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.
The paper improves Gaussian processes by adding sum constraints, enhancing prediction accuracy.
Log-concavity proven for multinomial likelihoods under specific constraints.
The paper explores how to learn models that respect constraints in probabilistic learning.
Proves strict inequality for minimizers of Willmore energy under isoperimetric constraints.
Paper tackles constrained bandit problems with a new learning framework.
The paper introduces MU for NMF with -divergences and disjoint constraints.
Paper introduces a new kernel model for PSD-valued functions with theoretical guarantees and applications.
Developed Gompf connected sum for orbifolds, constructing symplectic and K-contact manifolds.
Paper studies constrained control games with a novel approximation method.
In this paper, we study the sum rate maximization for successive zero-forcing dirty-paper coding (SZFDPC) with per-antenna power constraint (PAPC). Although SZFDPC is a low-complexity alternative to the optimal dirty paper coding (DPC), efficient algorithms to compute its sum rate are still open problems especially und…
We report on results concerning a partially aggregated Stock Flow Consistent (SFC) macroeconomic model in the stationary state where the sectors of banks and firms are aggregated, the sector of households is dis-aggregated, and the probability density function (pdf) of the wealth of households is exogenous, constrained…
We propose a mixed integer programming (MIP) model and iterative algorithms based on topological orders to solve optimization problems with acyclic constraints on a directed graph. The proposed MIP model has a significantly lower number of constraints compared to popular MIP models based on cycle elimination constraint…
Bayesian approach speeds up SPN learning and inference.
Biclustering techniques have been widely used to identify homogeneous subgroups within large data matrices, such as subsets of genes similarly expressed across subsets of patients. Mining a max-sum sub-matrix is a related but distinct problem for which one looks for a (non-necessarily contiguous) rectangular sub-matrix…
Paper solves complex game theory problems with new equations.
Belief Propagation algorithms are instruments used broadly to solve graphical model optimization and statistical inference problems. In the general case of a loopy Graphical Model, Belief Propagation is a heuristic which is quite successful in practice, even though its empirical success, typically, lacks theoretical gu…
New single-loop algorithm tackles weakly convex constraints in stochastic optimization.
This letter tackles channel assignment in uplink wireless communication systems.
SPPL simplifies probabilistic programming for exact inference.
Differentially private algorithms for submodular maximization under various constraints.
Paper tackles online DR-submodular maximization with stochastic constraints.
New DAG constraints improve differentiable DAG learning.
This paper considers distributed online optimization with time-varying coupled inequality constraints. The global objective function is composed of local convex cost and regularization functions and the coupled constraint function is the sum of local convex functions. A distributed online primal-dual dynamic mirror des…
We show that many machine learning goals, such as improved fairness metrics, can be expressed as constraints on the model's predictions, which we call rate constraints. We study the problem of training non-convex models subject to these rate constraints (or any non-convex and non-differentiable constraints). In the non…
Proposes an angle-based framework for multicategory cost-sensitive classification.
Paper shows affine constraint is unnecessary for high-dimensional data.
We propose an iterative gradient-based algorithm to efficiently solve the portfolio selection problem with multiple spectral risk constraints. Since the conditional value at risk (CVaR) is a special case of the spectral risk measure, our algorithm solves portfolio selection problems with multiple CVaR constraints. In e…
We derive constraints on Lagrangian embeddings in completions of certain stable symplectic fillings with semisimple symplectic cohomologies. Manifolds with these properties can be constructed by generalizing the boundary connected sum operation to our setting, and are related to certain birational surgeries like blow-d…
New cosmological spacetimes without CMC Cauchy surfaces found.
FLANs process each feature separately for better interpretability.
We use rudiments of the Seiberg-Witten gluing theory for trivial circle bundles over a Riemann surface to relate de Seiberg-Witten basic classes of two -manifolds containing Riemann surfaces of the same genus and self-intersection zero with those of the -manifold resulting as a connected sum along the surface. We…
Algorithm minimizes loss and constraint violations in online convex optimization with smooth penalties.
This paper presents an improvement to model learning when using multi-class LogitBoost for classification. Motivated by the statistical view, LogitBoost can be seen as additive tree regression. Two important factors in this setting are: 1) coupled classifier output due to a sum-to-zero constraint, and 2) the dense Hess…
In recent years, constrained optimization has become increasingly relevant to the machine learning community, with applications including Neyman-Pearson classification, robust optimization, and fair machine learning. A natural approach to constrained optimization is to optimize the Lagrangian, but this is not guarantee…
We solve a conjecture of Morgan and Szabo (Embedded genus 2 surfaces in four-manifolds, Preprint) about the relationship of the basic classes of two four-manifolds of simple type with , , such that there are embedded Riemann surfaces of genus and self-intersection zero (and representing o…
Two new Frank-Wolfe algorithms improve convergence for constrained optimization.
DePAint solves MARL for agents with local constraints, privacy, and no central controller.
We first show that the connected sum along submanifolds introduced by the second author for compact initial data sets of the vacuum Einstein system can be adapted to the asymptotically Euclidean and to the asymptotically hyperbolic context. Then, we prove that in any case, and generically, the gluing procedure can be l…
We establish a general gluing theorem for constant mean curvature solutions of the vacuum Einstein constraint equations. This allows one to take connected sums of solutions or to glue a handle (wormhole) onto any given solution. Away from this handle region, the initial data sets we produce can be made as close as desi…
Improved regret bounds for Tsallis-INF in adversarial bandits and corruptions.
This study improves knowledge distillation for RNN-T models with noisy labels.
New algorithms minimize regret in streaming MAB with memory constraints.
We study Frank-Wolfe methods for nonconvex stochastic and finite-sum optimization problems. Frank-Wolfe methods (in the convex case) have gained tremendous recent interest in machine learning and optimization communities due to their projection-free property and their ability to exploit structured constraints. However,…
The topology of broken Lefschetz fibrations is studied by means of handle decompositions. We consider a slight generalization of round handles, and describe the handle diagrams for all that appear in dimension four. We establish simplified handlebody and monodromy representations for a certain subclass of broken Lefsch…
New policy minimizes error in finding best arm with privacy constraints.
Proposes a wave-constrained matrix factorization for signal learning.
Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in control theory, machine learning, and discrete geometry. This class of optimization problems, known as rank minimization, is NP-HARD, and for most practical problems there are no efficient algorithms that…
Improved MESMOC+ optimizes constrained multi-objective problems efficiently.