We provide a dynamic programming principle for stochastic optimal control problems with expectation constraints. A weak formulation, using test functions and a probabilistic relaxation of the constraint, avoids restrictions related to a measurable selection but still implies the Hamilton-Jacobi-Bellman equation in the …
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arXiv research
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.
169,341 papers · 148 categories
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48 results for “weak constraints”
Deep learning improves weak lensing cosmological constraints.
problem Extracting non-Gaussian information from weak lensing data.
method 2D convolutional neural network trained on simulated lensing maps.
result Neural network yields 5x tighter constraints than power spectrum.
New method decomposes submartingale systems for BSDEs with weak constraints.
problem Tackles decomposition of submartingale systems for BSDEs with weak constraints.
method Introduces -submartingale systems and proves a Mertens decomposition using an original approach.
result Proves a Mertens decomposition for -submartingale systems.
Unified approach for multicalibration in weakly supervised learning.
problem Existing multicalibration methods require clean input-label pairs, which are unavailable in weakly supervised learning.
method Developed estimators and post-hoc correction methods for multicalibration under weak supervision.
result Unified framework for estimating and correcting multicalibration under weak supervision with finite-sample guarantees.
We study the Einstein-Dirac equation as well as the weak Killing equation on Riemannian spin manifolds with codimension one foliation. We prove that, for any manifold admitting real Killing spinors (resp. parallel spinors), there exist warped product metrics on such that $(M^n \ti…
Constructs a weak Poisson bracket and lifts it to differential forms.
problem Creating a weak Poisson bracket over submanifolds and foliations.
method Encoding weak Poisson structure into homotopy Poisson structure and lifting to differential forms.
result Lifts a weak Poisson bracket to the algebra of forms with a direct physical interpretation.
New methods prove weak homotopy inequivalence for manifold symmetries.
problem Understanding symmetries of smooth 4-manifolds.
method Using Pin(-2)-monopole equations.
result New examples of 4-manifolds where symmetries are not fully represented.
Method trains classifiers without labels using adversarial constraints.
problem Training classifiers without labeled data.
method Adversarial label learning method that trains classifiers to perform well against an adversary choosing labels.
result Method outperforms other weakly supervised learning approaches on real datasets.
Proposes a constrained labeling method for weakly supervised learning.
problem Combining weak supervision signals while navigating misleading correlations.
method Randomized constrained labeling within a defined space.
result Randomized constrained labeling converges after few iterations and outperforms other methods.
Study constraints on diffeomorphisms and homeomorphisms of 4-manifolds with boundary.
problem Constraints on smooth families of 4-manifolds with boundary.
method Use Manolescu's Seiberg-Witten Floer stable homotopy type.
result Inclusion map between diffeomorphisms and homeomorphisms is not a weak homotopy equivalence.
Study of surfaces meeting a plane orthogonally or contained in a line, deriving weak forms and proving regularity.
problem Critical points of the Willmore functional with boundary constraints.
method Weak forms of free boundary conditions derived by reflection.
result Proved regularity of the surfaces.
The paper proves weak continuity of Cartan structural system on semi-Riemannian manifolds with lower regularity.
problem Weak continuity of the Cartan structural system on semi-Riemannian manifolds with lower regularity.
method Formulated and proved a geometric compensated compactness theorem, deduced weak continuity of the Cartan structural system.
result Weak continuity of the Cartan structural system and Gauss-Codazzi-Ricci system on semi-Riemannian manifolds with lower regularity.
We present atomistic molecular dynamics simulations of two Polyethylene systems where all entanglements are trapped: a perfect network, and a melt with grafted chain ends. We examine microscopically at what level topological constraints can be considered as a collective entanglement effect, as in tube model theories, o…
Proposes ConstraintMatch for semi-supervised clustering with unconstrained data.
problem Leveraging unconstrained data alongside constraints for clustering models.
method Semi-supervised context with pseudo-constraining and pseudo-labeling mechanisms.
result Demonstrates effectiveness of ConstraintMatch over baselines.
The paper introduces a new method for risk measurement using weak optimal transport.
problem Risk measurement in insurance and financial contexts.
method Convex risk measures with weak optimal transport penalties, explicit representation via nonlinear transform, computational aspects, and approximations using neural networks.
result Explicit representation and computational methods for risk measures.
No-arbitrage constraints on implied variance slope are weak, leading to almost guaranteed arbitrage in many cases.
problem Weak constraints on implied variance slope in the Black-Scholes model lead to arbitrage opportunities.
method Analysis of constraints on implied variance slope and their implications for arbitrage.
result Arbitrage is almost always guaranteed in a wide range of slope values where constraints are enforced.
Consistent estimation of constrained autoregressive processes.
problem Estimating autoregressive processes with coefficients constrained to an ellipsoid.
method Use of constrained and penalized estimators under different norms.
result Provide consistency results for estimation of constrained autoregressive processes.
The study finds a continuous map achieving minmax area under Legendrian constraints.
problem Finding minmax areas under Legendrian constraints in 5D Sasakian manifolds.
method Continuous conformal Legendrian map with bounded multiplicity satisfying a weak Hamiltonian Minimal Equation.
result Continuous map achieving minmax area with bounded multiplicity.
Lasso method applied to polynomial models with hierarchy constraints.
problem Estimating parameters in polynomial models with hierarchy constraints.
method Using lasso and standard quadratic programming techniques to estimate parameters.
result The proposed methodology outperforms existing techniques in terms of validation error and model size.
End-to-end clustering framework uses neural networks and pairwise constraints.
problem Clustering data with weak labels and no predefined distance metrics.
method Neural network with contrastive learning for data-forming clusters and feature embedding.
result Framework outperforms conventional methods and is robust to the number of clusters.
Memory-limited learning tackles adversarial bandits with reduced storage.
problem Adversarial bandit problem with limited memory storage.
method Hierarchical learning policy with sublinear memory requirement.
result Established sublinear regret bounds for weak and shifting regrets.
Modified constraint operator for localized deformation with dominant energy condition.
problem Handling localized deformation with initial data sets under the dominant energy condition.
method Introduced a modified constraint operator to absorb metric changes and established local surjectivity theorem.
result Promoted dominant energy condition to strict inequality through compactly supported variations.
Framework identifies population quantities from MNAR feedback using weak shadow variables from pretrained models.
problem Estimating mean outcomes from MNAR user feedback with bias and lack of identification.
method Develops a partial identification framework using linear programs and weak shadow variables from pretrained models.
result Bounds on estimand are obtained by solving linear programs incorporating pretrained model predictions.
Proposes efficient bounds for causal effect estimation under weak confounding.
problem Estimating causal effects with weakly confounded variables.
method Develops an efficient linear program to derive upper and lower bounds on causal effect under small entropy of unobserved confounders.
result Bounds are consistent and tighter for weakly confounded variables.
We develop semistrict higher gauge theory from first principles. In particular, we describe the differential Deligne cohomology underlying semistrict principal 2-bundles with connective structures. Principal 2-bundles are obtained in terms of weak 2-functors from the Cech groupoid to weak Lie 2-groups. As is demonstrat…
The paper constrains families of smooth 4-manifolds using Seiberg-Witten invariants.
problem Understanding the topology of families of smooth 4-manifolds.
method Finite dimensional approximation of the Seiberg-Witten monopole map.
result Constructs examples of continuous -actions and shows non-smoothability.
Deconfounding Scores and Representation Learning for Causal Effect Estimation with Weak Overlapstat.ML
Deconfounding scores improve causal effect estimation with weak overlap.
problem Challenges in causal treatment effect estimation due to weak overlap in high-dimensional data.
method Propose deconfounding scores to preserve identification and target estimation while improving overlap.
result Prognostic scores are overlap-optimal under a broad family of generalized linear models with Gaussian features.
Improves GATs by adding margin-based constraints to prevent over-fitting and over-smoothing.
problem Over-fitting and over-smoothing in GATs.
method Margin-based constraints on attention weights and graph structure.
result Significant improvements over previous GATs on various datasets.
This paper concerns the questions of flexibility and rigidity of solutions to the Monge-Ampère equation which arises as a natural geometrical constraint in prestrained nonlinear elasticity. In particular, we focus on anomalous i.e. "flexible" weak solutions that can be constructed through methods of convex integration …
Cancellation-Free Regret Bounds for Lagrangian Approaches in Constrained Markov Decision Processescs.LG
New algorithm reduces regret in CMDPs without cancellation of errors.
problem Lagrangian approaches in CMDPs struggle with cancellation of errors.
method OptAug-CMDP, based on augmented Lagrangian method.
result Regret of for both objective and constraint violation.
Paper proves rigidity of weak solutions for anisotropic N-Laplacian equations with Neumann or Robin boundary conditions.
problem Rigidity of weak solutions for anisotropic N-Laplacian equations with boundary conditions.
method Established a key integral inequality involving anisotropic gradient and second fundamental form, proving rigidity under natural monotonicity assumptions.
result All weak solutions to Neumann boundary problems are constant without a priori boundedness assumption.
New conic quadratic formulations improve outlier detection in regression models.
problem Detecting outliers in regression models with corrupted data.
method Deriving stronger second-order conic relaxations without big-M constraints.
result Proposed formulations are significantly faster than existing methods.
Enhances weak lensing inference with neural summaries.
problem Extracting additional information from weak lensing convergence maps.
method Hybrid approach combining physics-based and neural summaries.
result Neural summaries extract up to 8 times more information than angular power spectra.
Unsupervised estimation of latent variable models is a fundamental problem central to numerous applications of machine learning and statistics. This work presents a principled approach for estimating broad classes of such models, including probabilistic topic models and latent linear Bayesian networks, using only secon…
This paper considers a sequence of discrete-time random walk markets with a safe and a single risky investment opportunity, and gives conditions for the existence of arbitrages or free lunches with vanishing risk, of the form of waiting to buy and selling the next period, with no shorting, and furthermore for weak conv…
New algorithms solve weak optimal transport problems for nonlinear costs.
problem Computing weak optimal transport with nonlinear costs.
method Mirror descent algorithms for primal and dual versions of WOT.
result Solutions for WOT and WOTUK compared with classical OT.
Semi-Supervised Nonlinear Distance Metric Learning via Forests of Max-Margin Cluster Hierarchiesstat.ML
Metric learning is a key problem for many data mining and machine learning applications, and has long been dominated by Mahalanobis methods. Recent advances in nonlinear metric learning have demonstrated the potential power of non-Mahalanobis distance functions, particularly tree-based functions. We propose a novel non…
Dense neural networks can't approximate all functions.
problem Approximation capabilities of dense neural networks.
method Model compression approach combining weak regularity lemma and graph neural networks.
result Existence of Lipschitz continuous functions not approximable by dense neural networks.
2D-PT improves sampling in constrained optimization problems.
problem Sampling Boltzmann distributions with soft constraints.
method Two-dimensional extension of parallel tempering.
result 2D-PT achieves near-ideal mixing in constrained problems.
Variable selection for models including interactions between explanatory variables often needs to obey certain hierarchical constraints. The weak or strong structural hierarchy requires that the existence of an interaction term implies at least one or both associated main effects to be present in the model. Lately, thi…
The paper studies surfaces in a bounded domain with orthogonal boundaries and proves curvature estimates.
problem Estimating the area of surfaces with orthogonal boundaries in a bounded domain.
method Weak formulation of orthogonality for curvature varifolds, classification of vanishing curvature varifolds.
result Existence of an orthogonal 2-varifold that minimizes curvature in the integer rectifiable class.
The paper finds the unique minimizer of area for hyperbolic bodies with curvature constraints.
problem Finding the unique minimizer of area for hyperbolic bodies with curvature constraints.
method Introduced the concept of 'thick -sausage' bodies and used extra assumption of thickness to handle non-convex inner parallel bodies.
result The thick -sausage body is the unique minimizer of area among all bodies with a given length and curvature constraints.
Stoch-GALL learns from noisy labels to improve model performance.
problem Training machine learning models with limited labeled data.
method Stochastic generalized adversarial label learning framework.
result Stoch-GALL outperforms weakly supervised learning methods in noisy label settings.
Decomposing market impact into diffusive components
problem Market impact scaling
method Decomposing impact into realized and counterfactual returns
result Implication of square-root law in information-neutral regime
Probabilistic model for weakly supervised analysis dictionary learning.
problem Discriminative analysis dictionary learning under weak supervision.
method Probabilistic modeling with EM algorithm and graph reformulation.
result Improved classification performance compared to synthesis dictionary learning.
Paper approximates fractional harmonic maps with numerical methods.
problem Approximating fractional harmonic maps with constraints and nonlocality.
method Weak compactness results and numerical methods for various PDEs.
result Convergence of numerical approximations for fractional harmonic maps.
Sampling and Reconstruction of Graph Signals via Weak Submodularity and Semidefinite Relaxationstat.ML
New method for efficient graph signal sampling and reconstruction.
problem Minimizing MSE in graph signal reconstruction with noisy data.
method Formulated as binary constraint minimization, approximated via SDP relaxation and greedy algorithm.
result Randomized greedy algorithm provides near-optimal subset with significant speedup.
Two new algorithms solve privacy-constrained SVI and SSP problems.
problem Privacy-constrained stochastic variational inequality and saddle-point problems.
method Proposed Noisy Stochastic Extragradient (NSEG) and Noisy Inexact Stochastic Proximal Point (NISPP) algorithms.
result Optimal risk bounds for weak gap function with sampling with replacement.