Clarifies global structure of Stokes-Dirac structures on manifolds.
problem Global structure of Stokes-Dirac structures on manifolds with non-trivial topology.
method Clarification through consistent boundary conditions.
result Clearer understanding of Stokes-Dirac structures on manifolds.
Sublinearly structured DNNs achieve feature learning consistency for compositional functions.
problem Achieving feature-learning and prediction consistency in deep neural networks.
method Sublinearly structured DNNs
result Sublinearly structured DNNs match or surpass wide DNNs in prediction.
ε-Consistent Mixup improves semi-supervised classification accuracy.
problem Improving semi-supervised classification accuracy with limited labeled data.
method Combines Mixup's linear interpolation with consistency regularization, using an adaptive tradeoff between the two.
result ε-Consistent Mixup yields the largest gains in low label-availability scenarios.
New method improves consistency in preference learning for neural networks.
problem Inconsistent surrogate losses in preference learning for neural networks.
method Formulated a margin-shifted ranking framework and introduced Structure-Aware H-consistency. result Proved superior consistency guarantees for capacity-bounded models using heavy-tailed surrogates.
New models capture dynamic derivatives pricing with efficient simulations.
problem Capturing dynamic features of derivatives' term structures.
method Machine learning techniques to store and efficiently simulate complex drift terms.
result First efficient dynamic term structure models.
AGM uses adversarial approach for robust prediction in structured prediction problems.
problem Structured prediction problems with complex relationships between variables.
method Adversarial Graphical Models (AGM) for distributionally robust prediction.
result AGM achieves Fisher consistency and flexibility in loss metrics.
This study compares different thermodynamic structure-informed neural networks for solving differential equations.
problem Improving the accuracy and physical consistency of neural network solutions to differential equations.
method Comprehensive evaluation of various thermodynamic formulations in physics-informed neural networks.
result Newtonian-residual-based PINNs fail to reliably recover physical quantities, while structure-preserving formulations enhance accuracy and robustness.
Regularized M-estimators are used in diverse areas of science and engineering to fit high-dimensional models with some low-dimensional structure. Usually the low-dimensional structure is encoded by the presence of the (unknown) parameters in some low-dimensional model subspace. In such settings, it is desirable for est…
Paper recovers uncertainty from dynamic valuation rules.
problem Recovering latent uncertainty from observable valuation rules.
method Developed procedures to identify and characterize uncertainty structures from valuation rules.
result Valuation rules contain sufficient information to identify and recover uncertainty structures.
Separable losses are inconsistent for structured prediction models.
problem Inconsistency of separable losses in structured prediction models.
method Analysis of separable negative log-likelihood losses for structured prediction.
result Separable losses are not Bayes consistent and may not predict the most probable structure.
In this paper we introduce a flexible HJM-type framework that allows for consistent modelling of intraday, spot, futures, and option prices. This framework is based on stochastic processes with economic interpretations and consistent with the initial term structure given in the form of a price forward curve. Furthermor…
Proposes a method to improve hierarchical clustering using set-level structural priors.
problem Lack of supervision for non-leaf structure in hierarchical clustering.
method Introduces set-level structural priors for semi-supervised hyperbolic hierarchical clustering.
result Improves label consistency and similarity-based tree quality over baselines.
Study shows k-NN classifier is not universally consistent on (0,1) but consistent on discrete and specific measure spaces.
problem Consistency of k-NN classifier under Wasserstein distance on measure spaces. method Analysis of k-NN classifier properties under Wasserstein distance, use of σ-finite metric dimension, geodesic structures of Wasserstein spaces. result Consistency of k-NN classifier on specific measure spaces (discrete, Gaussian, wavelet series) but not on (0,1). The problem of learning forest-structured discrete graphical models from i.i.d. samples is considered. An algorithm based on pruning of the Chow-Liu tree through adaptive thresholding is proposed. It is shown that this algorithm is both structurally consistent and risk consistent and the error probability of structure …
Max-min margin Markov networks improve consistency in structured prediction.
problem Statistical inconsistency in max-margin methods for structured prediction.
method Defining a max-min margin formulation to overcome statistical inconsistency.
result Proves consistency and provides an explicit algorithm with finite sample generalization bounds.
Proposes PSGAN for generating high-res anime images with structural consistency.
problem Lack of high-quality, structurally consistent full-body high-resolution anime images.
method Progressive Structure-conditional Generative Adversarial Networks (PSGAN) with progressive training.
result Demonstrates effectiveness through comparisons and diverse anime character generation.
Study shows group structures are crucial for financial model explanations.
problem Inconsistent explanations from existing explainable machine learning methods.
method Examined group structures in financial datasets and developed group versions of Shapley values.
result Group versions of Shapley values provide consistent explanations.
We propose and analyze a regularization approach for structured prediction problems. We characterize a large class of loss functions that allows to naturally embed structured outputs in a linear space. We exploit this fact to design learning algorithms using a surrogate loss approach and regularization techniques. We p…
New method learns output embeddings for structured prediction.
problem Structured prediction with output embeddings.
method Jointly learns output embedding and regression function.
result Structured predictor is a consistent estimator with smaller complexity.
Study on core consistency preservation in compressed tensors.
problem Ensuring low-rank structure is maintained during tensor compression.
method Theoretical analysis and experimental validation of compression schemes.
result Identified sufficient conditions for preserving core consistency.
Estimates multiple related causal graphs with shared causal order.
problem Discovering multiple related Gaussian DAGs with shared causal order.
method Proposes a l1/l2-regularized MLE for joint estimation of K linear structural equation models. result Joint estimator achieves better sample complexity and consistency in causal order recovery.
Novel method identifies structural differences between networks using structural equation models.
problem Identifying structural differences between networks characterized by structural equation models.
method Reparameterization and algorithm design with calibration and construction stages to identify differential structures.
result Our method outperformed independently constructed networks on synthetic data and demonstrated applicability on a real data set.
New spectral clustering method handles discrete covariates for better community detection.
problem Community detection in networks with discrete covariates.
method Spectral algorithm that separates latent network structure from observed covariates.
result Achieves perfect clustering with high probability in large, sparse networks.
Paper corrects Max-Margin loss for multi-label tasks.
problem Max-Margin loss inconsistency in multi-label classification.
method Introduced Restricted-Max-Margin loss.
result Consistent loss for multi-label tasks under milder conditions.
Proposes a method for explaining black-box models with nested feature attributions.
problem Making black-box models transparent and trustworthy.
method Model-agnostic local explanation method exploiting nested feature structure and consistency property.
result Accurate and consistent HiFAs and LoFAs estimated using fewer model queries.
Paper improves BN structure learning from incomplete data.
problem Learning BN structure from incomplete data.
method Node-Average Likelihood (NAL) approach.
result NAL proves consistent and identifiable for conditional Gaussian BNs.
We develop a framework for consistent polyhedral surrogates in classification and prediction.
problem Designing consistent polyhedral surrogates for classification and prediction problems.
method Formalizing and studying embeddings of predictions as points in R^d, assigning original loss values, and convexifying to create surrogates.
result Established a strong connection between embeddings and polyhedral surrogates, providing constructions and proofs of consistency or inconsistency.
Study analyzes bond price covariation robustly under no-arbitrage conditions.
problem Identifying the number of statistically relevant factors in the bond market.
method Nonparametric analysis of realized covariations in a general no-arbitrage setting.
result A high number of factors is needed to describe term structure evolution and term structure of volatility varies over time.
Kleinberg introduced three natural clustering properties, or axioms, and showed they cannot be simultaneously satisfied by any clustering algorithm. We present a new clustering property, Monotonic Consistency, which avoids the well-known problematic behaviour of Kleinberg's Consistency axiom, and the impossibility resu…
Paper introduces new actuarial-consistent valuations for insurance liabilities.
problem Valuation of insurance liabilities considering both financial and actuarial risks.
method Proposes two-step actuarial valuations and actuarial-consistent procedures.
result Actuarial-consistent valuations are equivalent to two-step actuarial valuations under coherence.
Bayesian method recovers causal structure in SEMs with equal error variances.
problem Recovering causal structure in SEMs with equal error variances.
method Bayesian DAG selection method using g-priors and the key property of minimum expected squared errors.
result The method consistently recovers the true graph without additional distributional assumptions.
The general problem for consistency between arbitrary transports along paths in fibre bundles and bundle morphisms between them is formulated and investigated. The special case of one fibre bundle, its morphism and transport along paths acting in it is considered. The consistency between linear transports along paths i…
Paper proposes a method to efficiently estimate structural breaks in cointegrating regressions.
problem Estimating structural breaks in cointegrating regressions is challenging due to inconsistency of group lasso.
method Adaptive group lasso procedure using a first step group lasso estimation of diverging breakpoint candidates to produce weights for a second estimation.
result The adaptive group lasso estimator delivers consistent parameter changes and oracle properties.
In the second part of our series we suggest new definitions of credit bond duration and convexity that remain consistent across all levels of credit quality including deeply distressed bonds and introduce additional risk measures that are consistent with the survival-based valuation framework. We then show how to use t…
Implementing k-NN classification using Gromov--Wasserstein distances
problem Comparing metric measure spaces
method Gromov--Wasserstein and fused Gromov--Wasserstein distances
result Universal consistency of k-NN classifiers Generates spin structure stabilizers using Dehn twists.
problem Determining generating sets for spin structure stabilizers.
method Using Dehn twists on specific curves.
result Explicit generating sets for stabilizers of spin structures.
New algorithm learns causal structures from multiple overlapping datasets.
problem Discovering causal relations from multiple datasets with overlapping variables.
method Adapting and extending bivariate causal discovery algorithms to handle overlapping datasets.
result Outperforms previous approaches on synthetic and real data.
New algebraic structures for topological pairs.
problem No specific problem stated; generalization of knot quandles.
method Introducing multi-quandles for topological pairs.
result New algebraic structures for topological pairs.
Study complex structures on geodesic and harmonic map spaces.
problem Existence and uniqueness of compatible complex structures.
method Definition and study of adapted complex and involutive structures.
result Existence and uniqueness of such structures are established.
In the third part of this series we introduce consistent relative value measures for CDS-Bond basis trades using the bond-implied CDS term structure derived from fitted survival rate curves. We explain why this measure is better than the traditionally used Z-spread or Libor OAS and offer simplified hedging and trading …
Proves DCNNs with expansive convolution are strongly universally consistent.
problem Theoretical consistency of deep convolutional neural networks (DCNNs).
method Empirical risk minimization on DCNNs with expansive convolution (with zero-padding).
result DCNNs with expansive convolution are strongly universally consistent.
This text explores strategies for learning discrete latent structures in neural networks.
problem Learning discrete latent structures in neural networks is challenging.
method Continuous relaxation, surrogate gradients, and probabilistic estimation.
result Many latent structure learning strategies use the same fundamental building blocks but apply them differently.
We consider the problem of extracting a low-dimensional, linear latent variable structure from high-dimensional random variables. Specifically, we show that under mild conditions and when this structure manifests itself as a linear space that spans the conditional means, it is possible to consistently recover the struc…
Curved Frobenius manifolds link to Hessian metrics in geometry.
problem Understanding curved Frobenius manifolds and their relation to Hessian metrics.
method Analyzing the relationship between curved Frobenius structures and Hessian metrics on spaces with non-vanishing curvature.
result Consistent curved Frobenius structures on constant curvature spaces are linked to Hessian metrics.
Consistent partial identification of causal effects proved for neural models.
problem Consistency of neural causal partial identification methods.
method Proving consistency for neural models with continuous and categorical variables, considering architecture design and Lipschitz regularization.
result Proven consistency of partial identification via neural causal models in a general setting.
We consider evaluation methods for payoffs with an inherent financial risk as encountered for instance for portfolios held by pension funds and insurance companies. Pricing such payoffs in a way consistent to market prices typically involves combining actuarial techniques with methods from mathematical finance. We prop…
This paper establishes the geometric structure of the lines of principal curvature of a hypersurface immersed in R4 in a neighborhood of the set S of its principal curvature singularities, consisting of the points at which atF least two principal curvatures are equal. Under generic conditions d…
IllinoisSL is a Java library for learning structured prediction models. It supports structured Support Vector Machines and structured Perceptron. The library consists of a core learning module and several applications, which can be executed from command-lines. Documentation is provided to guide users. In Comparison to …