Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction fairly may degrade the performance. For example, the interactions of a useless featur…
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48 results for “field interactions”
This work discovers latent field effects governing interacting dynamical systems.
problem Discovering field effects governing interacting dynamical systems.
method Proposes neural fields to learn latent force fields from observed dynamics, disentangling local object interactions and global field effects.
result Accurately discovers latent field effects in various dynamical systems.
New algorithm solves complex mean-field Schrödinger bridge problem.
problem Designing a controller for diffusion processes with nonlocal interaction.
method Generalized Hopf-Cole transform and Sinkhorn-type algorithm.
result Convergence guarantees for the proposed algorithm under mild assumptions.
Learning interaction kernels in mean-field equations of 1st-order systems of interacting particlesstat.ML
Algorithm learns interaction kernels for particle systems from data.
problem Understanding and modeling interactions in systems of interacting particles.
method Nonparametric algorithm using least squares with regularization, probabilistic error functional, and reproducing kernel Hilbert space convergence.
result The algorithm converges optimally and accurately learns interaction kernels.
Study identifies unique minimizers for interaction kernels in particle systems.
problem Identifying unique interaction kernels in mean-field equations of interacting particles.
method Data-adaptive spaces, RKHS analysis, regularization.
result Characterization of identifiability in both finite and infinite particle systems.
Bayesian method selects interacting regions in Markov models.
problem Estimating interacting regions in Markov Random Fields.
method Reversible Jump Monte Carlo Markov Chain algorithm with pseudoposteriors.
result Proposed method accurately selects interacting regions in simulations and real data.
-player and Mean-field Games in Itô-diffusion Markets with Competitive or Homophilous Interactionq-fin.MF
Study -player and mean-field games in Itô-diffusion markets with competitive or homophilous interactions.
problem Optimal portfolio choice in a common market with interacting players.
method Analyzes -player and mean-field games in incomplete and complete markets with CARA utilities and random risk tolerances.
result Derives explicit or closed-form solutions for equilibrium processes and game values.
The paper explores how non-Killing fields on internal spaces can produce massive gauge fields with chiral interactions.
problem Traditional Kaluza-Klein models limit gauge fields to Killing vector fields, ignoring chiral interactions.
method Investigates properties of 4D gauge fields linked to non-Killing fields on internal spaces using spin geometry and Riemannian submersions.
result Massive gauge fields linked to non-Killing fields can mix fermions with different masses and have asymmetric couplings to left- and right-handed fermions.
In a recent formulation of a quantum field theory of forward rates, the volatility of the forward rates was taken to be deterministic. The field theory of the forward rates is generalized to the case of stochastic volatility. Two cases are analyzed, firstly when volatility is taken to be a function of the forward rates…
Enhances quantum sensing by eliminating multiple oscillations in field amplitude estimation.
problem Multiple oscillations in field amplitude estimation due to inter-qubit interactions at high qubit densities.
method Adopting a quantum circuit learning framework to approximate a target function by optimizing gate parameters.
result Elimination of multiple oscillations, leading to enhanced dynamic range of quantum sensing.
A new method reduces inference cost for FwFM by allowing it to scale with item fields only.
problem High computational cost in FwFM for large field counts.
method Low-rank diagonal plus symmetric decomposition for field-wise interactions.
result Aggressive rank reduction outperforms pruning in accuracy and speed.
Renormalization in neural networks linked to quantum field theory.
problem Implementing renormalization in neural networks.
method Mapping neural networks to quantum field theory, applying renormalization techniques.
result Changing weight standard deviation corresponds to a renormalization flow.
Following the statistical mechanics methodology, firstly introduced in macroeconomics by Aoki [1996,2002], we provide some insights to the well known works of Greenwald and Stiglitz [1990, 1993]. Specifically, we reach analytically a closed form solution of their models overcoming the aggregation problem. The key idea …
The paper analyzes the dynamics of tokens in transformer models at moderate interaction levels.
problem Understanding the evolution of tokens in transformer models at moderate interaction levels.
method Modeling transformer models as a system of particles interacting in a mean-field way and studying the corresponding dynamics.
result Characterization and convergence of the limiting dynamics in different phases of the system.
Recursive Maximum Likelihood Estimation for Interacting Particle Systems using Virtual Particlesstat.ME
Estimates log-likelihood of interacting particle systems using virtual particles.
problem Inconsistent estimation of finite-particle log-likelihood in large particle systems.
method Stochastic gradient estimate using continuous trajectory and virtual particle systems.
result Convergence to stationary points of limiting mean-field system's log-likelihood.
Proposes FEFM and DeepFEFM for CTR prediction, outperforming state-of-the-art models.
problem Click-through rate prediction in online applications.
method Field-Embedded Factorization Machine (FEFM) and its deep counterpart DeepFEFM, combining feature embeddings and deep neural networks.
result FEFM and DeepFEFM outperform existing models in CTR prediction tasks.
This paper contains a summary of mathematical researches of stochastic properties of the long time behavior of a continuously observed (and interactively controlled) quantum--field top. Applications to interactively controlled stochastic computer-graphic dynamical systems are also discussed.
Mean Field Portfolio Gamesq-fin.MF
Study Nash equilibrium in mean field portfolio games with random market parameters.
problem Modeling wealth and relative performance in competitive financial markets.
method Martingale optimality principle approach to characterize Nash equilibrium in mean field FBSDE.
result Unique Nash equilibrium found under weak interaction assumption and market parameters independence.
We present a geometric approach to the field theory with higher order anisotropic interactions. The concepts of higher order space, or locally anisotropic, space (in brief, h-space, or la-space) are introduced as general ones for various types of higher order extensions of Lagrange and Finsler geometry and higher dimen…
Prior distributions of binarized natural images are learned by using a Boltzmann machine. According the results of this study, there emerges a structure with two sublattices in the interactions, and the nearest-neighbor and next-nearest-neighbor interactions correspondingly take two discriminative values, which reflect…
A geometric construction for obtaining a prolongation of a connection to a connection of a bundle of connections is presented. This determines a natural extension of the notion of canonical energy-tensor which suits gauge and gravitational fields, and shares the main properties of the energy-tensor of a matter field in…
In this work we introduce a model of default contagion that combines the approaches of Eisenberg-Noe interbank networks and dynamic mean field interactions. The proposed contagion mechanism provides an endogenous rule for early defaults in a network of financial institutions. The main result is to demonstrate a mean fi…
Kernel methods are studied in a mean field limit for high-dimensional data.
problem Analyzing kernel methods in high-dimensional data with many variables.
method Investigation of kernel methods in the mean field limit of interacting particle systems.
result Rigorous mean field limit of kernels and detailed analysis of the limiting reproducing kernel Hilbert space.
Integrates MRF into multimodal VAE for better complex intermodal interactions.
problem Lack of effective modeling of complex intermodal interactions in multimodal VAEs.
method Incorporates Markov Random Field into prior and posterior distributions of multimodal VAE.
result Demonstrates superior performance in managing complex intermodal dependencies.
Interactive tool helps choose and understand classification metrics.
problem Common metrics for binary classification have limitations.
method Graphical application to visualize and explore evaluation metrics.
result Promotes careful attention to interpretation of metrics.
New geometry theory solves dark matter issues.
problem Addressing dark matter and energy in Weyl geometry.
method Developed a generalized Weyl integrable geometry (GWIG) with interactions and anisotropic dilation.
result Solved singularity issues in point charged particle models.
Using holographic renormalization coupled with the Caffarelli/Silvestre\cite{caffarelli} extension theorem, we calculate the precise form of the boundary operator dual to a bulk scalar field rather than just its average value. We show that even in the presence of interactions in the bulk, the boundary operator dual to …
Click-through rate (CTR) prediction is a critical task in online display advertising. The data involved in CTR prediction are typically multi-field categorical data, i.e., every feature is categorical and belongs to one and only one field. One of the interesting characteristics of such data is that features from one fi…
HGNet improves GNNs' ability to handle long-range interactions in graphs.
problem Insufficiency of GNNs in capturing long-range interactions.
method Introduces hierarchical message passing models with multi-resolution graph representations.
result HGNet outperforms conventional GNNs in molecular property prediction.
I discuss geometry and normal forms for pseudo-Riemannian metrics with parallel spinor fields in some interesting dimensions. I also discuss the interaction of these conditions for parallel spinor fields with the condition that the Ricci tensor vanish (which, for pseudo-Riemannian manifolds, is not an automatic consequ…
MF-PID uses interacting samples to efficiently transport probability mass.
problem Efficiently transporting probability mass in generative models.
method Introducing Mean-Field Path-Integral Diffusion (MF-PID) where samples become interacting agents.
result MF-PID achieves 19-24% reductions in control energy for demand-response control of energy systems.
New method bypasses assumptions for unbiased estimation of complex system interactions.
problem Inferring pair-wise and higher-order interactions from observational data.
method Cross-disciplinary approach using Targeted Learning for unbiased estimation.
result Universal estimator of all-order symmetric interactions without parametric assumptions.
A stochastic volatility approximation for a tick-by-tick price model with mean-field interactionq-fin.MF
Modeling price formation with interacting Hawkes processes leading to stochastic volatility with leverage.
problem Capturing the complex dynamics of price formation in financial markets.
method Agent-based approach to aggregate self-exciting point processes with mean-field interaction.
result Aggregated model converges to a stochastic volatility model with leverage effect and faster-than-linear mean reversion.
Study explores optimal strategies in games with multiple players and mean-field interactions.
problem Optimal strategies in games with multiple players and mean-field interactions.
method Exploration of three different notions of optimality, including mean-field control solution, mean-field coarse correlated equilibria, and mean-field Nash equilibria.
result Approximation of cooperative and competitive equilibria in large -player games by mean-field control and mean-field equilibria.
iKF method uncovers complex variable interactions for scientific discovery.
problem Limited interpretability of existing models in decision-making applications.
method Iterative Kings' Forests (iKF) method to uncover multi-order interactions.
result iKF provides strong interpretive power for explainable modeling.
An informed broker optimizes trading strategies in a market influenced by many traders.
problem Optimizing trading strategies for an informed broker in a market with many traders.
method Developed a mean-field game approach to derive equilibrium strategies for both the broker and traders.
result The broker's optimal strategy involves a Stackelberg equilibrium, leading and traders following.
The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systemscond-mat.dis-nn
This work maps Boltzmann distributions to ARNNs for better physics-based model approximations.
problem Approximating Boltzmann distributions of binary systems.
method Exact mapping of Boltzmann distribution to autoregressive neural network architecture.
result New ARNN architectures derived from physical models show superior performance.
Deep learning enhances solving complex mean field games in finance.
problem Solving large-scale mean field games with financial applications.
method Combining mean field games theory with deep learning techniques.
result Improved solutions for large-scale financial games.
Graphon game model simplifies stochastic interactions among agents.
problem Complex interactions among heterogeneous agents in stochastic games.
method Introduced a discrete-time graphon game formulation with a representative player.
result Existence and uniqueness of graphon equilibrium proven with mild assumptions.
The search for higher-order feature interactions that are statistically significantly associated with a class variable is of high relevance in fields such as Genetics or Healthcare, but the combinatorial explosion of the candidate space makes this problem extremely challenging in terms of computational efficiency and p…
We review recent quantitative results on the approximation of mean field diffusion equations by large systems of interacting particles, obtained by optimal coupling methods. These results concern a larger range of models, more precise senses of convergence and links with the long time behaviour of the systems to be con…
We formulate and analyze a multi-agent model for the evolution of individual and systemic risk in which the local agents interact with each other through a central agent who, in turn, is influenced by the mean field of the local agents. The central agent is stabilized by a bistable potential, the only stabilizing force…
We consider a system of diffusion processes that interact through their empirical mean and have a stabilizing force acting on each of them, corresponding to a bistable potential. There are three parameters that characterize the system: the strength of the intrinsic stabilization, the strength of the external random per…
Study on HFTs' interactions with a large trader using mean field game theory.
problem Interactions between high-frequency traders and a large trader executing assets at discrete times.
method Modeling HFTs' behavior using a jump process and solving the equilibrium through mean field game approach.
result Inventory-averse HFTs lower LT's costs when market impact is large.
The Standard Model of elementary particles is a theory unifying three of the four basic forces of the Nature: electromagnetic, weak, and strong interactions. In this paper we consider the Standard Model in the presence of a classical (non-quantized) gravitation field and apply a bundle approach for describing it.
Kernel-based mean-field games use MMD penalties for interaction and target costs.
problem Optimizing mean-field games with specific cost functions.
method Kernel structure, random Fourier U-statistics, neural network training.
result Sample-level convergence theorem and rate of convergence proved.
Novel approach to Nash equilibrium in mean-field stochastic games with operator resolvents.
problem Finding Nash equilibrium in mean-field stochastic games with mean-field interaction.
method Proposed a novel approach to derive Nash equilibrium semi-explicitly using operator resolvents and stochastic Fredholm equations.
result Equilibrium of the -player game converges to mean-field equilibrium, and -Nash equilibrium derived as a by-product.
NON model improves tabular data classification accuracy.
problem Tabular data classification in real-world applications.
method Field-wise network, across field network, operation fusion network.
result NON significantly outperforms state-of-the-art models.