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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.

169,181 papers · 148 categories

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471114 · Jun 202019922001200920182026
48 results for pushing

Push-SAGA is a decentralized algorithm for directed graphs that converges linearly.

problem Finite-sum minimization over directed graphs with stochastic gradients.
method Combines variance reduction, gradient tracking, and consensus algorithms.
result Achieves linear convergence for smooth and strongly convex problems.

Robotic manipulation learns synergies between pushing and grasping from scratch.

problem Discovering complex synergies between pushing and grasping for efficient robotic manipulation.
method Self-supervised deep reinforcement learning with two convolutional networks.
result System learns pushing and grasping motions that improve picking success rates and efficiency.

This paper explores the nonconvexity of push-forward constraints in machine learning.

problem The nonconvexity of push-forward constraints in machine learning.
method The paper provides sufficient and necessary conditions for the (non)convexity of push-forward functions and maps.
result Push-forward constraints are generally nonconvex, which limits the design of convex optimization problems in machine learning.

Study finds anomalies in high-frequency S&P 500 price changes.

problem Anomalies in high-frequency S&P 500 price changes.
method Using NBBO event-time data, the study forms pairs of backward and forward price increments, standardizes them, and estimates expected responses on a fine grid of push magnitudes.
result Persistent structural shift in expected responses: near zero for short lags, pronounced tails for long lags, indicating correlation between larger historical pushes and nonzero responses.

We establish the Thom isomorphism in twisted K-theory for any real vector bundle and develop the push-forward map in twisted K-theory for any differentiable proper map f:XYf: X\to Y (not necessarily K-oriented). The push-forward map generalizes the push-forward map in ordinary K-theory for any KK-oriented differentiable…

2005-07-21abs ↗pdf ↗

This paper presents a data-driven approach to model planar pushing interaction to predict both the most likely outcome of a push and its expected variability. The learned models rely on a variation of Gaussian processes with input-dependent noise called Variational Heteroscedastic Gaussian processes (VHGP) that capture…

2017-04-10abs ↗pdf ↗

Let f:MmRm+kf:M^m\longrightarrow \Bbb R^{m+k} be an immersion where MM is a smooth connected mm-dimensional manifold without boundary. Then we construct a subspace Ω(f)Ω(f) of Rk \mathbb{R}^k, namely push-out space. which corresponds to a set of embedded manifolds which are either parallel to f f , tubes around f f or, in…

2013-04-17abs ↗pdf ↗

Unified framework for stability and generalization of Push-Sum in decentralized learning over directed graphs.

problem Understanding stability and generalization of Push-Sum in decentralized learning over directed networks.
method Developed a unified uniform-stability framework for SGP algorithm, incorporating imbalance-aware consistency bounds.
result Established finite-iteration stability and optimization guarantees for convex and non-convex objectives.

Push-forward models struggle to fit multimodal distributions due to high Lipschitz constants.

problem Expressivity of push-forward generative models in fitting multimodal distributions.
method Analyzing the Lipschitz constant and its relation to the total variation distance and Kullback-Leibler divergence.
result Push-forward models require high Lipschitz constants to approximate multimodal distributions, leading to a trade-off between expressivity and stability.

In this note, we reconcile two approaches that have been used to construct stringy multiplications. The pushing forward after pulling back that has been used to give a global stringy extension of the functors K_0,K^{top},A^*,H^* [CR, FG, AGV, JKK2], and the pulling back after having pushed forward, which we have previo…

2007-03-07abs ↗pdf ↗

Researchers examine various causal structures for spacetimes with continuous metrics.

problem Comparing causal structures for spacetimes with continuous but not necessarily smooth metrics.
method Examined three key properties: push-up lemma, openness of chronological futures, and existence of limit causal curves.
result Spacetimes with continuous metrics do not always satisfy all three key properties.

The Singular Asymptotics Lemma by Brüning and Seeley and the Push-Forward Theorem by Melrose lie at the very heart of their respective approaches to singular analysis. We review both and show that they deal with the same basic problem, giving solutions that emphasize different aspects of it. This also points to a possi…

2000-09-15abs ↗pdf ↗

KINet learns object interactions without supervision for robotic pushing.

problem Lack of supervised data for object-centric forward prediction.
method End-to-end unsupervised framework using keypoint representation and contrastive estimation.
result Automatically generalizes to unseen scenarios and accurately predicts future states.

In this paper, we address the problem of embedded feature selection for ranking on top of the list problems. We pose this problem as a regularized empirical risk minimization with pp-norm push loss function (p=p=\infty) and sparsity inducing regularizers. We leverage the issues related to this challenging optimization…

2012-06-27abs ↗pdf ↗

This research examines the geometry of latent spaces in push-forward generative models.

problem Tendency of deep generative models to output samples outside target distribution support.
method Geometric measure theory and truncation method to enforce simplicial cluster structure.
result Proves sufficient condition for optimality in latent space geometry.

The study constructs a Lorentzian length space and explores its properties and relationships with metric and causal geometry.

problem Understanding the relationship between metric and causal geometry in Lorentzian spaces.
method Constructing a Lorentzian length space with an orthogonal splitting on a product of an interval and a metric space, and using synthetic time-like Ricci curvature bounds.
result Established sufficient conditions for global hyperbolicity and formulated time-like Ricci curvature bounds without push-up and regularity assumptions.

Let f: P-->W be an embedding of a compact polyhedron in a closed oriented manifold W, let T be a regular neighborhood of P in W and let C:=closure(W-T) be its complement. Then W is the homotopy push-out of a diagram C<--dT-->P. This homotopy push-out square is an example of what is called a Poincare embedding. We study…

2005-03-25abs ↗pdf ↗

Study embeddings between Barron spaces with various activation functions, focusing on RePU.

problem Understanding the influence of activation functions on infinitely wide neural networks.
method Prove embeddings by constructing push-forward maps on measures representing functions.
result Barron spaces with RePU activation have a hierarchical structure similar to Sobolev spaces.

In this article we give necessary and sufficient conditions for two triples of integers to be realized as the Thurston-Bennequin number and the rotation number of a Legendrian theta-graph with all cycles unknotted. We show that these invariants are not enough to determine the Legendrian class of a topologically planar …

2013-03-08abs ↗pdf ↗

Given a contact manifold $M_#$ together with a transversal infinitesimal automorphism ξξ, we show that any local leaf space MM for the foliation determined by ξξ naturally carries a conformally symplectic (cs-) structure. Then we show that the Rumin complex on $M_#$ descends to a complex of differential operators on…

2013-12-10abs ↗pdf ↗

The paper proves a Hard Lefschetz Theorem for push-forwards of polarized twistor modules.

problem Proving a Hard Lefschetz Theorem for push-forwards of polarized twistor modules.
method Using Kashiwara and Kawai's theorem on Hodge structures and regular polarized twistor modules.
result Proves the Hard Lefschetz Theorem for push-forwards of polarized twistor modules.

Paper tackles hyper-gradient estimation in decentralized FL over time-varying networks.

problem Excessive communication costs and inability to use robust networks.
method Introduces an optimality condition and uses Push-Sum for averaging model parameters and gradients over time-varying directed networks.
result Derives a hyper-gradient estimator that operates over time-varying directed networks and converges to the true hyper-gradient.

Paper converts deep networks to flat, equivalent kernel machines.

problem Capacity control and uniform convergence in deep learning.
method Push-forward transformation from deep networks to indefinite kernel machines.
result Flat network weights are Lp-norm regularized (0<p<1).

A new algorithm for optimizing probability distributions converges linearly.

problem Optimizing functionals over families of probability distributions.
method Variational transport: particle-based algorithm approximating Wasserstein gradient descent.
result Variational transport converges linearly to the global minimum of the objective functional.

For any knot T transverse to a given contact structure on a 3-manifold, we exhibit a Legendrian two-component link such that T equals the transverse push-off of one of the link components and contact (+1)-surgery on the link has the same effect as a Lutz twist along T.

2004-01-24abs ↗pdf ↗