research
∙
07/10/2023
On the power of graph neural networks and the role of the activation function
In this article we present new results about the expressivity of Graph N...
share
research
∙
07/22/2022
Globally optimal and scalable N-way matching of astronomy catalogs
Building on previous Bayesian approaches, we introduce a novel formulati...
research
∙
11/15/2021
Neural networks with linear threshold activations: structure and algorithms
In this article we present new results on neural networks with linear th...
research
∙
11/09/2021
Helly systems and certificates in optimization
Inspired by branch-and-bound and cutting plane proofs in mixed-integer o...
research
∙
10/12/2021
Complexity of optimizing over the integers
In the first part of this paper, we present a unified framework for anal...
research
∙
06/23/2021
Leveraging semantically similar queries for ranking via combining representations
In modern ranking problems, different and disparate representations of t...
research
∙
05/31/2021
Towards Lower Bounds on the Depth of ReLU Neural Networks
We contribute to a better understanding of the class of functions that i...
research
∙
11/11/2020
Complexity of branch-and-bound and cutting planes in mixed-integer optimization – II
We study the complexity of cutting planes and branching schemes from a t...
research
∙
05/20/2020
Learning to rank via combining representations
Learning to rank – producing a ranked list of items specific to a query ...
research
∙
03/10/2020
Complexity of cutting planes and branch-and-bound in mixed-integer optimization
We investigate the theoretical complexity of branch-and-bound (BB) and c...
research
∙
01/21/2019
Admissibility of solution estimators for stochastic optimization
We look at stochastic optimization problems through the lens of statisti...
research
∙
07/18/2018
Convergence guarantees for RMSProp and ADAM in non-convex optimization and their comparison to Nesterov acceleration on autoencoders
RMSProp and ADAM continue to be extremely popular algorithms for trainin...
research
∙
11/08/2017
Lower bounds over Boolean inputs for deep neural networks with ReLU gates
Motivated by the resurgence of neural networks in being able to solve co...
research
∙
08/12/2017
Sparse Coding and Autoencoders
In "Dictionary Learning" one tries to recover incoherent matrices A^* ∈R...
research
∙
11/04/2016
