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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.

169,051 papers · 148 categories

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48 results for step and rank three

Complete classification of sub-Riemannian spaces with step and rank three.

problem Classifying sub-Riemannian model spaces with specific step and rank.
method Complete classification based on nilpotentization and algebra realization.
result No nontrivial sub-Riemannian model spaces of step and rank three with free nilpotentization.

Proposes new listwise learning-to-rank models to address rating ties and document relevance.

problem Rating ties and document relevance in existing listwise learning-to-rank models.
method Models ranking as selecting documents from a candidate set based on unique rating levels. Uses a new loss function and adapted RNN model for refining prediction scores.
result Models notably outperform state-of-the-art learning-to-rank models on four public datasets.

The Frank-Wolfe (FW) algorithm has been widely used in solving nuclear norm constrained problems, since it does not require projections. However, FW often yields high rank intermediate iterates, which can be very expensive in time and space costs for large problems. To address this issue, we propose a rank-drop method …

2017-04-13abs ↗pdf ↗

We prove two rigidity results for complete Riemannian three-manifolds of higher rank. Complete three-manifolds have higher spherical rank if an only if they are spherical space forms. Complete finite volume three-manifolds have higher hyperbolic rank if and only if they are hyperbolic space forms.

2016-08-16abs ↗pdf ↗

Memory-efficient optimizers fail to track a subspace, leading to unpredictable model performance.

problem Memory-efficient optimizers fail to track a subspace, leading to unpredictable model performance.
method Analyzing the behavior of memory-efficient optimizers like GaLore, which project gradients onto a rank-r subspace recomputed every T steps.
result Memory-efficient optimizers fail to track a subspace, leading to unpredictable model performance.

GrowNet uses shallow neural networks for gradient boosting, outperforming existing methods.

problem Improving gradient boosting performance through shallow neural networks.
method Unified gradient boosting framework with shallow neural networks as weak learners, incorporating corrective steps.
result GrowNet outperformed state-of-the-art boosting methods in classification, regression, and learning to rank tasks.

Researchers describe Casimir functions for 3- and 4-step nilpotent Lie groups.

problem Understanding Casimir functions for free nilpotent Lie groups of steps 3 and 4.
method Construction of Casimir functions for free nilpotent Lie groups of steps 3 and 4.
result For 3-step groups, coadjoint orbits are fully described as affine subspaces or direct products of quadrics.

We propose to solve a label ranking problem as a structured output regression task. We adopt a least square surrogate loss approach that solves a supervised learning problem in two steps: the regression step in a well-chosen feature space and the pre-image step. We use specific feature maps/embeddings for ranking data,…

2018-07-06abs ↗pdf ↗

New method estimates neuronal connectivity from partially observed data.

problem Estimating neuronal connectivity from partially observed data.
method Two-step approach: low-rank covariance completion followed by graph structure estimation.
result Graph selection consistency demonstrated for one approach.

DoRA improves adaptation efficiency for large models by factoring norms and fusing kernels.

problem High-rank DoRA is computationally expensive and infeasible on common GPUs.
method Factored norms and fused Triton kernels to reduce memory and speed up computation.
result Fused implementation is up to 2.0x faster for inference and 1.9x faster for gradient computation.

iSplit LBI predicts individualized partial rankings from ties, outperforming state-of-the-art methods.

problem Predicting partial rankings from pairwise comparisons with ties, considering individual preferences.
method Variable splitting-based algorithm (iSplit LBI) that generates a sequence of estimations with a regularization path, decomposing parameters into abnormal signals, personalized signals, and random noise.
result iSplit LBI significantly outperforms state-of-the-art alternatives in predicting individualized partial rankings.

We study the problem of low-rank tensor factorization in the presence of missing data. We ask the following question: how many sampled entries do we need, to efficiently and exactly reconstruct a tensor with a low-rank orthogonal decomposition? We propose a novel alternating minimization based method which iteratively …

2014-06-11abs ↗pdf ↗

We complete the classification of rank two affine manifolds in the moduli space of translation surfaces in genus three. Combined with a recent result of Mirzakhani and Wright, this completes the classification of higher rank affine manifolds in genus three.

2016-12-21abs ↗pdf ↗

Computes the decomposition of rank-three bundles over the projective line with three marked points.

problem Decomposing rank-three bundles over the projective line with three marked points.
method Using the monodromy derivative to compute the roots of the bundles.
result Computes the exact decomposition of rank-three bundles for m=3m = 3.

A simple framework uses daily prices and volumes to beat the market.

problem Optimizing portfolio performance using only observable data.
method Three matrices derived from price history: return correlations, monthly ranking Markov chains.
result Market-beating portfolio with high Sharpe ratios.

The theoretical existence of non-classical Schottky groups is due to Marden. Explicit examples of such kind of groups are only known in rank two, the first one by by Yamamoto in 1991 and later by Williams in 2009. In 2006, Maskit and the author provided a theoretical method to obtain examples of non-classical Schottky …

2017-12-15abs ↗pdf ↗

A new multi-label classification model combining SVM and BR with low-rank learning.

problem Class imbalance and label correlation issues in multi-label classification.
method Joint Ranking SVM and Binary Relevance with robust Low-rank learning (RBRL).
result RBRL outperforms state-of-the-art methods in multi-label classification.

SVD training reduces DNN rank and computation load without SVD per step.

problem High memory and computational load in deep neural networks.
method Explicitly achieves low-rank DNNs during training without SVD per step, using orthogonality regularization and sparsity-inducing regularizers.
result Significantly reduces DNN rank and computation load compared to existing methods.

The paper studies automorphisms of 2-step nilpotent Lie groups, showing continuity up to center and field automorphisms.

problem Investigating the continuity of abstract automorphisms in 2-step nilpotent Lie groups.
method Analyzes various types of 2-step nilpotent Lie groups, using tools from Riemannian geometry.
result Abstract automorphisms are continuous 'up to discontinuity due to the center and field automorphisms of C\mathbb{C}' for many 2-step nilpotent Lie groups.

By proving precisely which singularity index lists arise from the pair of invariant foliations for a pseudo-Anosov surface homeomorphism, Masur and Smillie determined a Teichmüller flow invariant stratification of the space of quadratic differentials. In this final paper of a three-paper series, we give a first step to…

2013-01-29abs ↗pdf ↗

This paper improves entropy bounds for ranking time-series complexity.

problem Ranking the complexity of time series processes.
method Building on information theoretic bounds, the paper improves the upper bound of conditional differential entropy using Hadamard's inequality and covariance matrix properties.
result The improved bounds can be used to rank the complexity of time series processes.

MuonEq improves training of matrix-valued parameters by rebalancing momentum before orthogonalization.

problem Training matrix-valued parameters with orthogonalized-update optimizers like Muon.
method MuonEq introduces three lightweight pre-orthogonalization equilibration schemes: two-sided row/column normalization (RC), row normalization (R), and column normalization (C).
result Row/column normalization acts as a zeroth-order surrogate for whitening and improves the geometry seen by orthogonalization.

This paper tackles fitting multilevel low rank matrices by addressing three problems.

problem Fitting a given matrix by an MLR matrix in the Frobenius norm.
method Factor fitting, rank allocation, and hierarchical partitioning.
result The proposed methods can fit a given matrix by an MLR matrix in the Frobenius norm.

We propose a simple and efficient method for ranking features in multi-label classification. The method produces a ranking of features showing their relevance in predicting labels, which in turn allows to choose a final subset of features. The procedure is based on Markov Networks and allows to model the dependencies b…

2016-02-24abs ↗pdf ↗

We prove the smoothness of abnormal minimizers of subriemannian manifolds of step 3 with a nilpotent basis. We prove that rank 2 Carnot groups of step 4 admit no strictly abnormal minimizers. For any subriemannian manifolds of step less than 7, we show all abnormal minimizers have no corner type singularities, which pa…

2012-02-20abs ↗pdf ↗

It is known that if a classical link group is a free abelian group, then its rank is at most two. It is also known that a kk-component 2-link group (k>1k>1) is not free abelian. In this paper, we give examples of T2T^2-links each of whose link groups is a free abelian group of rank three or four. Concerning the T2T^2-l…

2009-11-22abs ↗pdf ↗

Data compression speeds up machine learning loss calculations.

problem Computational demand in calculating mean squared error for large datasets.
method Use rank-1 lattices to compress data, assigning weights based on original data and responses.
result Our QMC data compression algorithms can lead to arbitrary high convergence rates for smooth functions.

Study degenerate Bianchi transformations for pseudo-spherical submanifolds in 5D space.

problem Characterize three-dimensional pseudo-spherical submanifolds with degenerate Bianchi transformations.
method Complete description through holonomically degenerate Bianchi transformations.
result Obtained a complete description of degenerate pseudo-spherical submanifolds.