First place solution for cross-device user matching in online advertising.
problem Identifying same users across multiple devices from browsing logs.
method Pairwise classification using unsupervised neural feature ensemble and supervised classifiers.
result Improved accuracy in cross-device user matching compared to traditional methods.
DoCoFL compresses model updates for cross-device federated learning.
problem Downlink compression for cross-device federated learning where clients may appear only once.
method Proposes DoCoFL framework for downlink compression in cross-device federated learning.
result Significant bi-directional bandwidth reduction with competitive accuracy.
MetFA aligns source and target domains for cross-device image classification.
problem Learning discriminative class boundaries across different domains.
method Distance metric guided feature alignment (MetFA) for domain-invariant and discriminative feature extraction.
result MetFA outperforms state-of-the-art methods in cross-device image classification.
AAggFF improves federated learning fairness through sequential decision making.
problem Achieving client-level fairness in federated learning systems.
method Unified online convex optimization framework for adaptive aggregation strategies.
result AAggFF achieves better client-level fairness in federated learning.
FedBuff improves federated learning scalability with asynchronous updates.
problem Limited scalability of federated learning with synchronous updates.
method Introduces asynchronous updates (staleness) in federated learning.
result Theoretical analysis shows improved convergence rate with boundedness removed.
FLAIR dataset for federated learning benchmarks.
problem Lack of suitable federated learning datasets.
method Curated large-scale annotated image dataset for multi-label classification.
result FLAIR captures real-world federated learning challenges.
Mime algorithm improves federated learning by adapting centralized methods.
problem Mitigating client drift in federated learning.
method Combines control variates and server-level statistics to adapt centralized algorithms to federated learning.
result Mime outperforms any centralized method in federated learning.
Proposes FedPop for personalised federated learning with uncertainty quantification.
problem Uncertainty quantification and client drift in personalised federated learning.
method FedPop recasts FL into population modeling with Markov chain Monte Carlo methods.
result Non-asymptotic convergence guarantees for uncertainty quantification.
Study improves privacy in cross-silo federated learning by personalizing data.
problem Privacy concerns in cross-silo federated learning.
method Introduced sample-level differential privacy for silos, analyzed mean-regularized multi-task learning.
result Mean-regularized multi-task learning is a strong baseline for cross-silo federated learning under stronger privacy requirements.
FedShuffle improves local updates in FL, especially with data imbalance.
problem Data imbalance in FL leads to different clients performing different numbers of local updates.
method FedShuffle incorporates random reshuffling, data imbalance, and client sampling.
result FedShuffle improves upon FL methods that assume homogeneous updates in heterogeneous setups.
New approach for large-scale distributed learning systems that improve generalization performance.
problem Transitioning from centralized to distributed AI systems for complex learning tasks.
method Self-organizing hierarchical structuring mechanism based on agglomerative clustering, hierarchical generalization, and personalized learning.
result Demonstrates better generalization performance compared to conventional federated learning algorithms.
Paper proposes a new AI design philosophy for distributed learning.
problem Designing AI systems that mimic human cognitive capabilities.
method Democratized learning (Dem-AI) with self-organized hierarchical agents.
result Self-organized learning systems can perform complex tasks more efficiently.
SIGMA model improves graph matching across various applications.
problem Graph matching problem in different domains.
method Stochastic Iterative Graph Matching (SIGMA) model with multi-step refinement and dummy nodes.
result SIGMA produces significantly improved graph matching results compared to state-of-the-art models.
Neural score matching improves high-dimensional causal inference by using neural networks for balancing scores.
problem Impracticality of traditional matching methods in high-dimensional datasets due to the curse of dimensionality.
method Develops neural networks to create non-trivial, multivariate balancing scores for high-dimensional causal inference.
result Neural score matching outperforms other methods in treatment effect estimation and reducing imbalance on high-dimensional datasets.
Study dynamic matching in heterogeneous networks using ODE model.
problem Dynamic matching in heterogeneous networks with compatibility restrictions.
method Introduced an ODE model to evaluate matching algorithms.
result Demonstrated trade-off between quick matching and optimal matching.
FLAME efficiently matches high-dimensional categorical datasets for causal inference.
problem Matching treatment and control units based on covariate information in causal inference.
method FLAME learns a distance metric using a hold-out training set and uses query processing techniques for large datasets.
result FLAME achieves significantly better performance than other matching methods, scaling to huge datasets.
Efficiently learns matching rewards in two-sided markets with matrix completion.
problem Learning high-dimensional matching rewards in matching markets with limited data.
method Utilizes matrix completion with a novel approach to handle matching interference.
result Near-optimal guarantees for reward learning under matching interference.
A new matching method for causal inference that handles irrelevant variables and missing data.
problem Creating high-quality treatment-control matches for categorical data in social sciences.
method A weighted Hamming distance matching method that considers covariate importance and creates a hierarchy of covariate combinations.
result The method produces high-quality matches and handles irrelevant variables and missing data.
Proposes a dynamic matching algorithm for two-sided online markets.
problem Dynamic preferences in two-sided online matching platforms.
method Dynamic Matching Bandit Algorithm with statistical preference ranking estimation.
result Agent-optimal stable matching result with logarithmic regret bound.
The strength of association between a pair of data vectors is represented by a nonnegative real number, called matching weight. For dimensionality reduction, we consider a linear transformation of data vectors, and define a matching error as the weighted sum of squared distances between transformed vectors with respect…
Methods for creating matchings in triangulated manifolds.
problem Creating matchings in triangulated manifolds.
method Several methods for constructing matchings.
result Constructing matchings in triangulated manifolds.
Unified framework learns matching from noisy data.
problem Learning adaptive interaction costs from incomplete data.
method Inverse optimal transport with marginal relaxation.
result Efficiently predicts new matching in various contexts.
Proposes a non-adversarial method for distribution matching.
problem Stability and optimization challenges in adversarial matching methods.
method Non-adversarial VAE-based matching method with alignment upper bounds.
result Demonstrates applicability of non-adversarial matching methods without modifying original architectures.
The paper addresses statistical inference in matching markets with dependent missingness.
problem Statistical inference for two-sided matching markets with matching-induced dependence.
method Non-convex algorithm based on Grassmannian gradient descent, debiasing and projection framework.
result Near-optimal entrywise convergence rates for various matching mechanisms.
Paper tackles distribution matching by partially matching distributions, achieving robust results.
problem Robustly aligning two probability distributions.
method Developed a partial Wasserstein adversarial network (PWAN) to efficiently approximate the partial Wasserstein-1 (PW) discrepancy.
result The PWAN effectively produces highly robust matching results, outperforming state-of-the-art methods.
Partial soft-matching distance improves neural representation comparison by allowing some neurons to remain unmatched.
problem Neural representations are noisy and contain outliers, making traditional matching methods unreliable.
method Extends soft-matching distance to a partial optimal transport setting, allowing some neurons to remain unmatched.
result Partial soft-matching provides robust correspondences that are more reliable under noise and outliers.
Method finds multiple noisy graph templates in large graphs.
problem Finding multiple graph templates in noisy large graphs.
method Iteratively penalizes node-pair similarity matrix in matched filter algorithm.
result Method can sequentially discover multiple templates under mild model conditions.
Bayesian algorithm detects image matches and fraud.
problem Detecting identity matches and fraud in image databases.
method Generative model of image graph trained with matching algorithm.
result Bayesian approach improves detection accuracy.
Score matching is a recently developed parameter learning method that is particularly effective to complicated high dimensional density models with intractable partition functions. In this paper, we study two issues that have not been completely resolved for score matching. First, we provide a formal link between maxim…
NeuroMatch efficiently matches subgraphs in large graphs using neural networks.
problem Determining the presence and location of a query graph in a large target graph.
method NeuroMatch decomposes graphs into subgraphs, embeds them using graph neural networks, and matches them directly in the embedding space.
result NeuroMatch is 100x faster and 18% more accurate than existing methods.
Polynomial time algorithm matches correlated Gaussian matrices without vanishing correlation.
problem Matching vertices in two correlated Erdős-Rényi graphs.
method Iterative matching algorithm for correlated Gaussian Wigner matrices.
result First polynomial time algorithm for graph matching with arbitrarily small constant correlation.
Efficiently matches subgraphs in noisy data without node labels.
problem Subgraph isomorphism in noisy, real-valued graphs.
method Two-step approach: extract topology, then expand matches.
result Realistically sub-linear computational efficiency, robustness to noise.
We present a novel approximate graph matching algorithm that incorporates seeded data into the graph matching paradigm. Our Joint Optimization of Fidelity and Commensurability (JOFC) algorithm embeds two graphs into a common Euclidean space where the matching inference task can be performed. Through real and simulated …
The paper analyzes set-to-set matching with neural networks, focusing on theoretical generalization.
problem Theoretical analysis of set-to-set matching with neural networks.
method Generalization error analysis of set-to-set matching with neural networks.
result Theoretical insights into the behavior of set-to-set matching models.
Improved score matching methods for estimating score functions and Hessians without high dimensionality.
problem Estimating score functions and Hessians efficiently in high-dimensional data.
method Implicit score matching and denoising score matching, leveraging Gagliardo-Nirenberg inequalities.
result Achieves convergence rates similar to denoising score matching and estimates Hessians without dimensionality issues.
Paper proposes a new method for population-wise matching of sulcal graphs.
problem Challenges in matching cortical fold variations across individuals.
method Population-wise multi-graph matching of sulcal graphs.
result Effectiveness of multi-graph matching in obtaining consistent labeling of sulcal basins.
Submodular functions have many applications. Matchings have many applications. The bitext word alignment problem can be modeled as the problem of maximizing a nonnegative, monotone, submodular function constrained to matchings in a complete bipartite graph where each vertex corresponds to a word in the two input senten…
In this paper, we introduce a novel, non-recursive, maximal matching algorithm for double auctions, which aims to maximize the amount of commodities to be traded. It differs from the usual equilibrium matching, which clears a market at the equilibrium price. We compare the two algorithms through experimental analyses, …
This work improves likelihood of score-based diffusion ODEs using high-order denoising score matching.
problem The gap between maximum likelihood and score matching objectives for score-based diffusion ODEs.
method High-order denoising score matching to maximize likelihood.
result Score-based diffusion ODEs achieve better likelihood on synthetic and CIFAR-10 data.
MREC efficiently matches and aligns point clouds, useful for single cell molecular data.
problem Comparing and aligning large datasets across various domains.
method Recursive decomposition algorithm for matching data sets, optimizing over partitioning and matching algorithms.
result Demonstrates flexibility and power in applying MREC to single cell molecular data alignment problems.
Transformations studied in graphs with edges and vertices of degree zero or one.
problem Transformations between partial matchings.
method Introducing a method of presenting transformations and lattice presentations.
result Investigation of transformations with minimal area.
SDE Matching eliminates simulation for training Latent SDEs, achieving similar performance.
problem Training Latent SDEs with adjoint sensitivity methods is computationally expensive and limited.
method SDE Matching, inspired by Score- and Flow Matching, eliminates simulation for training Latent SDEs.
result SDE Matching achieves performance comparable to adjoint sensitivity methods while reducing computational complexity.
Given a matched pair of Lie groups, we show that the tangent bundle of the matched pair group is isomorphic to the matched pair of the tangent groups. We thus obtain the Euler-Lagrange equations on the trivialized matched pair of tangent groups, as well as the Euler-Poincaré equations on the matched pair of Lie algebra…
Topological Flow Matching: A Generative Modeling Framework for Structured Spaces
problem Handling structured spaces in generative modeling
method Introducing topological flow matching
result Captures the structure of the underlying domain while preserving desirable properties
Efficient algorithm for matching graphs with community structure.
problem Graph matching between correlated stochastic block models with constant correlation.
method Partition trees rooted from each vertex, comparing edge statistics to different communities.
result First low-order polynomial-time algorithm achieving exact matching with high probability in dense graphs.
New method matches curves using varifolds and Sobolev metrics.
problem Matching unparametrized curves efficiently.
method Combines varifold-based inexact matching with second order Sobolev metrics.
result Shows improved shape analysis of mosquito wings.
Algorithm identifies optimal stable matching in uncertain two-sided markets.
problem Sequential learning in two-sided markets with unknown preferences.
method Pure exploration approach with elimination-based algorithms exploiting partial preference information.
result Identification of pervasive stable matching for optimal stable matching identification.
Matched Machine Learning combines machine learning and matching for causal inference.
problem Non-interpretable methods for causal inference.
method Combines machine learning and matching for interpretable causal inference.
result Performs as well as black-box machine learning methods and better than existing matching methods.