New insights into surface group actions and entropy.
problem Understanding proper affine actions of surface groups.
method Explicit neighborhoods in quasifuchsian space and critical points of entropy.
result Critical points of entropy lie on the Fuchsian locus.
Mathematical analysis of SNE and t-SNE for dimension reduction.
problem Optimal mapping of high-dimensional data to low dimensions.
method Gradient flow of relative entropy to minimize the distance between points.
result The diameter of the evolving sets remains bounded for SNE but may blow up for t-SNE.
In this article, we prove the mean convex neighborhood conjecture for the mean curvature flow of surfaces in R3. Namely, if the flow has a spherical or cylindrical singularity at a space-time point X=(x,t), then there exists a positive ε=ε(X)>0 such that the flow is mean convex in a …
iPool selects informative nodes for pooling in arbitrary graphs.
problem Pooling in graph neural networks is often overlooked.
method iPool uses a criterion based on neighborhood conditional entropy to select nodes for pooling.
result iPool achieves state-of-the-art performance on graph classification tasks.
A new method for efficient structural node embeddings using Von Neumann entropy.
problem Efficiently identifying structurally equivalent nodes in complex networks.
method VNEstruct: a simple approach generating low-dimensional structural node embeddings using Von Neumann entropy.
result VNEstruct achieves robustness on structural role identification and state-of-the-art performance on graph classification tasks.
Topological entropy decreases strictly along Ricci flow near hyperbolic metrics.
problem Understanding entropy changes in flows near hyperbolic metrics.
method Analysis of geodesic flow on Riemannian manifolds with variable negative curvature.
result Topological entropy strictly decreases along normalized Ricci flow near hyperbolic metrics.
We study model recovery for data classification, where the training labels are generated from a one-hidden-layer neural network with sigmoid activations, also known as a single-layer feedforward network, and the goal is to recover the weights of the neural network. We consider two network models, the fully-connected ne…
Paper develops a robust HVA measure for dynamic hedging under liquidity stress.
problem Valuation of dynamic hedging under liquidity stress.
method Defines robust HVA as worst-case expected loss over a relative-entropy neighborhood of loss distributions for no-trade bands.
result Wider no-trade bands lower rebalancing costs but increase hedge-error risk.
The study proves a neighborhood theorem for mean curvature flow in higher dimensions.
problem Proving a canonical neighborhood theorem for mean curvature flow in higher dimensions.
method Proved a canonical neighborhood theorem for mean curvature flow of compact submanifolds in RN with a pinching condition. result Proved a canonical neighborhood theorem for mean curvature flow in dimensions n≥5. LES optimizes designs by sampling descent sequences, achieving strong sample efficiency.
problem Optimizing large, complex design spaces is infeasible and unnecessary.
method LES uses Bayesian optimization to target solutions reachable by iterative optimizers.
result LES achieves strong sample efficiency compared to existing methods.
Learn conditional averages in PAC framework for better predictions.
problem Learning average labels over neighborhoods in unknown concept class.
method Characterization of learnability using combinatorial parameters.
result Complete characterization and sample complexity bounds.
Develops a robust hedging valuation adjustment measure for dynamic hedging under liquidity-demand stress.
problem Dynamic hedging under liquidity-demand stress
method Define robust HVA as the worst-case expected loss over a relative-entropy neighborhood of the loss distribution generated by simulated rebalancing and maturity-unwind trades.
result Distinguishes fixed-radius convention from fixed benchmark-stress convention and shows wider no-trade bands lower rebalancing costs but raise hedge-error risk.
The paper characterizes curvature-dimension conditions and related inequalities on Riemannian manifolds.
problem Curvature-dimension conditions and related inequalities on Riemannian manifolds.
method Information-theoretic approach to study curvature-dimension condition, rigidity theorems, and entropy differential inequalities.
result Equivalence of curvature-dimension condition and entropy differential inequalities on Riemannian manifolds.
Proposes a method to estimate functional graphical models from multivariate random functions.
problem Estimating conditional independence structure of multivariate random functions.
method Neighborhood selection approach combining function-on-function regression and graph recovery.
result Statistical consistency of the method in high-dimensional settings.
Revises GNN neighborhood aggregation for more accurate node classification.
problem Flaws in benchmark GNN models for node classification.
method Statistical signal processing approach to neighborhood aggregation.
result Novel insights for designing more efficient GNN models.
ANTIDOTE reduces noisy labels influence during learning.
problem Learning with noisy labels.
method Information-divergence neighborhood relaxation and adversarial training.
result ANTIDOTE outperforms standard cross-entropy loss in noisy label settings.
The null energy condition is characterized via convexity of entropy in Lorentzian manifolds.
problem Characterizing the null energy condition in Lorentzian manifolds.
method Characterization via convexity of the relative entropy along displacement interpolations on null hypersurfaces.
result The null energy condition is characterized in terms of convexity of the relative entropy.
Adaptive Nucleus Truncation Improves Long-Form Reasoning
problem Improving long-form reasoning in language models
method Adaptive Nucleus Truncation Sampling (ANTS)
result Significant performance gains across various benchmarks
Minimal volume entropy vanishes or is positive under certain fiber growth conditions.
problem Conditions for minimal volume entropy to be zero or positive.
method Analyzes topological conditions related to fiber growth of maps.
result Examples of finite simplicial complexes with zero simplicial volume and large minimal volume entropy.
In this paper, we study the global geometry of complete, constant mean curvature hypersurfaces embedded in n-manifolds. More precisely, we give conditions that imply properness of such surfaces and prove the existence of fixed size one-sided regular neighborhoods for certain constant mean curvature hypersurfaces in cer…
The abstract applies waist inequality to dynamical systems and entropy.
problem Understanding the relationship between waist inequality and dynamical systems.
method Applying waist inequality to entropy and mean dimension of dynamical systems.
result Maps between dynamical systems have positive conditional metric mean dimension under certain conditions.
The paper proves properties of Renyi entropy power on Riemannian manifolds.
problem Properties of Renyi entropy power on Riemannian manifolds.
method Proof of concavity, rigidity models, Aronson-Benilan estimates, NIW formula, entropy isoperimetric inequality.
result Rigidity models and intrinsic relationships for Renyi entropy power.
The article examines entropy-information inequalities for continuous-time Markov chains under curvature-dimension conditions.
problem Proving Li-Yau inequalities and modified logarithmic Sobolev inequalities for reversible Markov chains.
method Introducing the CDΥ(κ,F) condition and deriving entropy-information inequalities. result Derives functional inequalities relating entropy to Fisher information.
The paper proves entropy power properties on Riemannian manifolds and Ricci flows.
problem Entropy power on Riemannian manifolds and Ricci flows.
method Proving concavity and convexity of Shannon entropy power for heat and conjugate heat equations on Riemannian manifolds and Ricci flows.
result Entropy power rigidity models on Einstein or quasi Einstein manifolds and shrinking Ricci solitons.
Spectral analysis of neighborhood graphs is one of the most widely used techniques for exploratory data analysis, with applications ranging from machine learning to social sciences. In such applications, it is typical to first encode relationships between the data samples using an appropriate similarity function. Popul…
The paper sets limits for sequential prediction and recursive algorithms using entropy analysis.
problem Fundamental limitations in sequential prediction and recursive algorithms.
method Entropic analysis to investigate underlying relationships of data and noises.
result Derives Lp bounds quantifiable in conditional entropy. A treatment in a neighborhood and at a point of the equivalence principle on the basis of derivations of the tensor algebra over a manifold is given. Necessary and sufficient conditions are given for the existence of local bases, called normal frames, in which the components of derivations vanish in a neighborhood or a…
A new GNN model SPIN achieves state-of-the-art performance on diverse real-world datasets.
problem Graph classification efficiency and accuracy.
method Parallel neighborhood aggregations (PA-GNNs) and SPIN model.
result SPIN model achieves state-of-the-art performance on diverse real-world datasets.
Neighborhood regression has been a successful approach in graphical and structural equation modeling, with applications to learning undirected and directed graphical models. We extend these ideas by defining and studying an algebraic structure called the neighborhood lattice based on a generalized notion of neighborhoo…
Ancient Ricci flows with nonnegative curvature operator have bounded entropy.
problem Conditions for bounded entropy in ancient Ricci flows.
method Used Perelman's entropy and Hamilton's trace Harnack inequality.
result Curvature operator nonnegativity is not necessary for bounded entropy.
The paper studies curvature conditions on manifolds with boundary.
problem Curvature preservation on manifolds with smooth boundaries.
method Constructing a family of metrics that agree with given metrics on the boundary and interior.
result Deforming metrics to ones with totally geodesic boundary while preserving curvature conditions.
The study examines conditions for minimal volume entropy of simplicial complexes.
problem Conditions for minimal volume entropy of simplicial complexes.
method Topological conditions and growth of fundamental groups.
result Examples of simplicial complexes with zero simplicial volume and large minimal volume entropy.
New geometric quantities help classify manifolds and relate to entropy.
problem Classifying Riemannian manifolds using geometric quantities.
method Introducing and analyzing asymptotic geometric quantities like p-capacity, eigenvalues, and Maz'ya constant.
result Geometric quantities coincide with entropy in specific conditions, characterizing manifolds.
Introduces REVE, a regularization scheme that compresses class conditioned entropy.
problem Improving generalization performance of deep learning models.
method Identifies a variable responsible for final prediction, compresses class conditioned entropy, introduces a variational upper bound, and integrates a tractable loss into training.
result Demonstrates the efficiency of REVE on various neural networks and datasets.
Abstract: Necessary and sufficient conditions for gradient flows of relative entropy in Lindblad equations.
problem Conditions for gradient flows in finite-dimensional Lindblad equations.
method Analyzes conditions for a finite-dimensional Lindblad equation to have a gradient flow structure for the von Neumann relative entropy.
result A finite-dimensional Lindblad equation admits a gradient flow structure for the von Neumann relative entropy if and only if the BKM-detailed balance condition holds.
Entropy-regularized NPG converges linearly with linear function approximation.
problem Analyzing convergence of entropy-regularized NPG with function approximation.
method Established finite-time convergence analyses with entropy regularization and linear function approximation.
result Entropy-regularized NPG achieves linear convergence up to a function approximation error.
We introduce a class of generalized relative entropies (inspired by the Bregman divergence in information theory) on the Wasserstein space over a weighted Riemannian or Finsler manifold. We prove that the convexity of all the entropies in this class is equivalent to the combination of the nonnegative weighted Ricci cur…
This note relaxes conditions for Kähler metrics with bounded entropy and scalar curvature.
problem Boundedness conditions for Kähler metrics with bounded entropy and scalar curvature.
method Slightly relaxes the boundedness condition on the scalar curvature.
result Apriori estimates and C3,α estimate for the potential of the Kähler metrics under relaxed conditions. This paper improves entropy bounds for ranking time-series complexity.
problem Ranking the complexity of time series processes.
method Building on information theoretic bounds, the paper improves the upper bound of conditional differential entropy using Hadamard's inequality and covariance matrix properties.
result The improved bounds can be used to rank the complexity of time series processes.
New measures for causal entropy and information gain studied.
problem Quantifying causal relationships in machine learning.
method Formal study of causal entropy and information gain.
result Established fundamental properties and relationships.
We propose a new yet natural algorithm for learning the graph structure of general discrete graphical models (a.k.a. Markov random fields) from samples. Our algorithm finds the neighborhood of a node by sequentially adding nodes that produce the largest reduction in empirical conditional entropy; it is greedy in the se…
We present a simple and fast geometric method for modeling data by a union of affine subspaces. The method begins by forming a collection of local best-fit affine subspaces, i.e., subspaces approximating the data in local neighborhoods. The correct sizes of the local neighborhoods are determined automatically by the Jo…
Necessary and sufficient conditions are investigated for the existence of local bases in which the components of derivations of tensor algebras over differentiable manifold vanish in a neighborhood or only at a single point. The problem when these bases are holonomic or anholonomic is considered. Attention is paid to t…
New entropy functionals for curved spaces help predict shape behavior.
problem Understanding entropy behavior in curved spaces.
method Introduced new entropy functionals for submanifolds of Cartan-Hadamard manifolds.
result Obtained sharp lower bounds on these entropies for certain closed hypersurfaces and observed a novel rigidity phenomenon.
EVODiff optimizes DM inference by reducing conditional entropy, improving image generation.
problem Slow and inaccurate inference in diffusion models.
method Entropy-aware variance optimization for efficient inference.
result Significant improvement in image generation quality and efficiency.
This work enhances collaborative inference privacy by minimizing conditional entropy and boosting robustness against model inversion attacks.
problem Privacy leakage in collaborative inference systems via model inversion attacks.
method Theoretical proof and derivation of a differentiable measure for bounding conditional entropy, followed by a CEM algorithm to maximize it.
result Theoretical proof and experimental validation show that CEM consistently boosts inversion robustness without compromising feature utility or efficiency.
In this note we determine the first two derivatives of the classical Boltzmann-Shannon entropy of the conjugate heat equation on general evolving manifolds. Based on the second derivative of the Boltzmann-Shannon entropy, we construct Perelman's F and W entropy in abstract geometric flows. Monotonicity of the entropies…
Paper proposes a new loss function for conditional models using soft targets.
problem Improving generalization performance of deep neural networks on supervised classification tasks.
method Introduces a new loss function compatible with soft targets, based on noise contrastive estimation.
result Soft target InfoNCE loss performs on par with cross-entropy baselines and outperforms other losses.