research
∙
05/20/2020
Model-Based Robust Deep Learning
While deep learning has resulted in major breakthroughs in many applicat...
share
research
∙
02/24/2020
Precise Tradeoffs in Adversarial Training for Linear Regression
Despite breakthrough performance, modern learning models are known to be...
research
∙
02/23/2020
Quantized Push-sum for Gossip and Decentralized Optimization over Directed Graphs
We consider a decentralized stochastic learning problem where data point...
research
∙
12/03/2019
Age of Information in Random Access Channels
In applications of remote sensing, estimation, and control, timely commu...
research
∙
10/28/2019
Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback
In this paper, we propose three online algorithms for submodular maximis...
research
∙
10/23/2019
Learning Q-network for Active Information Acquisition
In this paper, we propose a novel Reinforcement Learning approach for so...
research
∙
10/10/2019
One Sample Stochastic Frank-Wolfe
One of the beauties of the projected gradient descent method lies in its...
research
∙
09/30/2019
Optimal Algorithms for Submodular Maximization with Distributed Constraints
We consider a class of discrete optimization problems that aim to maximi...
research
∙
09/28/2019
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization
Federated learning is a new distributed machine learning approach, where...
research
∙
07/24/2019
Robust and Communication-Efficient Collaborative Learning
We consider a decentralized learning problem, where a set of computing n...
research
∙
06/12/2019
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks
Tight estimation of the Lipschitz constant for deep neural networks (DNN...
research
∙
02/19/2019
Stochastic Conditional Gradient++
In this paper, we develop Stochastic Continuous Greedy++ (SCG++), the fi...
research
∙
02/17/2019
Quantized Frank-Wolfe: Communication-Efficient Distributed Optimization
How can we efficiently mitigate the overhead of gradient communications ...
research
∙
01/28/2019
Black Box Submodular Maximization: Discrete and Continuous Settings
In this paper, we consider the problem of black box continuous submodula...
research
∙
11/10/2018
Channel Coding at Low Capacity
Low-capacity scenarios have become increasingly important in the technol...
research
∙
11/02/2018
SPECTRE: Seedless Network Alignment via Spectral Centralities
Network alignment consists of finding a correspondence between the nodes...
research
∙
10/28/2018
Latency-Reliability Tradeoffs for State Estimation
The emerging interest in low-latency high-reliability applications, such...
research
∙
10/09/2018
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...
research
∙
07/04/2018
Discrete Sampling using Semigradient-based Product Mixtures
We consider the problem of inference in discrete probabilistic models, t...
research
∙
06/29/2018
Quantized Decentralized Consensus Optimization
We consider the problem of decentralized consensus optimization, where t...
research
∙
04/24/2018
Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization
This paper considers stochastic optimization problems for a large class ...
research
∙
02/22/2018
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity
Online optimization has been a successful framework for solving large-sc...
research
∙
02/16/2018
Online Continuous Submodular Maximization
In this paper, we consider an online optimization process, where the obj...
research
∙
01/29/2018
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...
research
∙
11/05/2017
Stochastic Submodular Maximization: The Case of Coverage Functions
Stochastic optimization of continuous objectives is at the heart of mode...
research
∙
02/15/2017
Time-Invariant LDPC Convolutional Codes
Spatially coupled codes have been shown to universally achieve the capac...
research
∙
03/11/2016