2 citations · 5 across the 8 of their papers we have counts for
8 papers
Equilibrium and Pricing in Consumer Networks with Nonlinear Utilities: An Online Shape-Constrained Learning Approach
Daniele Bracale, George Michailidis
We study optimal monopoly pricing over consumer networks governed by general nonlinear utilities. In our framework, a consumer's utility is jointly determined by an individualized…
Online Price Competition under Generalized Linear Demands
Daniele Bracale, Moulinath Banerjee, Cong Shi +1
We study a sequential price competition among sellers, each influenced by the pricing decisions of their rivals. Specifically, the demand function for each seller follows t…
Revenue Maximization Under Sequential Price Competition Via The Estimation Of s-Concave Demand Functions
Daniele Bracale, Moulinath Banerjee, Cong Shi +1
We consider price competition among multiple sellers over a selling horizon of periods. In each period, sellers simultaneously offer their prices (which are made public) and su…
Dynamic Pricing in the Linear Valuation Model using Shape Constraints
Daniele Bracale, Moulinath Banerjee, Yuekai Sun +2
We propose a shape-constrained approach to dynamic pricing for censored data in the linear valuation model eliminating the need for tuning parameters commonly required by existing…
Microfoundation Inference for Strategic Prediction
Daniele Bracale, Subha Maity, Felipe Maia Polo +3
Often in prediction tasks, the predictive model itself can influence the distribution of the target variable, a phenomenon termed performative prediction. Generally, this influence…
Learning the Distribution Map in Reverse Causal Performative Prediction
Daniele Bracale, Subha Maity, Moulinath Banerjee +1
In numerous predictive scenarios, the predictive model affects the sampling distribution; for example, job applicants often meticulously craft their resumes to navigate through a s…
Large-width functional asymptotics for deep Gaussian neural networks
Daniele Bracale, Stefano Favaro, Sandra Fortini +1
In this paper, we consider fully connected feed-forward deep neural networks where weights and biases are independent and identically distributed according to Gaussian distribution…
Infinite-channel deep stable convolutional neural networks
Daniele Bracale, Stefano Favaro, Sandra Fortini +1
The interplay between infinite-width neural networks (NNs) and classes of Gaussian processes (GPs) is well known since the seminal work of Neal (1996). While numerous theoretical r…