activity
20162022
most citedWasserstein Adversarial Imitation Learning

35 citations · 54 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AI20204 cited

Non-cooperative Multi-agent Systems with Exploring Agents

Jalal Etesami, Christoph-Nikolas Straehle

Multi-agent learning is a challenging problem in machine learning that has applications in different domains such as distributed control, robotics, and economics. We develop a pres…

cs.AI20202 cited

Causal Transfer for Imitation Learning and Decision Making under Sensor-shift

Jalal Etesami, Philipp Geiger

Learning from demonstrations (LfD) is an efficient paradigm to train AI agents. But major issues arise when there are differences between (a) the demonstrator's own sensory input,…

cs.LG201935 cited

Wasserstein Adversarial Imitation Learning

Huang Xiao, Michael Herman, Joerg Wagner +3

Imitation Learning describes the problem of recovering an expert policy from demonstrations. While inverse reinforcement learning approaches are known to be very sample-efficient i…

stat.ML20185 cited

Nonparametric Hawkes Processes: Online Estimation and Generalization Bounds

Yingxiang Yang, Jalal Etesami, Niao He +1

In this paper, we design a nonparametric online algorithm for estimating the triggering functions of multivariate Hawkes processes. Unlike parametric estimation, where evolutionary…

stat.ML20172 cited

A New Measure of Conditional Dependence

Jalal Etesami, Kun Zhang, Negar Kiyavash

Measuring conditional dependencies among the variables of a network is of great interest to many disciplines. This paper studies some shortcomings of the existing dependency measur…

cs.CR2016

On the Vulnerability of Digital Fingerprinting Systems to Finite Alphabet Collusion Attacks

Jalal Etesami, Negar Kiyavash

This paper proposes a novel, non-linear collusion attack on digital fingerprinting systems. The attack is proposed for fingerprinting systems with finite alphabet but can be extend…