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.
The paper models user-advertiser interactions using point processes.
We introduce a framework for studying the effects of self-interaction on the construction of point particle initial data in General Relativity. Within this framework we rigorously prove the vanishing mass claim made by Arnowitt, Deser and Misner regarding point sources. We identify a geometric structure and a scaling p…
We use probabilistic methods to study classical solutions for systems of interacting semilinear parabolic partial differential equations. In a modeling framework for a financial market with interacting Ito and point processes, such PDEs are shown to provide a natural description for the solution of hedging and valuatio…
We introduce a class of hybrid marked point processes, which encompasses and extends continuous-time Markov chains and Hawkes processes. While this flexible class amalgamates such existing processes, it also contains novel processes with complex dynamics. These processes are defined implicitly via their intensity and a…
The paper proves no point interactions exist for 3D sub-Laplacians.
A new metric space model for point process excitations uncovers hidden interactions.
Proposes a new model for diversified interactive recommendation.
New K-theory approach classifies anyonic topological phases in 2D semimetals.
New result on critical points of Bethe free energy under deformation retracts.
Novel neural network layer improves long-range interactions in point clouds.
TimeCNN improves forecasting by refining cross-variable interactions over time.
Interactions among people or objects are often dynamic in nature and can be represented as a sequence of networks, each providing a snapshot of the interactions over a brief period of time. An important task in analyzing such evolving networks is change-point detection, in which we both identify the times at which the …
MicroRNAs (miRNAs) are small RNA molecules composed of 19-22 nt, which play important regulatory roles in post-transcriptional gene regulation by inhibiting the translation of the mRNA into proteins or otherwise cleaving the target mRNA. Inferring miRNA targets provides useful information for understanding the roles of…
Interactive machine learning with weak supervision and pre-trained embeddings.
Article models firm interactions on market with taxation.
New method predicts drug interactions from drug images.
We propose a latent self-exciting point process model that describes geographically distributed interactions between pairs of entities. In contrast to most existing approaches that assume fully observable interactions, here we consider a scenario where certain interaction events lack information about participants. Ins…
This work finds mixed equilibria in zero-sum games using interacting particle dynamics.
We consider learning under the constraint of local differential privacy (LDP). For many learning problems known efficient algorithms in this model require many rounds of communication between the server and the clients holding the data points. Yet multi-round protocols are prohibitively slow in practice due to network …
Modeling price formation with interacting Hawkes processes leading to stochastic volatility with leverage.
We present a simple model of firm rating evolution. We consider two sources of defaults: individual dynamics of economic development and Potts-like interactions between firms. We show that such a defined model leads to phase transition, which results in collective defaults. The existence of the collective phase depends…
Interactive art piece shows braid groups and plane motions.
Unified model for interactive estimation with improved learnability measure.
We propose a novel class of network models for temporal dyadic interaction data. Our goal is to capture a number of important features often observed in social interactions: sparsity, degree heterogeneity, community structure and reciprocity. We propose a family of models based on self-exciting Hawkes point processes i…
SLIM model tackles graph classification by resolving part-interaction dilemmas.
MPM-ParVI uses particle sampling for variational inference.
The paper analyzes PPM for nonconvex-nonconcave problems, identifying three regions with varying convergence guarantees.
Advanced inference techniques allow one to reconstruct the pattern of interaction from high dimensional data sets. We focus here on the statistical properties of inferred models and argue that inference procedures are likely to yield models which are close to a phase transition. On one side, we show that the reparamete…
Signed pairwise interactions conflate uniqueness, redundancy, and synergy
Modeling social network activity through user and topic interaction.
We consider a financial market model which consists of a financial asset and a large number of interacting agents classified into many types. Different types of agents are heterogeneous in their price expectations. Each agent can change its type based on the current empirical distribution of the types and the equilibri…
A new method captures higher-order interactions in data clusters.
An interactive image retrieval system learns which images in the database belong to a user's query concept, by analyzing the example images and feedback provided by the user. The challenge is to retrieve the relevant images with minimal user interaction. In this work, we propose to solve this problem by posing it as a …
We consider the two body problem with central interaction on two point homogeneous spaces from point of view of the invariant differential operators theory. The representation of the two particle Hamiltonian in terms of the radial differential operator and invariant operators on the symmetry group is found. The connect…
Estimates log-likelihood of interacting particle systems using virtual particles.
New geometry theory solves dark matter issues.
New method bypasses assumptions for unbiased estimation of complex system interactions.
The paper proposes a method to learn from both simulation and real-world data.
Feature maps, that preserve the global topology of arbitrary datasets, can be formed by self-organizing competing agents. So far, it has been presumed that global interaction of agents is necessary for this process. We establish that this is not the case, and that global topology can be uncovered through strictly local…
New method identifies important features and interactions in RF models.
A new model explains protein interactions via electron delocalization.
We consider fast kernel summations in high dimensions: given a large set of points in dimensions (with ) and a pair-potential function (the {\em kernel} function), we compute a weighted sum of all pairwise kernel interactions for each point in the set. Direct summation is equivalent to a (dense) matrix-vec…
MoReL models multi-omics data to find hidden molecular interactions.
MagNet uses neural networks to predict multi-agent dynamics from observations.
The leverage effect weakly impacts return distributions, especially for small firms.
We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges denoting observed ratings. Building on recent progress in deep learning on graph-structured data, we prop…
Motivated by the pursuit of a systematic computational and algorithmic understanding of Generative Adversarial Networks (GANs), we present a simple yet unified non-asymptotic local convergence theory for smooth two-player games, which subsumes several discrete-time gradient-based saddle point dynamics. The analysis rev…
New algorithm predicts spatio-temporal events with improved accuracy.