Study on how deterministic dependencies affect information synergy and redundancy.
problem Understanding how deterministic dependencies impact information synergy and redundancy.
method Systematic analysis of deterministic dependencies on information decomposition.
result Identifies how negative terms can originate from deterministic dependencies and discusses implications for neural coding.
This paper separates static and dynamic features in video data.
problem Combining static and dynamic features in video data.
method Hierarchical Variational Auto-encoders with factored prior distributions.
result The model successfully separates static and dynamic features.
Technical proofs for Radon-Nikodym derivative identities.
problem Formalizing and proving theorems on Radon-Nikodym derivatives.
method Careful consideration of conditional and marginal probability measures.
result New interpretation of mutual and lattum information sums.
Improved autoencoder for F0-consistent voice conversion.
problem Non-parallel many-to-many voice conversion with prosodic information leakage.
method Conditional autoencoder with information-constraining bottlenecks.
result Controlled F0 contour and improved speech quality.
Study Hardy identities and inequalities on Cartan-Hadamard manifolds.
problem Existence and nonexistence of extremal functions in Hardy inequalities.
method Using the notion of a Bessel pair, we derive Hardy identities and inequalities.
result Established several Hardy type inequalities with improvements and understandings.
Study evaluates impact of probabilistic identity data in lookalike targeting campaigns.
problem Evaluate the impact of probabilistic identity data in lookalike targeting campaigns.
method Employ off-policy techniques to evaluate without risking large ad spend or A/B tests.
result Significant lift in conversion rate with identity-powered lookalikes.
A single algebraic identity unifies information-theoretic variational results.
problem Deriving and generalizing classical information-theoretic variational results
method Proving a single algebraic mixed coincidence identity
result Unified derivation of classical cornerstones of information theory
Public trading wallets reveal price information not captured by anonymous data.
problem Informed traders' anonymity in public exchanges.
method Reconstructed full-depth limit order book from 17.1 billion messages.
result Wallets' aggressive orders predict returns, with a 13.2% gain over anonymous benchmarks.
The paper decomposes probabilistic scores into reliability, uncertainty, and information loss.
problem Understanding the reliability and uncertainty of probabilistic predictions.
method Developed decomposition identities for proper losses, quantifying reliability, residual uncertainty, and information gain.
result A three-term identity for classification scores, revealing miscalibration, grouping term, and feature-level uncertainty.
Unsupervised mesh disentanglement separates identity and pose.
problem Geometric disentanglement for 3D deformable models.
method CFAN-VAE architecture using conformal factor and normal features.
result CFAN-VAE achieves state-of-the-art performance on unsupervised geometric disentanglement.
Deep networks can approximate smooth functions by compositions of nearly identity functions.
problem Optimizing deep networks for smooth function approximation.
method Representing smooth functions as compositions of near-identity functions with decreasing Lipschitz constants.
result Functional gradient methods for residual networks avoid suboptimal critical points in the near-identity region.
Paper proposes a method to encrypt faces while maintaining visual similarity.
problem Protecting personal data from unauthorized face recognition.
method Targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks.
result TIP-IM provides 95%+ protection success rate against face recognition models.
Paper proposes a framework to protect user anonymity in emotion recognition.
problem Preserving user anonymity in face-based emotion recognition systems.
method Adversarial learning framework using CNN architecture.
result The proposed approach minimizes identity-specific information and maximizes emotion-dependent information.
In this communication, we describe some interrelations between generalized q-entropies and a generalized version of Fisher information. In information theory, the de Bruijn identity links the Fisher information and the derivative of the entropy. We show that this identity can be extended to generalized versions of en…
Paper improves Fisher information estimation methods.
problem Estimating Fisher information for location parameters.
method Revisits and improves Bhattacharya estimator, introduces clipped estimator.
result Clipped estimator shows superior convergence rates in Gaussian noise.
Paper develops a neural network method to anonymize data without losing important information.
problem Protecting sensitive information while preserving useful data for analysis.
method Adversarial neural networks training with three sub-networks to prevent private labels from being predictive.
result Demonstrated success in anonymizing handwritten digits and sentiment analysis data.
Language models allocate information storage, not collapsing into uniform representations.
problem Incomplete neural collapse in language model representations.
method Analyzing variance and information sharing across 14 models, proving an information floor.
result Within-class variance is allocated information storage, not collapsed into uniform representations.
ITF improves DSR but inflates curvature, while marginal likelihood reduces it, affecting QoIs.
problem Curvature mismatch between teacher forcing and marginal likelihood in chaotic dynamical systems.
method Comparing objective-induced curvatures of ITF and marginal likelihood in a probabilistic switching augmentation of AL-RNNs.
result Curvature inflation by ITF and reduction by marginal likelihood affect dynamical quantities of interest.
Identity-link IRT improves TVD-MI scores without curvature violations.
problem Preserving additivity in TVD-MI scores for efficient LLM evaluation.
method Derives clipped-linear model from Gini entropy maximization, using identity link.
result Identity-link yields lower curvature violations (median curl 0.080-0.150) compared to probit/logit.
Kernel analysis reveals rumor truth from diffusion patterns alone.
problem Detecting unverified rumors on Twitter using text and user identities.
method Graph kernels to extract diffusion patterns from Twitter cascade structures.
result Diffusion patterns are highly informative of rumor truth or falsehood.
Augments GNNs with diversification to preserve node identity.
problem Current GNNs filter node information, potentially losing node identity.
method Integrates diversification operators with aggregation to enrich node representations.
result Significant performance boost on 9 node classification tasks.
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
This study shows how social insects and machine learning methods share a common mathematical framework.
problem Understanding how decentralized systems achieve optimal decision-making.
method Developed a rigorous mathematical framework to show isomorphism between ant colonies and ensemble machine learning.
result Demonstrated that ant colony decision-making and random forest learning implement identical variance reduction strategies through decorrelation of identical units.
Anonymization reduces economic signal extraction from financial texts.
problem Reducing meaningful economic signals from financial texts due to anonymization.
method Analyzed the impact of anonymization on textual understanding and economic signal extraction.
result Information loss due to anonymization is severe and pervasive, outweighing its benefits in certain financial applications.
Paper tackles zeroth-order optimization for nonconvex problems with constraints, high-dimensions, and saddle-points.
problem Optimization of nonconvex functions with constraints and high-dimensionality, avoiding saddle-points.
method Proposes zeroth-order stochastic approximation algorithms, including conditional gradient and truncated gradient methods, and a zeroth-order cubic regularization Newton's method.
result Demonstrates algorithms achieving rates similar to standard stochastic gradient methods, with rates dependent on poly-logarithmic dimensionality.
PUMA interprets metabolomics data to predict pathway activity and assign chemical identities.
problem Interpreting metabolomics data to determine biochemical pathway activities.
method Generative probabilistic modeling using stochastic sampling.
result PUMA predicts pathway activity and assigns chemical identities to metabolites.
Optimal rates for learning hidden tree structures are determined.
problem Learning hidden tree structures from noisy data.
method Study of the (noisy) information threshold and the Chow-Liu algorithm.
result Optimal rates for structure recovery are inversely proportional to the information threshold squared.
This paper discovers new identities linking geodesic and orthogeodesic lengths on hyperbolic surfaces.
problem Understanding relationships between geodesic and orthogeodesic lengths on hyperbolic surfaces.
method Investigates a broad family of identities involving lengths of all closed geodesics and orthogeodesics.
result Introduces new identities that include lengths of all closed geodesics, contrasting with previous identities.
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using the Expectation Maximization (EM) algorithm. This algorithm, however, can get trapped in local maxima. In this paper we explore a new appro…
Establishes a correspondence between two mathematical identities.
problem None explicitly stated; focuses on identity correspondence.
method Establishes correspondence between Pestov and Weitzenböck identities.
result Established correspondence between Pestov and Weitzenböck identities.
The Yukawa term in statistical mechanics quantifies information generation.
problem Understanding information generation in statistical mechanics.
method Defining a statistical product and Yukawa term to quantify information generation.
result The Yukawa term diverges in the quantum case, indicating Bose-Einstein condensation.
We consider utility maximization problem for semi-martingale models depending on a random factor ξ. We reduce initial maximization problem to the conditional one, given ξ=u, which we solve using dual approach. For HARA utilities we consider information quantities like Kullback-Leibler information and Hellinger inte…
Deep learning predicts user identity, activity, and location from Wi-Fi signals.
problem Privacy concerns and need for non-invasive user authentication, activity classification, and tracking.
method End-to-end deep learning framework using passive Wi-Fi signals.
result System autonomously predicts user identity, activity, and location without user intervention.
Improved covariance matrix estimation for portfolio optimization with guaranteed PSD and controlled conditioning.
problem Guaranteeing positive semidefinite ness and controlling spectral conditioning in IQ estimators.
method Introducing squeezing identity and atomic-IQ parameterization to construct structured channel matrices with PSD guarantees and analytic eigen floor for conditioning control.
result Atomic-IQ improves Sharpe ratios and delivers a more stable risk profile compared to standard estimators.
CN normalizes channels for better time series model performance.
problem Improper channel identification in time series models.
method Channel Normalization (CN) and its variants (ACN, PCN) using distinct affine transformations.
result Significant performance gains across various time series models.
We develop a probabilistic consumer choice framework based on information asymmetry between consumers and firms. This framework makes it possible to study market competition of several firms by both quality and price of their products. We find Nash market equilibria and other optimal strategies in various situations ra…
The paper proves geometric and spectral alignment for deep neural networks.
problem Understanding the singular spectra of deep neural network layers.
method Proves deterministic quotient-geometric estimates for singular spectra of Frobenius-normalized layer factors.
result Exact power-law spectra form a trace-normalized Cartan orbit under Frobenius normalization.
Quandle homology was defined from rack homology as the quotient by a subcomplex corresponding to the idempotency, for invariance under the type I Reidemeister move. Similar subcomplexes have been considered for various identities of racks and moves on diagrams. We observe common aspects of these identities and subcompl…
Einstein's philosophy uses differential identities to derive GR field equations.
problem Constructing field theories in alternative geometries.
method Explains differential identities and their role in GR and PAP-geometry.
result Derived a more general differential identity in PAP-geometry.
There is a remarkable and canonical problem in 3D geometry and topology: To understand existing models of 3D fluid motion or to create new ones that may be useful. We discuss from an algebraic viewpoint the PDE called Euler's equation for incompressible frictionless fluid motion. In part I we define a "finite dimension…
The paper derives curvature identities for 5D and 6D Einstein manifolds.
problem Deriving curvature identities for specific dimensions of Einstein manifolds.
method Using Patterson's curvature identities and the Chern-Gauss-Bonnet Theorem, the paper provides explicit formulae for 5D and 6D Einstein manifolds.
result The curvature identities for 5D and 6D Einstein manifolds are confirmed to be consistent with previous work.
Global Pestov identity proved on frame bundle and related fibrations.
problem Global Pestov identity on frame bundles and fibrations.
method Global Pestov identity on frame bundles and fibrations.
result Global Pestov identity on frame bundles and fibrations.
RLINK uses deep reinforcement learning to improve user identity linkage across social networks.
problem Recognizing the same user across different social networks.
method Converts user identity linkage into a sequence decision problem and uses deep reinforcement learning to optimize the linkage strategy.
result Achieves better performance than state-of-the-art methods in experiments on various datasets.
Proves Bochner's identity on graphs using a new auxiliary graph.
problem Extending Bochner's identity to graph theory.
method Introduces a complete tangent graph to prove the identity.
result Validates Bochner's identity on graphs.
Doodles link to commutator identities in a 2-sphere.
problem Understanding commutator identities in free groups via doodles.
method Analyzing doodles with proper noose systems and establishing bijections.
result A bijection between doodles and commutator identities.
The paper decomposes unsupervised learning's generalization error into model, data, and variance components.
problem Understanding the components of unsupervised learning's generalization error.
method Information-geometric decomposition of the Kullback-Leibler generalization error.
result The optimal rank in ε-PCA is the noise floor, balancing model-error gain and data-bias cost. The paper studies harmonic identity maps on Riemannian manifolds.
problem Understanding harmonicity of identity maps on Riemannian manifolds.
method Constructing new examples and defining a symmetric tensor field.
result New examples of identity harmonic maps are constructed.
Paper learns identity-sensitive word embeddings from text corpora.
problem Lack of context-aware word embeddings.
method Constructs a heterogeneous network of words and identities, then embeds into a low-dimensional space.
result Identity-sensitive word embeddings capture different meanings of words.