arXiv research

Study dynamic matching in heterogeneous networks using ODE model.

CR-UOT improves matching of heterogeneous single-cell omics profiles.

Matching two different sets of items, called heterogeneous set-to-set matching problem, has recently received attention as a promising problem. The difficulties are to extract features to match a correct pair of different sets and also preserve two types of exchangeability required for set-to-set matching: the pair of …

The paper introduces heterogeneous manifolds for better graph embeddings.

Paper develops efficient recursive learning for multi-channel systems with heterogeneous dynamics.

When pre-processing observational data via matching, we seek to approximate each unit with maximally similar peers that had an alternative treatment status--essentially replicating a randomized block design. However, as one considers a growing number of continuous features, a curse of dimensionality applies making asym…

Framework improves target domain prediction using quantile matching.

New methods improve tool-to-tool matching in semiconductor manufacturing.

New bounds for LDP with heterogeneous privacy levels guaranteeing high probability of accuracy.

To compare entities of differing types and structural components, the artificial neural network paradigm was used to cross-compare structural components between heterogeneous documents. Trainable weighted structural components were input into machine-learned activation functions of the neurons. The model was used for m…

We consider the problem faced by a service platform that needs to match limited supply with demand but also to learn the attributes of new users in order to match them better in the future. We introduce a benchmark model with heterogeneous "workers" (demand) and a limited supply of "jobs" that arrive over time. Job typ…

Information systems have widely been the target of malware attacks. Traditional signature-based malicious program detection algorithms can only detect known malware and are prone to evasion techniques such as binary obfuscation, while behavior-based approaches highly rely on the malware training samples and incur prohi…

Single global merging boosts decentralized learning performance.

Proposes a new method to handle data heterogeneity in causal inference.

Flexible inference model for multilayer networks with heterogeneous data.

We propose the Heterogeneous Thurstone Model (HTM) for aggregating ranked data, which can take the accuracy levels of different users into account. By allowing different noise distributions, the proposed HTM model maintains the generality of Thurstone's original framework, and as such, also extends the Bradley-Terry-Lu…

Rescaled ASGD optimizes distributed learning under heterogeneous data.

New framework tackles stochastic latent subgroup heterogeneity in online decision-making.

Proposes flexible spatial models for better understanding spatial heterogeneity.

The problem of identifying geometric structure in heterogeneous, high-dimensional data is a cornerstone of representation learning. While there exists a large body of literature on the embeddability of canonical graphs, such as lattices or trees, the heterogeneity of the relational data typically encountered in practic…

This paper characterizes the equilibrium in a continuous time financial market populated by heterogeneous agents who differ in their rate of relative risk aversion and face convex portfolio constraints. The model is studied in an application to margin constraints and found to match real world observations about financi…

New federated learning protocols improve on knowledge distillation's poor performance.

FedForest adapts RF for federated learning, improving performance and efficiency.

QR-learner estimates individual treatment effects using external data.

Federated UCBVI reduces communication costs while minimizing regret in multi-agent settings.

New method for clustering tasks with heterogeneous data.

New algorithms select and rank features from MTS without feature extraction.

Unified analysis of federated learning with compression for various data distributions.

This paper studies generalization in machine learning with mixture data.

Study tackles contamination and heterogeneity in multi-task learning, improving robustness and personalization.

Proposes CDTD, a diffusion model for mixed-type tabular data.

New method tightens federated probe-logit distillation rates under varying bandwidths.

Method identifies unknown intervention targets in structural causal models from diverse data.

A new sliced IGW distance for Gromov-Wasserstein alignment.

A novel federated learning framework resolves structural misalignment in model fusion.

Many scientific and engineering challenges -- ranging from personalized medicine to customized marketing recommendations -- require an understanding of treatment effect heterogeneity. In this paper, we develop a non-parametric causal forest for estimating heterogeneous treatment effects that extends Breiman's widely us…

Many scientific and engineering challenges -- ranging from pharmacokinetic drug dosage allocation and personalized medicine to marketing mix (4Ps) recommendations -- require an understanding of the unobserved heterogeneity in order to develop the best decision making-processes. In this paper, we develop a hypothesis te…

Heterogeneous domain adaptation (HDA) aims to facilitate the learning task in a target domain by borrowing knowledge from a heterogeneous source domain. In this paper, we propose a Soft Transfer Network (STN), which jointly learns a domain-shared classifier and a domain-invariant subspace in an end-to-end manner, for a…

MicAugment transfers audio style from few seconds of input to match target conditions.

Study optimizes data collection from biased, costly sources to minimize risk.

New algorithms reduce matching market regret to log(T) with improved stability.

Paper introduces Functional Effects Models to account for individual heterogeneity in panel data.

The statistical description and modeling of volatility plays a prominent role in econometrics, risk management and finance. GARCH and stochastic volatility models have been extensively studied and are routinely fitted to market data, albeit providing a phenomenological description only. In contrast, the field of econop…

Method uses pseudo-samples to improve RCT data in ride-hailing pricing studies.

We consider a simple market where a vendor offers multiple variants of a certain product and preferences of both the vendor and potential buyers are heterogeneous and possibly even antagonistic. Optimization of the joint benefit of the vendor and the buyers turns the toy market into a combinatorial matching problem. We…

Paper proposes SCALLION and SCAFCOM for compressed FL with reduced communication.

Develops locally private methods for nonparametric contextual bandits.

A new algorithm for competing agents in a two-sided market setting.