26 citations · 35 across the 10 of their papers we have counts for
14 papers
Generalization Properties of Adversarial Training for -Bounded Adversarial Attacks
Payam Delgosha, Hamed Hassani, Ramtin Pedarsani
We have widely observed that neural networks are vulnerable to small additive perturbations to the input causing misclassification. In this paper, we focus on the -bounded…
A Universal Low Complexity Compression Algorithm for Sparse Marked Graphs
Payam Delgosha, Venkat Anantharam
Many modern applications involve accessing and processing graphical data, i.e. data that is naturally indexed by graphs. Examples come from internet graphs, social networks, genomi…
Binary Classification Under Attacks for General Noise Distribution
Payam Delgosha, Hamed Hassani, Ramtin Pedarsani
Adversarial examples have recently drawn considerable attention in the field of machine learning due to the fact that small perturbations in the data can result in major performanc…
Efficient and Robust Classification for Sparse Attacks
Mark Beliaev, Payam Delgosha, Hamed Hassani +1
In the past two decades we have seen the popularity of neural networks increase in conjunction with their classification accuracy. Parallel to this, we have also witnessed how frag…
A Universal Lossless Compression Method applicable to Sparse Graphs and Heavy-Tailed Sparse Graphs
Payam Delgosha, Venkat Anantharam
Graphical data arises naturally in several modern applications, including but not limited to internet graphs, social networks, genomics and proteomics. The typically large size of…
Robust Classification Under Attack for the Gaussian Mixture Model
Payam Delgosha, Hamed Hassani, Ramtin Pedarsani
It is well-known that machine learning models are vulnerable to small but cleverly-designed adversarial perturbations that can cause misclassification. While there has been major p…
Universal Lossless Compression of Graphical Data
Payam Delgosha, Venkat Anantharam
Graphical data is comprised of a graph with marks on its edges and vertices. The mark indicates the value of some attribute associated to the respective edge or vertex. Examples of…
A Notion of Entropy for Stochastic Processes on Marked Rooted Graphs
Payam Delgosha, Venkat Anantharam
In this document, we introduce a notion of entropy for stochastic processes on marked rooted graphs. For this, we employ the framework of local weak limit theory for sparse marked…
Deep Switch Networks for Generating Discrete Data and Language
Payam Delgosha, Naveen Goela
Multilayer switch networks are proposed as artificial generators of high-dimensional discrete data (e.g., binary vectors, categorical data, natural language, network log files, and…
Distributed Compression of Graphical Data
Payam Delgosha, Venkat Anantharam
In contrast to time series, graphical data is data indexed by the vertices and edges of a graph. Modern applications such as the internet, social networks, genomics and proteomics…
Load Balancing in Hypergraphs
Payam Delgosha, Venkat Anantharam
Consider a simple locally finite hypergraph on a countable vertex set, where each edge represents one unit of load which should be distributed among the vertices defining the edge.…
High Probability Guarantees in Repeated Games: Theory and Applications in Information Theory
Payam Delgosha, Amin Gohari, Mohammad Akbarpour
We introduce a "high probability" framework for repeated games with incomplete information. In our non-equilibrium setting, players aim to guarantee a certain payoff with high prob…
Impossibility of Local State Transformation via Hypercontractivity
Payam Delgosha, Salman Beigi
Local state transformation is the problem of transforming an arbitrary number of copies of a bipartite resource state to a bipartite target state under local operations. That is, g…
Information Theoretic cutting of a cake
Payam Delgosha, Amin Gohari
Cutting a cake is a metaphor for the problem of dividing a resource (cake) among several agents. The problem becomes non-trivial when the agents have different valuations for diffe…