method Analyze group decompositions into simpler pieces (direct products, free products, graph of groups) and deduce growth rates.
result Information about automorphism growth rates of a group can be deduced from its simpler decompositions.

TrIM improves gradient-based dimension reduction and regression.

problem Efficiently identifying relevant feature subspace for high-dimensional regression.
method Introduced TrIM forest, an iterative approach using Mondrian forest and EGOP estimate.
result Consistency guarantees and convergence rates for EGOP matrix and random forest estimator.

AGOP from KRR recovers central subspace in fewer samples than needed for prediction.

problem Recovering low-dimensional structure in multi-index polynomial functions.
method Fit kernel ridge regression and compute AGOP from the fitted predictor.
result AGOP's top rr eigenspace recovers the central subspace in ndp+δn \asymp d^{p+δ} samples.

We generalise the Karrass-Pietrowski-Solitar and the Nielsen realisation theorems from the setting of free groups to that of free products. As a result, we obtain a fixed point theorem for finite groups of outer automorphisms acting on the relative free splitting complex of Handel--Mosher and on the outer space of a fr…

2016-01-10abs ↗pdf ↗

Let G=G1GkFG=G_1\ast\dots\ast G_k\ast F be a countable group which splits as a free product, where all groups GiG_i are freely indecomposable and not isomorphic to Z\mathbb{Z}, and FF is a finitely generated free group. If for all i{1,,k}i\in\{1,\dots,k\}, both GiG_i and its outer automorphism group Out(Gi)\text{Out}(G_i) satisfy t…

2014-08-03abs ↗pdf ↗

The paper explores how gradient descent trains associative memories, revealing oscillations and convergence issues.

problem Training dynamics of associative memories in overparameterized and underparameterized settings.
method Reduction to particle system dynamics, theory, and experiments.
result Oscillatory transitory regimes and benign loss spikes in overparameterized settings, suboptimal memorization in underparameterized settings.

We associate a contractible ``outer space'' to any free product of groups G=G_1*...*G_q. It equals Culler-Vogtmann space when G is free, McCullough-Miller space when no G_i is Z. Our proof of contractibility (given when G is not free) is based on Skora's idea of deforming morphisms between trees. Using the action of Ou…

2005-01-19abs ↗pdf ↗

In this paper we study obstructions to presentability by products for finitely generated groups. Along the way we develop both the concept of acentral subgroups, and the relations between presentability by products on the one hand, and certain geometric and measure or orbit equivalence invariants of groups on the other…

2010-03-19abs ↗pdf ↗

Study uses outer metrics for PDE-constrained shape optimization over diffeomorphism group.

problem Optimizing shapes governed by PDEs over the diffeomorphism group.
method Outer metrics on diffeomorphism group, Riemannian steepest descent method.
result Riemannian approach outperforms other metrics in solving PDE-constrained shape optimization problems.

We study aspherical manifolds that do not support Anosov diffeomorphisms. Weakening conditions of Gogolev and Lafont, we show that the product of an infranilmanifold with finitely many aspherical manifolds whose fundamental groups have trivial center and finite outer automorphism group does not support Anosov diffeomor…

2018-06-09abs ↗pdf ↗

Outer billiards defined on geodesics surfaces in 3D space forms.

problem Defining and analyzing outer billiards on geodesics in 3D space forms.
method Defined an outer billiard map on the space of oriented geodesics, showing diffeomorphism and symplectomorphism properties.
result Outer billiard map is a diffeomorphism and symplectomorphism under certain conditions.

Let GG be a countable group that splits as a free product of groups of the form G=G1GkFNG=G_1\ast\dots\ast G_k\ast F_N, where FNF_N is a finitely generated free group. We identify the closure of the outer space PO(G,{G1,,Gk})P\mathcal{O}(G,\{G_1,\dots,G_k\}) for the axes topology with the space of projective minimal, \emph{very small} …

2014-08-03abs ↗pdf ↗

We consider a class of a nested optimization problems involving inner and outer objectives. We observe that by taking into explicit account the optimization dynamics for the inner objective it is possible to derive a general framework that unifies gradient-based hyperparameter optimization and meta-learning (or learnin…

2017-12-18abs ↗pdf ↗

The paper explains spikes in training loss as catapults, improving feature learning and generalization.

problem Understanding and improving the training process of neural networks.
method Analysis of training loss spikes in SGD, empirical evidence of catapults, and demonstration of AGOP alignment.
result Catapults in training loss promote feature learning and better generalization.

xRFM improves tabular data inference with better accuracy and scalability.

problem Inference from tabular data remains challenging and underdeveloped compared to other AI areas.
method Combines feature learning kernel machines with a tree structure.
result xRFM outperforms other methods across 100 regression and 200 classification datasets.

In this paper, we consider the convex and non-convex composition problem with the structure 1ni=1nFi(G(x))\frac{1}{n}\sum\nolimits_{i = 1}^n {{F_i}( {G( x )} )}, where G(x)=1nj=1nGj(x)G( x )=\frac{1}{n}\sum\nolimits_{j = 1}^n {{G_j}( x )} is the inner function, and Fi()F_i(\cdot) is the outer function. We explore the variance reduction based met…

2018-09-06abs ↗pdf ↗

New approach improves model generalization through distributionally robust learning.

problem Improving model generalization in machine learning.
method Stochastic gradient descent applied to the outer minimization problem, with gradient estimation through multi-level Monte Carlo randomization.
result Our approach yields significant benefits over previous work in numerical experiments.

In a recent work, Galloway [9] proved a local foliation theorem by MOTSs for a 3-dimensional initial data set (M,g,K)(M,g,K) with mean curvature τ0τ\le0 in a 4-dimensional spacetime (M,g)(\overline M,\overline g) when (under suitable assumptions) MM has a stable spherical MOTS ΣΣ which achieves an upper bound for the area. H…

2015-04-25abs ↗pdf ↗

We prove that for k5k\ge 5 there does not exist a continuous map CV(Fk)PCurr(Fk)\partial CV(F_k)\to\mathbb PCurr(F_k) that is either Out(Fk)Out(F_k)-equivariant or Out(Fk)Out(F_k)-anti-equivariant. Here CV(Fk)\partial CV(F_k) is the "length-function" boundary of Culler-Vogtmann's Outer space CV(Fk)CV(F_k), and PCurr(Fk)\mathbb PCurr(F_k) is the space of pr…

2006-05-19abs ↗pdf ↗

Develops first and second-order pseudo-mirror descent methods for nonnegative function estimation.

problem Nonnegative function estimation in settings like MLE and trajectory optimization.
method First and second-order pseudo-mirror descent with pseudo-gradients and projections.
result Establishes tradeoffs and non-asymptotic bounds on model complexity.

The thesis shows how automorphisms of hyperbolic groups can be represented by train track maps.

problem Representing automorphisms of hyperbolic groups using train track maps.
method Using graphs of groups and Bestvina-Handel's irreducible train track maps, the thesis constructs relative train track maps.
result Outer automorphisms of finitely-generated word hyperbolic groups satisfy a dynamical trichotomy.