arXiv research

A new method LIF for equality constraints in GCNN models is proposed.

Detects financial fraud schemes in networks using graph structure learning.

Bayesian model improves data-efficiency in reinforcement learning.

Federated LIDAR aided beam selection reduces mmWave beam search overhead.

A new method for filling in missing traffic data improves accuracy over existing techniques.

Many clustering schemes are defined by optimizing an objective function defined on the partitions of the underlying set of a finite metric space. In this paper, we construct a framework for studying what happens when we instead impose various structural conditions on the clustering schemes, under the general heading of…

Continues work on derived manifolds and symplectic schemes, constructing virtual classes.

Conformal Autoencoders infer intrinsic dimensionality and impose invariance.

LD-SGD improves communication in decentralized SGD.

A new algorithm for decentralized learning in heterogeneous networks reduces sub-optimality over time.

The study tightens risk bounds for mixtures of experts using local differential privacy.

Gauge symmetries explain the emergence of Merton-Garman equation from Black-Scholes in finance.

DM2L tackles missing labels in multi-label learning by modeling local and global rank structures.

Characterizes discrete nets with characteristic properties on larger parameter rectangles.

Combines local and global smoothing for multivariate density estimation.

Novel algorithm reduces delays and communication in decentralized learning.

New methods show robustness and accuracy can coexist.

The paper develops no arbitrage results for trajectory based models by imposing general constraints on the trading portfolios. The main condition imposed, in order to avoid arbitrage opportunities, is a local continuity requirement on the final portfolio value considered as a functional on the trajectory space. The pap…

We consider the problem of integration of L_\infty-algebroids (differential graded manifolds) to L_\infty-groupoids. We first construct a "big" Kan simplicial manifold (Fréchet or Banach) whose points are solutions of a (generalized) Maurer-Cartan equation. The main analytic trick in our work is an integral transformat…

Paper analyzes Langevin dynamics for multimodal Gaussian mixtures, controlling errors across dimensions.

We analyze a monetary system of random money transfer on the basis of double entry bookkeeping. Without boundary conditions, we do not reach a price equilibrium and violate text-book formulas of economists quantity theory (MV=PQ). To match the resulting quantity of money with the model assumption of a constant price, w…

Flexible device participation improves federated learning convergence.

Explains gradient descent methods and their convergence, focusing on simple analysis.

Generalizes soft noncommutative schemes to flag varieties.

New iterative schemes solve Yamabe-type equations on closed manifolds.

oPoW proposes a new PoW algorithm to reduce mining costs and environmental impact.

New neural network theory mimics physics laws, making computations more plausible.

For the first time in mathematical finance field, we propose the local weak form meshless methods for option pricing; especially in this paper we select and analysis two schemes of them named local boundary integral equation method (LBIE) based on moving least squares approximation (MLS) and local radial point interpol…

New schemes improve error estimates for sampling from non-log-concave distributions.

NeLLoC improves image compression with parallel decoding.

We provide a scheme for inferring causal relations from uncontrolled statistical data based on tools from computational algebraic geometry, in particular, the computation of Groebner bases. We focus on causal structures containing just two observed variables, each of which is binary. We consider the consequences of imp…

We study partial hedging for game options in markets with transaction costs bounded from below. More precisely, we assume that the investor's transaction costs for each trade are the maximum between proportional transaction costs and a fixed transaction costs. We prove that in the continuous time Black--Scholes (BS) mo…

Banks in the interbank network can not assess the true risks associated with lending to other banks in the network, unless they have full information on the riskiness of all the other banks. These risks can be estimated by using network metrics (for example DebtRank) of the interbank liability network which is availabl…

The MBO scheme for data clustering is analyzed in the large data limit, proving convergence to optimal partition problems.

Unified Newton-type methods for convex optimization using generalized self-concordant functions.

New method upsamples sparse, non-uniform point clouds more accurately.

Constructs k-regular maps using algebraic geometry.

New PDMP samplers improve BNN inference with accelerated computation.

Develops LSH schemes for f-divergences and mutual information loss.

Paper tackles estimating initial conditions of spatio-temporal processes from sparse data.

Paper addresses FL over MAC with DP constraints, proposing a novel consensus scheme.

We obtain explicit representations of locally risk-minimizing strategies of call and put options for the Barndorff-Nielsen and Shephard models, which are Ornstein--Uhlenbeck-type stochastic volatility models. Using Malliavin calculus for Levy processes, Arai and Suzuki (2015) obtained a formula for locally risk-minimiz…

New method reduces deep learning complexity on IoT devices.

Develops a method for reverse stress testing in multivariate scenarios.

Infinite Lie groups meet continuity conditions.

Derives local energy equation and proves consistency of staggered finite volume schemes for Euler equations.

New method reduces mobile device energy for image classification.

cKAM improves adaptive sampling by incorporating a cyclical stepsize scheme.