activity
20172020
most citedLearning Hawkes Processes from a Handful of Events

12 citations · 26 across the 3 of their papers we have counts for

collaborators

7 papers

cs.LG2020

Generalization Comparison of Deep Neural Networks via Output Sensitivity

Mahsa Forouzesh, Farnood Salehi, Patrick Thiran

Although recent works have brought some insights into the performance improvement of techniques used in state-of-the-art deep-learning models, more work is needed to understand the…

cs.LG2019★ 12 cited

Learning Hawkes Processes from a Handful of Events

Farnood Salehi, William Trouleau, Matthias Grossglauser +1

Learning the causal-interaction network of multivariate Hawkes processes is a useful task in many applications. Maximum-likelihood estimation is the most common approach to solve t…

stat.ML2019★ 3 cited

Augmenting and Tuning Knowledge Graph Embeddings

Robert Bamler, Farnood Salehi, Stephan Mandt

Knowledge graph embeddings rank among the most successful methods for link prediction in knowledge graphs, i.e., the task of completing an incomplete collection of relational facts…

cs.LG2018

An Algorithmic Framework to Control Bias in Bandit-based Personalization

L. Elisa Celis, Sayash Kapoor, Farnood Salehi +1

Personalization is pervasive in the online space as it leads to higher efficiency and revenue by allowing the most relevant content to be served to each user. However, recent studi…

cs.LG2017

Coordinate Descent with Bandit Sampling

Farnood Salehi, Patrick Thiran, L. Elisa Celis

Coordinate descent methods usually minimize a cost function by updating a random decision variable (corresponding to one coordinate) at a time. Ideally, we would update the decisio…

cs.LG2017★ 11 cited

Stochastic Optimization with Bandit Sampling

Farnood Salehi, L. Elisa Celis, Patrick Thiran

Many stochastic optimization algorithms work by estimating the gradient of the cost function on the fly by sampling datapoints uniformly at random from a training set. However, the…