b'Hamed Hassani'

Model-Based Robust Deep Learning

While deep learning has resulted in major breakthroughs in many applicat...
05/20/2020 ∙ by Alexander Robey, et al. ∙ 81 ∙ share

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Precise Tradeoffs in Adversarial Training for Linear Regression

Despite breakthrough performance, modern learning models are known to be...
02/24/2020 ∙ by Adel Javanmard, et al. ∙ 0 ∙ share

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Quantized Push-sum for Gossip and Decentralized Optimization over Directed Graphs

We consider a decentralized stochastic learning problem where data point...
02/23/2020 ∙ by Hossein Taheri, et al. ∙ 0 ∙ share

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Age of Information in Random Access Channels

In applications of remote sensing, estimation, and control, timely commu...
12/03/2019 ∙ by Xingran Chen, et al. ∙ 0 ∙ share

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Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback

In this paper, we propose three online algorithms for submodular maximis...
10/28/2019 ∙ by Mingrui Zhang, et al. ∙ 16 ∙ share

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Learning Q-network for Active Information Acquisition

In this paper, we propose a novel Reinforcement Learning approach for so...
10/23/2019 ∙ by Heejin Jeong, et al. ∙ 28 ∙ share

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One Sample Stochastic Frank-Wolfe

One of the beauties of the projected gradient descent method lies in its...
10/10/2019 ∙ by Mingrui Zhang, et al. ∙ 7 ∙ share

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Optimal Algorithms for Submodular Maximization with Distributed Constraints

We consider a class of discrete optimization problems that aim to maximi...
09/30/2019 ∙ by Alexander Robey, et al. ∙ 22 ∙ share

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FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization

Federated learning is a new distributed machine learning approach, where...
09/28/2019 ∙ by Amirhossein Reisizadeh, et al. ∙ 18 ∙ share

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Robust and Communication-Efficient Collaborative Learning

We consider a decentralized learning problem, where a set of computing n...
07/24/2019 ∙ by Amirhossein Reisizadeh, et al. ∙ 0 ∙ share

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Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks

Tight estimation of the Lipschitz constant for deep neural networks (DNN...
06/12/2019 ∙ by Mahyar Fazlyab, et al. ∙ 9 ∙ share

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Stochastic Conditional Gradient++

In this paper, we develop Stochastic Continuous Greedy++ (SCG++), the fi...
02/19/2019 ∙ by Hamed Hassani, et al. ∙ 0 ∙ share

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Quantized Frank-Wolfe: Communication-Efficient Distributed Optimization

How can we efficiently mitigate the overhead of gradient communications ...
02/17/2019 ∙ by Mingrui Zhang, et al. ∙ 0 ∙ share

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Black Box Submodular Maximization: Discrete and Continuous Settings

In this paper, we consider the problem of black box continuous submodula...
01/28/2019 ∙ by Lin Chen, et al. ∙ 6 ∙ share

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Channel Coding at Low Capacity

Low-capacity scenarios have become increasingly important in the technol...
11/10/2018 ∙ by Mohammad Fereydounian, et al. ∙ 0 ∙ share

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SPECTRE: Seedless Network Alignment via Spectral Centralities

Network alignment consists of finding a correspondence between the nodes...
11/02/2018 ∙ by Mikhail Hayhoe, et al. ∙ 0 ∙ share

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Latency-Reliability Tradeoffs for State Estimation

The emerging interest in low-latency high-reliability applications, such...
10/28/2018 ∙ by Konstantinos Gatsis, et al. ∙ 0 ∙ share

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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...
10/09/2018 ∙ by Yogesh Balaji, et al. ∙ 10 ∙ share

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Discrete Sampling using Semigradient-based Product Mixtures

We consider the problem of inference in discrete probabilistic models, t...
07/04/2018 ∙ by Alkis Gotovos, et al. ∙ 0 ∙ share

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Quantized Decentralized Consensus Optimization

We consider the problem of decentralized consensus optimization, where t...
06/29/2018 ∙ by Amirhossein Reisizadeh, et al. ∙ 0 ∙ share

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Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization

This paper considers stochastic optimization problems for a large class ...
04/24/2018 ∙ by Aryan Mokhtari, et al. ∙ 0 ∙ share

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Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity

Online optimization has been a successful framework for solving large-sc...
02/22/2018 ∙ by Lin Chen, et al. ∙ 0 ∙ share

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Online Continuous Submodular Maximization

In this paper, we consider an online optimization process, where the obj...
02/16/2018 ∙ by Lin Chen, et al. ∙ 0 ∙ share

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Almost Optimal Scaling of Reed-Muller Codes on BEC and BSC Channels

Consider a binary linear code of length N, minimum distance d_min, trans...
01/29/2018 ∙ by Hamed Hassani, et al. ∙ 0 ∙ share

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Stochastic Submodular Maximization: The Case of Coverage Functions

Stochastic optimization of continuous objectives is at the heart of mode...
11/05/2017 ∙ by Mohammad Reza Karimi, et al. ∙ 0 ∙ share

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Time-Invariant LDPC Convolutional Codes

Spatially coupled codes have been shown to universally achieve the capac...
02/15/2017 ∙ by Dimitris Achlioptas, et al. ∙ 0 ∙ share

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Near-Optimal Active Learning of Halfspaces via Query Synthesis in the Noisy Setting

In this paper, we consider the problem of actively learning a linear cla...
03/11/2016 ∙ by Lin Chen, et al. ∙ 0 ∙ share

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Hamed Hassani

Featured Co-authors

Wei Liu

Andreas Krause

Rama Chellappa

Lin Chen

Stefanie Jegelka

Amin Karbasi

Ali Jadbabaie

George J. Pappas

Soheil Feizi

Mario Lucic

Mahdi Soltanolkotabi

Model-Based Robust Deep Learning

Precise Tradeoffs in Adversarial Training for Linear Regression

Quantized Push-sum for Gossip and Decentralized Optimization over Directed Graphs

Age of Information in Random Access Channels

Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback

Learning Q-network for Active Information Acquisition

One Sample Stochastic Frank-Wolfe

Optimal Algorithms for Submodular Maximization with Distributed Constraints

FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization

Robust and Communication-Efficient Collaborative Learning

Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks

Stochastic Conditional Gradient++

Quantized Frank-Wolfe: Communication-Efficient Distributed Optimization

Black Box Submodular Maximization: Discrete and Continuous Settings

Channel Coding at Low Capacity

SPECTRE: Seedless Network Alignment via Spectral Centralities

Latency-Reliability Tradeoffs for State Estimation

Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs

Discrete Sampling using Semigradient-based Product Mixtures

Quantized Decentralized Consensus Optimization

Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization

Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity

Online Continuous Submodular Maximization

Almost Optimal Scaling of Reed-Muller Codes on BEC and BSC Channels

Stochastic Submodular Maximization: The Case of Coverage Functions

Time-Invariant LDPC Convolutional Codes

Near-Optimal Active Learning of Halfspaces via Query Synthesis in the Noisy Setting