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.
MiLeNAS improves neural architecture search by reducing approximation errors and achieving better accuracy.
Stochastic gradient methods are the workhorse (algorithms) of large-scale optimization problems in machine learning, signal processing, and other computational sciences and engineering. This paper studies Markov chain gradient descent, a variant of stochastic gradient descent where the random samples are taken on the t…
We develop a family of reformulations of an arbitrary consistent linear system into a stochastic problem. The reformulations are governed by two user-defined parameters: a positive definite matrix defining a norm, and an arbitrary discrete or continuous distribution over random matrices. Our reformulation has several e…
Reformulated Markov's conjecture in combinatorial terms.
After reconsidering the Dasbach-Hougardy counterexample to the Kauffman Conjecture on alternating knots, we reformulate the conjecture and consider Dasbach-Hougardy counterexample and similar counterexamples in the light of the reformulated conjecture.
We reformulate LIPs as min-max problems for easier solution.
Virtual index cocycles reformulate virtual link invariants.
DNNs with regularization reveal feature learning dynamics and sparsity.
Reformulated sigma models for complex Grassmannians using Gross-Neveu formalism.
We reformulate data-dependent constraints to ensure they are always met with high probability.
We establish a characterization of adequate knots in terms of the degree of their colored Jones polynomial. We show that, assuming the Strong Slope conjecture, our characterization can be reformulated in terms of "Jones slopes" of knots and the essential surfaces that realize the slopes .For alternating knots the refor…
We propose a method to efficiently learn diverse strategies in reinforcement learning for query reformulation in the tasks of document retrieval and question answering. In the proposed framework an agent consists of multiple specialized sub-agents and a meta-agent that learns to aggregate the answers from sub-agents to…
A restricted Boltzmann machine (RBM) is a two-layer neural network with shared weights and has been extensively studied for dimensionality reduction, data representation and recommendation systems in the literature. The traditional RBM requires a probabilistic interpretation of the values on both layers and a Markov ch…
Paper reformulates UOT as non-negative penalized linear regression for efficient algorithms.
The paper reformulates an invariant and calculates it for lens spaces.
Quaternionic reformulation simplifies surface curvature theory.
Unsupervised machine learning helps design complex experiments more efficiently.
The paper explores handlebody versions of various diagram algebras.
Paper tackles SMPC for linear systems with unknown noise distribution.
Study reformulates Finsler metrizability problems using geodesic invariance.
Optimizes distributions robustly with Sinkhorn distance.
The topology of symplectic 4-manifolds is related to that of singular plane curves via the concept of branched covers. Thus, various classification problems concerning symplectic 4-manifolds can be reformulated as questions about singular plane curves. Moreover, using braid monodromy, these can in turn be reformulated …
We claim that the recently discovered universal-matrix precursor for the functions, which define the differential expansion of colored polynomials for twist and double braid knots, can be extended from rectangular to non-rectangular representations. This case is far more interesting, because it involves multiplicit…
The concept of refinement from probability elicitation is considered for proper scoring rules. Taking directions from the axioms of probability, refinement is further clarified using a Hilbert space interpretation and reformulated into the underlying data distribution setting where connections to maximal marginal diver…
The paper sets lower bounds for adversarial robustness in multiclass classification.
Unified framework for analyzing machine learning model attributions.
Optimizes AMM markets with a new framework reducing complex optimization to simpler root finding.
A new algorithm solves bilevel optimization with linear constraints.
Reformulates elasticity complex with new differential and Hodge star operators.
In order to cope with the increased data volumes generated by modern radio interferometers such as LOFAR (Low Frequency Array) or SKA (Square Kilometre Array), fast and efficient calibration algorithms are essential. Traditional radio interferometric calibration is performed using nonlinear optimization techniques such…
New approach to convex hulls for low-rank problems.
The problem of joint feature selection across a group of related tasks has applications in many areas including biomedical informatics and computer vision. We consider the l2,1-norm regularized regression model for joint feature selection from multiple tasks, which can be derived in the probabilistic framework by assum…
This note corrects one serious mistake and several smaller mistakes from arXiv:math/0502404. The main results of that paper are unchanged.
General Relativity can be reformulated as a diffeomorphism invariant SU(2) gauge theory. A new action principle for this "pure connection" formulation of GR is described.
New MIP formulations for neural network Lipschitz constant estimation.
We reformulate Heegaard Floer homology in terms of holomorphic curves in the cylindrical manifold Sigma x [0,1] x R, where Sigma is the Heegaard surface, instead of Sym^g(Sigma). We then show that the entire invariance proof can be carried out in our setting. In the process, we derive a new formula for the index of the…
MCD reformulates conditional density estimation into binary classification.
This paper reformulates the -adic Littlewood Conjecture using infinite loops.
In this article we dwell into the class of so called ill posed Linear Inverse Problems (LIP) in machine learning, which has become almost a classic in recent times. The fundamental task in an LIP is to recover the entire signal / data from its relatively few random linear measurements. Such problems arise in variety of…
Reformulates divergence map for Turaev cobracket in non-commutative geometry.
In this paper, we propose a scalable algorithm for spectral embedding. The latter is a standard tool for graph clustering. However, its computational bottleneck is the eigendecomposition of the graph Laplacian matrix, which prevents its application to large-scale graphs. Our contribution consists of reformulating spect…
A global weak solution of the biharmonic wave map equation in the energy space for spherical targets is constructed. The equation is reformulated as a conservation law and solved by a suitable Ginzburg-Landau type approximation.
Paper tackles imbalanced binary classification by optimizing precision and recall directly.
Proposes a Taylor framework to unify and analyze attribution methods.
Feature attribution methods, or saliency maps, are one of the most popular approaches for explaining the decisions of complex machine learning models such as deep neural networks. In this study, we propose a stochastic optimization approach for the perturbation-based feature attribution method. While the original optim…
New method optimizes PCA for better prediction and variance.
Using basic topology and linear algebra, we define a plethora of invariants of boundary links whose values are power series with noncommuting variables. These turn out to be useful and elementary reformulations of an invariant originally defined by M. Farber.
Study on policy testing in MDPs with lower bounds and new algorithm.