
ModelBased Robust Deep Learning
While deep learning has resulted in major breakthroughs in many applicat...
read it

Learning Qnetwork for Active Information Acquisition
In this paper, we propose a novel Reinforcement Learning approach for so...
read it

Optimal Algorithms for Submodular Maximization with Distributed Constraints
We consider a class of discrete optimization problems that aim to maximi...
read it

FedPAQ: A CommunicationEfficient Federated Learning Method with Periodic Averaging and Quantization
Federated learning is a new distributed machine learning approach, where...
read it

Online Continuous Submodular Maximization: From FullInformation to Bandit Feedback
In this paper, we propose three online algorithms for submodular maximis...
read it

Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs
Building on the success of deep learning, two modern approaches to learn...
read it

Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks
Tight estimation of the Lipschitz constant for deep neural networks (DNN...
read it

One Sample Stochastic FrankWolfe
One of the beauties of the projected gradient descent method lies in its...
read it

Black Box Submodular Maximization: Discrete and Continuous Settings
In this paper, we consider the problem of black box continuous submodula...
read it

TimeInvariant LDPC Convolutional Codes
Spatially coupled codes have been shown to universally achieve the capac...
read it

NearOptimal Active Learning of Halfspaces via Query Synthesis in the Noisy Setting
In this paper, we consider the problem of actively learning a linear cla...
read it

ProjectionFree Online Optimization with Stochastic Gradient: From Convexity to Submodularity
Online optimization has been a successful framework for solving largesc...
read it

Almost Optimal Scaling of ReedMuller Codes on BEC and BSC Channels
Consider a binary linear code of length N, minimum distance d_min, trans...
read it

Stochastic Submodular Maximization: The Case of Coverage Functions
Stochastic optimization of continuous objectives is at the heart of mode...
read it

Online Continuous Submodular Maximization
In this paper, we consider an online optimization process, where the obj...
read it

Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization
This paper considers stochastic optimization problems for a large class ...
read it

Discrete Sampling using Semigradientbased Product Mixtures
We consider the problem of inference in discrete probabilistic models, t...
read it

Quantized Decentralized Consensus Optimization
We consider the problem of decentralized consensus optimization, where t...
read it

LatencyReliability Tradeoffs for State Estimation
The emerging interest in lowlatency highreliability applications, such...
read it

SPECTRE: Seedless Network Alignment via Spectral Centralities
Network alignment consists of finding a correspondence between the nodes...
read it

Channel Coding at Low Capacity
Lowcapacity scenarios have become increasingly important in the technol...
read it

Stochastic Conditional Gradient++
In this paper, we develop Stochastic Continuous Greedy++ (SCG++), the fi...
read it

Quantized FrankWolfe: CommunicationEfficient Distributed Optimization
How can we efficiently mitigate the overhead of gradient communications ...
read it

Robust and CommunicationEfficient Collaborative Learning
We consider a decentralized learning problem, where a set of computing n...
read it

Age of Information in Random Access Channels
In applications of remote sensing, estimation, and control, timely commu...
read it

Quantized Pushsum for Gossip and Decentralized Optimization over Directed Graphs
We consider a decentralized stochastic learning problem where data point...
read it

Precise Tradeoffs in Adversarial Training for Linear Regression
Despite breakthrough performance, modern learning models are known to be...
read it
Hamed Hassani
is this you? claim profile