The paper extends portfolio theory to include contingent claim functions for option pricing.
problem Developing a method to price options using portfolio generating functions.
method Extending portfolio theory to include contingent claim functions and applying partial differential equations.
result A method to price options using portfolio generating functions and replicable contingent claim functions.
Generative method avoids function estimation for data generation.
problem Challenges in function estimation for generative models.
method Deterministic point transport with gradient descent.
result Data generation possible without function estimation.
Proposes a generalized XGBoost method for nonconvex loss functions.
problem Limited to convex loss functions in XGBoost.
method Extends XGBoost to use nonconvex loss functions and multivariate loss functions.
result Generalized XGBoost method can model multiple parameters in various distributions.
Derives PF-ODE for infinite-dimensional functions, improving function generation tasks.
problem Efficient inference in infinite-dimensional diffusion models.
method Derives PF-ODE in infinite-dimensional function spaces.
result Reduces function evaluations while maintaining sample quality.
New method generates portfolios using market weights and past data.
problem Creating efficient trading strategies based on market weights.
method Pathwise generation of portfolios using market weights and past data.
result Improved conditions for outperforming the market over time.
We show that for smooth manifolds X and Y, any isomorphism between the special algebra of Colombeau generalized functions on X, resp. Y is given by composition with a unique Colombeau generalized function from Y to X. We also identify the multiplicative linear functionals from the special algebra of Colombeau generaliz…
Based on Colombeau's theory of algebras of generalized functions we introduce the concepts of generalized functions taking values in differentiable manifolds as well as of generalized vector bundle homomorphisms. We study their basic properties, in particular with respect to some new point value concepts for generalize…
FFM generates functions between Gaussian and data distributions.
problem Generating functions between Gaussian and data distributions.
method Define a path of measures, learn a vector field to generate this path.
result FFM outperforms other function-space generative models.
Generative models for function-valued data in infinite dimensions.
problem Lack of semantics relating discretized data to underlying functional forms.
method Generalized diffusion models to function space, using Gaussian measures on Hilbert spaces.
result Explicit specification of function space allows unconditional and conditional generation of function-valued data.
The paper generalizes inequalities on almost Kähler manifolds.
problem Generalizing inequalities on almost Kähler manifolds.
method Considered Donaldson gauge functional and twisted Aubin functionals.
result Generalized inequality between Aubin functionals.
Neural FGP learns portfolio generating functions from data.
problem Portfolio optimisation challenges in estimating drifts and covariances.
method Neural network approach to learn G ( ⋅ ) G(\cdot) G ( ⋅ ) from market data. result Neural FGP outperforms classical benchmarks.
The paper studies generalization bounds for VRM, a variant of ERM.
problem Understanding the generalization performance of VRM.
method Proves generalization bounds for VRM under specific conditions.
result Generalization performance of VRM depends on vicinal function choice and function class quality.
Interdisciplinary result linking minimal surfaces to generalized harmonic functions.
problem Linking minimal surfaces to generalized harmonic functions.
method Interdisciplinary approach combining geometric concepts and harmonic functions.
result Interdisciplinary result linking minimal surfaces to generalized harmonic functions.
The paper classifies singularities of plane congruences and affine distance functions.
problem Classifying singularities of plane congruences and affine distance functions.
method Classification through 2-parameter plane congruences in \(\mathbb{R^4}\) and affine normal plane congruences.
result Generic singularities of plane congruences and affine distance functions are classified.
Study one-dimensional topological theories with linear generating functions.
problem Understanding one-dimensional topological theories with defects.
method Construct bases of hom spaces for decorated unoriented one-dimensional cobordisms.
result Gram determinant and linear generating functions constructed.
Distance function to a finite set is a topological Morse function.
problem Characterizing the topological Morse function of a finite set.
method Analyzing the distance function to a finite set in \(\mathbb{R}^n\).
result Distance function is a topological Morse function, with precise critical points and indices.
FDPs generalize diffusion models to function spaces, enabling efficient image generation.
problem Efficiently generating images from continuous data.
method Introducing FDPs with new mathematical frameworks and training objectives.
result FDPs achieve high-quality image generation with fewer parameters.
New L L L -functions for 3-manifolds connect to Witten invariants and relate to generalized Bernoulli polynomials.
problem Understanding L L L -functions for 3-manifolds and their invariants. method Using Mellin transforms and asymptotic techniques, proving entire functions and their values.
result Linear relations between L L L -function values at negative integers, generalizing known zeta functions. Study shows infoGAN's generalization error bound for two-layer networks.
problem Understanding generalization error in infoGAN for two-layer neural networks.
method Analyzes the difference between empirical and population objective functions, derives Rademacher complexity bounds.
result Derives error bound for infoGAN's generalization error in a two-layer network.
FunDiff models physical functions using diffusion and autoencoders.
problem Adapting generative models to continuous physical functions.
method Combines latent diffusion with function autoencoder, enforcing physical priors.
result Achieves optimal convergence rates for physical function estimation.
New insights into risk aversion for complex decision models.
problem Understanding risk aversion in non-monotone decision models.
method Characterization of probabilistic risk aversion for generalized rank-dependent functions.
result Probabilistic risk aversion is determined by the distortion function, which is convex or scaled quantile-spread mixtures.
Generative neural network simulates characteristic functions.
problem Simulating from characteristic functions inaccessible in closed form.
method Generative neural network with Maximum-Mean-Discrepancy loss.
result Universal algorithm independent of dimensionality and function properties.
Develops methods to estimate causal effects using instrumental variables.
problem Estimating causal effects from observational data with confounders.
method General Control Function (GCF) method, variational decoupling (VDE), semi-supervised GCF.
result General control functions can be constructed to estimate causal effects without strong assumptions.
We propose fast approximations for the generalized sliced-Wasserstein distance.
problem Efficient approximation of the generalized sliced-Wasserstein distance in high dimensions.
method Deterministic approximations using random projections and concentration of measure results.
result One-dimensional projections of high-dimensional random vectors are approximately Gaussian.
New GAN loss functions improve image generation quality and stability.
problem Improving the performance of GANs in generating high-quality images.
method Introducing least kth-order GAN (L k k k GAN) and Rényi-centric GAN loss functions. result The proposed loss functions lead to better image quality and stability.
Sharp bounds for approximating Sobolev functions by ridge functions and networks.
problem Approximating Sobolev functions with multivariate ridge functions and networks.
method Proving sharp upper and lower bounds for approximation order.
result Order of approximation asymptotically behaves as n − r / ( d − ℓ ) n^{-r/(d-\ell)} n − r / ( d − ℓ ) . Paper develops efficient RL algorithm for general value function approximation.
problem Lack of theory for RL with general value function approximation.
method Provable efficient RL algorithm using bounded eluder dimension.
result Achieves a regret bound of O ~ ( p o l y ( d H ) T ) \widetilde{O}(\mathrm{poly}(dH)\sqrt{T}) O ( poly ( d H ) T ) . Generative classifiers' properties are linked to linear constraints.
problem Understanding the Markov property in generative classifiers.
method Characterization of discrimination functions using linear constraints and a second order finite difference operator.
result Discrimination functions of undirected Markov network classifiers are characterized by sets of linear constraints.
Generative model learns functional vector fields for pharmacokinetics.
problem Generating accurate virtual cohorts and forecasting patient trajectories without manual tuning.
method Prior-Fitted Functional Flows model, learning functional vector fields conditioned on sparse, irregular data.
result State-of-the-art predictive accuracy on real-world datasets.
Generative models learn distributions of continuous functions.
problem Training generative models on discretized grids limits model size and data type.
method Parameterize data points by continuous functions, learn distributions over these functions.
result Models can learn rich distributions of functions independently of data type and resolution.
We extend rectified flow to infinite-dimensional Hilbert space.
problem Extending rectified flow to infinite-dimensional spaces.
method Established a rigorous functional formulation using the superposition principle for continuity equations.
result Demonstrated superior performance compared to existing models.
Proposes a new sparse recovery method using generalized error function.
problem Sparse recovery in signal processing and imaging.
method Introduces a penalty function with shape and scale parameters for sparse recovery.
result The method improves MRI reconstruction and is theoretically sound.
New neural network models learn symmetric functions of varying input sizes.
problem Learning symmetric functions with varying input sizes.
method Functional perspective on neural networks, treating symmetric functions as functions over probability measures.
result Established approximation and generalization bounds for shallow architectures that extend across input sizes.
Condition for generic sliced spaces to be globally hyperbolic.
problem Global hyperbolicity of sliced spaces without lapse or shift function hypotheses.
method Topological condition for globally hyperbolic sliced spaces.
result Generic sliced spaces are globally hyperbolic under certain conditions.
Estimates generalization gap for overparameterized models using Langevin approximation.
problem Estimating the difference between training and generalization performance in overparameterized models.
method Functional variance and Langevin approximation of functional variance.
result Demonstrates efficient estimation of generalization gaps for overparameterized models.
This work extends diffusion models to function space for better generative modeling.
problem Limited applicability of diffusion models to functional data domains.
method Introduces Denoising Diffusion Operators (DDOs) for training diffusion models in function space.
result Demonstrates accurate function-valued generation at fixed cost.
We present a novel and comprehensive approach to the study of the parametric Plateau problem for locally strictly convex (LSC) hypersurfaces of prescribed curvature for general convex curvature functions inside general Riemannian manifolds. We prove existence of solutions to the Plateau problem with outer barrier for L…
SFM generates smooth functional data without exposing real data.
problem Challenges in statistical analysis of functional data.
method Copula framework and smooth flow construction.
result SFM produces high-quality synthetic functional data.
GANs can generate realistic data without minimizing a divergence, contrary to current theory.
problem Current theory suggests GANs minimize a divergence to generate realistic data.
method Discussed various loss functions for G, showing they are not divergences and do not have the same equilibrium.
result GANs can use a wide range of loss functions, not just divergences, to generate realistic data.
A Morse 2-function is a generic smooth map from a smooth manifold to a surface. In the absence of definite folds (in which case we say that the Morse 2-function is indefinite), these are natural generalizations of broken (Lefschetz) fibrations. We prove existence and uniqueness results for indefinite Morse 2-functions …
CROWN certifies robustness of neural networks with general activation functions.
problem Certifying robustness of neural networks with general activation functions.
method Bounding activation functions with linear and quadratic surrogates, adaptively selecting surrogates for each neuron.
result Significantly improves certified lower bounds on ReLU networks compared to Fast-Lin.
We describe the Williams zeta functions and the twist zeta functions of sub-Lorenz templates generated by renormalizable Lorenz maps, in terms of the corresponding zeta-functions of the sub-Lorenz templates generated by the renormalized map and by the map that determines the renormalization type.
We study the smooth structure of convex functions by generalizing a powerful concept so-called self-concordance introduced by Nesterov and Nemirovskii in the early 1990s to a broader class of convex functions, which we call generalized self-concordant functions. This notion allows us to develop a unified framework for …
NNGP combines neural nets and GPs for function approximation and PDE solving.
problem Function approximation and solving PDEs with high accuracy and uncertainty quantification.
method Generalized NNGP with larger hyperparameters trained by ML, analytical covariance formula.
result Generalized NNGP outperforms GPs and deep NNs for both smooth and non-smooth functions.
The goal of this paper is to classify pairs of Morse functions in general position modulo the action of different groups.In particular, we obtain the classification of generic pairs of Morse functions, with or without target diffeomorphisms, and that of quotients of Morse functions.We will also present a lemma which gi…
We extend the problem of finding Hamiltonian-invariant volume forms on a Poisson manifold to the problem of construction of Hamiltonian-invariant generalized functions. For this we introduce the notion of generalized center of a Poisson algebra, which is the space of generalized Casimir functions. We study as the case …
A new method uses deep learning to efficiently sample rare transitions for estimating committor functions.
problem Efficiently sampling rare transitions to estimate committor functions in high-dimensional problems.
method DASTR (Deep Adaptive Sampling on Transition Paths) method using deep generative models.
result Significantly improved accuracy in approximating committor functions through efficient sampling.
Improved GSPGS estimators reduce bias in noisy function measurements.
problem Reduced bias in noisy function measurements.
method Generalized Simultaneous Perturbation-based Gradient Search (GSPGS) with various estimators.
result Estimators requiring more function measurements have lower bias.