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.
Proposes a dynamic matching algorithm for two-sided online markets.
Augmented bridge matching preserves coupling information between distributions.
We study graph matching with correlated Gaussian features and find thresholds for exact recovery.
Matched filters reveal optimal normalization methods for different market participants.
Graph clustering method uses templates to match vertices and outperforms classical methods.
Study sharpens threshold for matching correlated graphs without labels.
Tennis is a popular sport worldwide, boasting millions of fans and numerous national and international tournaments. Like many sports, tennis has benefitted from the popularity of rigorous record-keeping of game and player information, as well as the growth of machine learning methods for use in sports analytics. Of par…
GDT improves reinforcement learning by matching future state information efficiently.
The Maximum Mutual Information (MMI) criterion is different from the Least Error Rate (LER) criterion. It can reduce failing to report small probability events. This paper introduces the Channels Matching (CM) algorithm for the MMI classifications of unseen instances. It also introduces some semantic information method…
Paper proposes a new method for training diffusion models using Markov operators.
We propose an unsupervised object matching method for relational data, which finds matchings between objects in different relational datasets without correspondence information. For example, the proposed method matches documents in different languages in multi-lingual document-word networks without dictionaries nor ali…
While many multiple graph inference methodologies operate under the implicit assumption that an explicit vertex correspondence is known across the vertex sets of the graphs, in practice these correspondences may only be partially or errorfully known. Herein, we provide an information theoretic foundation for understand…
Improves sample efficiency in RL by matching model-based gradients.
Model predicts Bitcoin's future movements using multimodal pattern matching.
Matching datasets of multiple modalities has become an important task in data analysis. Existing methods often rely on the embedding and transformation of each single modality without utilizing any correspondence information, which often results in sub-optimal matching performance. In this paper, we propose a nonlinear…
CPFM integrates dimensionality reduction and reconstruction with flow networks.
A broad range of cross--domain generation researches boil down to matching a joint distribution by deep generative models (DGMs). Hitherto algorithms excel in pairwise domains while as increases, remain struggling to scale themselves to fit a joint distribution. In this paper, we propose a domain-scalable DGM, i…
A new method improves model generalization by recognizing representations.
QAM uses adjoint matching to optimize continuous-action RL policies efficiently.
Improved graph matching using covariates for network data integration.
Prime Match protects client stock trades from market price manipulation.
Topological Flow Matching: A Generative Modeling Framework for Structured Spaces
This paper resolves the all-or-nothing phase transition in graph matching.
We address the following problem: given two smooth densities on a manifold, find an optimal diffeomorphism that transforms one density into the other. Our framework builds on connections between the Fisher-Rao information metric on the space of probability densities and right-invariant metrics on the infinite-dimension…
A classical problem in causal inference is that of matching, where treatment units need to be matched to control units based on covariate information. In this work, we propose a method that computes high quality almost-exact matches for high-dimensional categorical datasets. This method, called FLAME (Fast Large-scale …
A new model DKMPP integrates covariates and uses an integration-free method for spatio-temporal point processes.
Unified framework for continuous-state discrete flow matching models.
A matching in a two-sided market often incurs an externality: a matched resource may become unavailable to the other side of the market, at least for a while. This is especially an issue in online platforms involving human experts as the expert resources are often scarce. The efficient utilization of experts in these p…
A new measure -variance captures local distributional shape.
The bipartite record linkage task consists of merging two disparate datafiles containing information on two overlapping sets of entities. This is non-trivial in the absence of unique identifiers and it is important for a wide variety of applications given that it needs to be solved whenever we have to combine informati…
Algorithm identifies optimal stable matching in uncertain two-sided markets.
Improved matching for multiple objects using a novel reweighting method.
The aim of this paper is to provide a new method for learning the relationships between data that have been obtained independently. Unlike existing methods like matching, the proposed technique does not require any contextual information, provided that the dependency between the variables of interest is monotone. It ca…
In record linkage (RL), or exact file matching, the goal is to identify the links between entities with information on two or more files. RL is an important activity in areas including counting the population, enhancing survey frames and data, and conducting epidemiological and follow-up studies. RL is challenging when…
Although information extraction and coreference resolution appear together in many applications, most current systems perform them as ndependent steps. This paper describes an approach to integrated inference for extraction and coreference based on conditionally-trained undirected graphical models. We discuss the advan…
Improved binning technique boosts nUV measure performance.
Efficient algorithm for graph matching in correlated stochastic block models.
Lower bounds on Bayes risk for realizable models derived using information theory.
This paper reports on Qwant Research contribution to tasks 2 and 3 of the DEFT 2019's challenge, focusing on French clinical cases analysis. Task 2 is a task on semantic similarity between clinical cases and discussions. For this task, we propose an approach based on language models and evaluate the impact on the resul…
We study how information perturbations can destabilize two-sided matching markets. In our model, agents arrive on the market over two periods, while agents in the first period do not know the types of those arriving later. Agents already present in the market may match early or wait for the small group of new entrants.…
Algorithm recovers permutations of high-dimensional Gaussian vectors with constant correlation.
Community detection or clustering is a fundamental task in the analysis of network data. Many real networks have a bipartite structure which makes community detection challenging. In this paper, we consider a model which allows for matched communities in the bipartite setting, in addition to node covariates with inform…
Enhances community detection in correlated networks with node attributes.
Domain adaptation aims to leverage the supervision signal of source domain to obtain an accurate model for target domain, where the labels are not available. To leverage and adapt the label information from source domain, most existing methods employ a feature extracting function and match the marginal distributions of…
A new method for training diffusion models using likelihood matching.
A latent-variable model is introduced for text matching, inferring sentence representations by jointly optimizing generative and discriminative objectives. To alleviate typical optimization challenges in latent-variable models for text, we employ deconvolutional networks as the sequence decoder (generator), providing l…
Paper tackles entity matching over multi-source data, optimizing alignment and mitigating negative transfer.
IMP finds sparse subnetworks that match full networks, revealing geometric insights.