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Found 11 papers, 0 papers with code
An ODE Model for Dynamic Matching in Heterogeneous Networks
We study the problem of dynamic matching in heterogeneous networks, where agents are subject to compatibility restrictions and stochastic arrival and departure times.
Robust multi-item auction design using statistical learning: Overcoming uncertainty in bidders' types distributions
This paper presents a novel mechanism design for multi-item auction settings with uncertain bidders' type distributions.
Double Matching Under Complementary Preferences
In this paper, we propose a new algorithm for addressing the problem of matching markets with complementary preferences, where agents' preferences are unknown a priori and must be learned from data.
Incentive-Aware Recommender Systems in Two-Sided Markets
Online platforms in the Internet Economy commonly incorporate recommender systems that recommend arms (e. g., products) to agents (e. g., users).
On Large Batch Training and Sharp Minima: A Fokker-Planck Perspective
We study the statistical properties of the dynamic trajectory of stochastic gradient descent (SGD).
Post-Regularization Confidence Bands for Ordinary Differential Equations
In this article, we construct post-regularization confidence band for individual regulatory function in ODE with unknown functionals and noisy data observations.
Orthogonalized Kernel Debiased Machine Learning for Multimodal Data Analysis
Our proposal enjoys, to a good extent, both model interpretability and model flexibility.
A resampling approach for causal inference on novel two-point time-series with application to identify risk factors for type-2 diabetes and cardiovascular disease
Baseline data contain a pre-intervention health record of study participants, and health data after LCD intervention are recorded at the follow-up visit, providing a two-point time-series pattern without a parallel control group.
Methodology Applications
Learning in Multi-Stage Decentralized Matching Markets
Matching markets are often organized in a multi-stage and decentralized manner.
Learning Strategies in Decentralized Matching Markets under Uncertain Preferences
We study the problem of decision-making in the setting of a scarcity of shared resources when the preferences of agents are unknown a priori and must be learned from data.
Towards Theoretical Understanding of Large Batch Training in Stochastic Gradient Descent
In particular, we give an explicit escaping time of SGD from a local minimum in the finite-time regime and prove that SGD tends to converge to flatter minima in the asymptotic regime (although may take exponential time to converge) regardless of the batch size.
