New tests compare unknown functions' distributions, improving on previous methods.
problem Comparing unknown but estimable functions' distributions.
method Developed a novel family of nonparametric omnibus tests based on U-statistics.
result The tests can determine if an unknown function is zero almost surely.
This paper precisely estimates transformer derivatives for explicit learning guarantees.
problem Computing fully-explicit generalization bounds for transformers with precise higher-order derivative estimates.
method Analyzes and estimates all higher-order derivatives of transformers with multiple attention heads and layer normalization.
result Obtains explicit pathwise generalization bounds for transformers learning from non-i.i.d. samples.
New method reduces errors in pricing and sensitivities for discontinuous payoffs.
problem Errors in pricing and sensitivities for discontinuous payoffs in digital and barrier options.
method Alternative methods for estimating sensitivities, including likelihood ratio and hybrid methods.
result New methods substantially reduce test errors in prices and sensitivities.
Efficient estimators for smooth Hilbert-valued parameters with theoretical guarantees.
problem Estimating smooth Hilbert-valued parameters with theoretical guarantees.
method Pathwise differentiable Hilbert-valued parameters, efficient influence functions, regularized one-step estimators.
result Theoretical guarantees for efficient estimators even when nuisance functions are arbitrary.
This paper simplifies hedge ratios in financial models using pathwise algorithmic differentiation.
problem Expensive and unstable computation of hedge ratios from pathwise sensitivities.
method Develops reduced stochastic hedge ratios of the form φ_j^r = Σ_j^r ξ_j^q X_q, retaining sensitivity tensor through empirical averages.
result Two coefficient criteria are introduced to minimize pathwise residuals and satisfy moment equations.
New Monte Carlo method for calculating sensitivities of barrier options.
problem Calculating sensitivities for discontinuous payoff functions in barrier options.
method Combining one-step survival idea with stable differentiation approach.
result Calculated sensitivities for different types of barrier options.
Quasi-Monte Carlo speeds up option Greeks calculation on GPUs.
problem Efficiently calculating option Greeks for risk management.
method Quasi-Monte Carlo (QMC) combined with GPU acceleration for pathwise sensitivity calculation.
result Increased computational speed and efficiency in estimating option Greeks.
We study the use of the multilevel Monte Carlo technique in the context of the calculation of Greeks. The pathwise sensitivity analysis differentiates the path evolution and reduces the payoff's smoothness. This leads to new challenges: the inapplicability of pathwise sensitivities to non-Lipschitz payoffs often makes …
Paper combines Vibrato and automatic differentiation for efficient financial option sensitivities.
problem Efficient computation of high-order derivatives for financial option sensitivities.
method Combines Vibrato and automatic differentiation methods.
result Combined method is faster and more stable than standard finite difference methods.
Develops strategies to minimize trading costs in volatile markets.
problem Minimizing trading costs in volatile markets with uncertain asset price paths.
method Constructs dynamic, pathwise optimal trade execution strategies using random Young differential equations.
result Good trade execution strategies minimize trading costs in a pathwise sense, not just expected costs.
A new method in finance without probabilities or integrals.
problem Creating a model-free approach to continuous-time finance.
method Pathwise approach using causal functional calculus and transition principle of Isaacs.
result A fully non-linear path-dependent equation characterizes optimal solutions.
The paper introduces a new concept of higher order approximate differentiability for sets.
problem Characterizing higher order rectifiable sets.
method Introducing the approximate differential of order k for subsets of Euclidean space.
result The approximate differential of order k is a Borel map whose domain is a Borel set.
Quantum machine learning solves high-dimensional PDEs with lower variance and improved accuracy.
problem Approximating solutions to high-dimensional parabolic PDEs.
method Pure Variational Quantum Circuit (VQC) for BSDE approximation, using temporal discretization and Monte Carlo simulation.
result VQC achieves lower variance and improved accuracy in most cases, particularly in highly nonlinear regimes.
Proposes a new method for estimating non-pathwise differentiable functional parameters.
problem Estimating dose-response curves for continuous exposure.
method Targeted Highly Adaptive Lasso (HAL) for non-pathwise differentiable functional parameters.
result The Targeted HAL-MLE achieves dimension-free rates up to log(n) factors and outperforms other methods in simulations.
Pathwise no-arbitrage proven for Delta hedging strategies in a specific setting.
problem Proving no-arbitrage opportunities in pathwise Delta hedging strategies.
method Existence of Delta hedging strategies via recursive schemes and functional Cauchy problems on path space.
result Nonexistence of pathwise arbitrage opportunities in specific classes of strategies.
The paper defines new ways to measure higher-order differentiability of sets.
problem Measuring differentiability of arbitrary sets in Euclidean space.
method Develops two concepts of pointwise differentiability using distance functions to smooth submanifolds.
result Strong pointwise differentiability of every positive integer order at almost all points of the set's intersection with a plane.
We use the Frölicher-Nijenhuis formalism to reformulate the inverse problem of the calculus of variations for a system of differential equations of order 2k in terms of a semi-basic 1-form of order k. Within this general context, we use the homogeneity proposed by Crampin and Saunders in [14] to formulate and discuss t…
Develops a new higher-order calculus using cubic algebra.
problem Foundational issues in differential calculus.
method Generalizes local linear algebra to higher order local linear algebra using cubic combinatorial objects.
result New conceptual cubic calculus theories provide insights into foundational issues.
Efficient inference for adaptive data with directional stability condition.
problem Efficient inference on scalar targets after adaptive data collection.
method Introduces directional stability, a weaker condition than i.i.d. data, and shows asymptotic normality and efficiency of estimators.
result Estimators remain asymptotically normal and semiparametrically efficient under directional stability.
This work proposes using zero-variance control variates to reduce variance in pathwise gradient estimators for variational inference.
problem Pathwise gradient estimators in variational inference have high variance, leading to inefficient optimization.
method Apply zero-variance control variates to pathwise gradient estimators.
result Zero-variance control variates can significantly reduce the variance of pathwise gradient estimators without requiring complex assumptions.
Pathwise uniqueness shown for specific stochastic equations.
problem Stochastic Volterra equations with singular kernels and Hölder coefficients.
method Established pathwise uniqueness through Hölder continuity of coefficients.
result Pathwise uniqueness and existence of unique strong solutions.
New principle for optimal control with higher order differential constraints.
problem Optimal control problems with higher order differential constraints.
method Derivation of the Principle of Minimal Labour and generalization of Pontryagin Maximum Principle.
result Generalized Pontryagin Maximum Principle for higher order constraints.
Noncommutative geometry connects higher order connections to quantization.
problem Quantization in noncommutative geometry.
method Introducing natural linear differential operators and Spencer operators.
result Higher order connections are equivalent to quantization.
Develops pathwise analysis for log-optimal portfolios using rough paths theory.
problem Analyzing stability and approximation of log-optimal portfolios.
method Pathwise approach based on càdlàg rough paths theory.
result Establishes pathwise stability and error estimates for log-optimal portfolios.
Second-order optimization speeds up deep hedging for complex options.
problem Hedging exotic options with market frictions in realistic markets.
method Second-order optimization scheme leveraging pathwise differentiability and Kronecker-factoring.
result Our method optimizes the policy in 1/4 the steps of standard optimization.
Develops portfolio theory without probabilistic analysis, focusing on pathwise decomposition.
problem Ensuring market viability without probabilistic assumptions.
method Uses pathwise decomposition and trend extractors to replace semimartingale decomposition.
result Growth-numéraire and viability equivalences are similar but not identical in pathwise setting.
Two new versions of a master formula in portfolio theory proven using pathwise Itô calculus.
problem Proving strictly pathwise versions of a master formula in portfolio theory.
method Pathwise Itô calculus, Föllmer's pathwise Itô calculus, Dupire's functional pathwise Itô calculus, Cont & Fournié's functional pathwise Itô calculus.
result Two new versions of the master formula in stochastic portfolio theory proven.
Paper finds unique eigenproperties of Euclidean operators.
problem Understanding eigenproperties of Euclidean operators.
method Identifies a family of differential operators and their eigenproperties.
result Eigenproperties are related to embedded minimal surfaces and Nitsche conjecture.
New measure captures differences across entire distributions of counterfactual outcomes.
problem Capturing differences across entire distributions of counterfactual outcomes.
method Entropic optimal transport measure, statistical functional, smooth transformation of embeddings.
result Established first-order and second-order pathwise differentiability.
Recalls and refines the concept of algebraically rectifiable curves.
problem Classical notion of algebraically rectifiable plane curves.
method Provides new criteria, relates to quadratic differentials, and generalizes to higher order differentials.
result Generalization and new criteria for algebraic rectifiability.
Paper defines a pathwise Ito integral without probabilistic assumptions.
problem Defines a non-probabilistic Ito integral for non-stochastic processes.
method Provides constructions for the pathwise Ito integral under various conditions.
result Existence of the integral for cadlag integrands and integrators with bounded jumps.
Study proves higher-order conformal forms don't exist in odd dimensions.
problem Proving non-existence of higher-order conformal forms in odd dimensions.
method Analyzing conformal hypersurface embeddings and differential order invariants.
result General non-existence of higher-order conformal forms in odd dimensions.
Efficient pathwise gradient estimators for multivariate distributions.
problem Constructing efficient gradient estimators for multivariate distributions.
method Using null solutions of the transport equation and control variates for gradient estimation.
result Pathwise gradient estimators for mixtures of multivariate Normal distributions can outperform other methods in high dimensions.
A new approach to continuous-time universal portfolios using pathwise Itô calculus.
problem Continuous-time version of Cover's universal portfolio strategies.
method Pathwise Itô calculus approach to establish existence and properties of universal portfolio strategies.
result The universal portfolio strategy's portfolio value process is the average of all values of constant rebalanced strategies.
ULFS-KDPE estimates parameters efficiently without influence functions.
problem Estimating pathwise differentiable parameters in nonparametric models.
method Kernel debiased plug-in estimator based on universal least favorable submodel.
result Semiparametric efficiency achieved without influence function derivation.
The purpose of this article is to present the theory of higher order connections on vector bundles from a viewpoint inspired by projective differential geometry.
New method computes pathwise gradients for non-reparameterizable distributions.
problem Computing gradients for complex distributions not directly amenable to the reparameterization trick.
method Using optimal transport theory, compute gradients for Gamma, Beta, and Dirichlet distributions.
result Optimal gradients have reduced variance and are competitive with other methods.
This paper deals with the enumeration of the higher order non-trivial compositions of the differential operations and the directional derivative in the space Rn (n≥3). We present the recurrences for a counting the higher order non-trivial compositions.
Motivated by obtaining a consistent mathematical description for the radiation reaction of point charged particles in linear classical electrodynamics, a theory of generalized higher order tensors and differential forms is introduced. The generalization of some fundamental notions of the differential geometry and the t…
New machine learning methods solve complex PDEs with improved accuracy.
problem Solving fully nonlinear PDEs with convex Hamiltonian.
method Rewriting PDE in dual stochastic control form, estimating optimal feedback control with neural network, approximating value function with neural networks.
result Improved estimation of PDE solution and its derivatives, especially the second derivative.
The pathwise coordinate optimization is one of the most important computational frameworks for high dimensional convex and nonconvex sparse learning problems. It differs from the classical coordinate optimization algorithms in three salient features: {\it warm start initialization}, {\it active set updating}, and {\it …
GENIE accelerates DDM synthesis with higher-order solvers.
problem Efficiently solving the differential equation for high-quality generation.
method Higher-order Taylor methods, utilizing Jacobian-vector products.
result GENIE significantly accelerates synthesis compared to previous solvers.
We formulate higher order variations of a Lagrangian in the geometric framework of jet prolongations of fibered manifolds. Our formalism applies to Lagrangians which depend on an arbitrary number of independent and dependent variables, together with higher order derivatives. In particular, we show that the second varia…
Superposition rules form a class of functions that describe general solutions of systems of first-order ordinary differential equations in terms of generic families of particular solutions and certain constants. In this work we extend this notion and other related ones to systems of higher-order differential equations …
Using supervector fields and graded forms along a morphism, we study the geometry of ordinary differential superequations, extend the formalism of higher order Lagrangian mechanics to the graded context and prove a generalization of Noether's theorem.
Tangent automates derivatives in Python, improving expressiveness and performance.
problem Efficiently calculating derivatives for complex models in Python.
method Source-code transformation for dynamically typed array programming.
result Demonstrates improved expressiveness and performance in automatic differentiation.
We study higher-order conservation laws of the non-linearizable elliptic Poisson equation ∂z∂zˉ∂2u=−f(u) as elements of the characteristic cohomology of the associated exterior differential system. The theory of characteristic cohomology determines a normal form for diffe…
A new method for solving complex financial equations.
problem Solving complex financial equations with nested conditional expectations.
method Pathwise iteration for backward SDEs.
result Computes and iteratively improves upper and lower bounds on the true solution.