Deep neural networks can be unreliable in the real world when the traini...
Training convolutional neural networks (CNNs) with a strict 1-Lipschitz
...
Vector norms play a fundamental role in computer science and optimizatio...
In submodular optimization we often deal with the expected value of a
su...
The well-known Komlós conjecture states that given n vectors in
ℝ^d with...
Deep neural networks can be unreliable in the real world especially when...
We study the problem of solving Packing Integer Programs (PIPs) in the o...
A well-known result of Banaszczyk in discrepancy theory concerns the pre...
The vector-balancing problem is a fundamental problem in discrepancy the...
Babaioff et al. [BIK2007] introduced the matroid secretary problem in 20...
A key reason for the lack of reliability of deep neural networks in the ...
Training convolutional neural networks (CNNs) with a strict Lipschitz
co...
We consider online scheduling to minimize weighted completion time on re...
Training convolutional neural networks with a Lipschitz constraint under...
Adversarial training is one of the most effective defenses against
adver...
We introduce online learning with vector costs () where in each time
ste...
We present a computationally-efficient truthful mechanism for combinator...
Our main contribution is a general framework to design efficient polynom...
In the stochastic online vector balancing problem, vectors
v_1,v_2,…,v_T...
We consider the online carpooling problem: given n vertices, a sequence ...
We present adversarial attacks and defenses for the perceptual adversari...
Deep neural networks are being increasingly used in real world applicati...
A robustness certificate is the minimum distance of a given input to the...
This chapter introduces the random-order model in online algorithms.
In ...
In a classical online decision problem, a decision-maker who is trying t...
We consider an online vector balancing question where T vectors, chosen
...
In this paper we consider the classic matroid intersection problem: give...
In deep neural networks, the spectral norm of the Jacobian of a layer bo...
In classical secretary problems, a sequence of n elements arrive in a
un...
A longstanding open problem in Algorithmic Mechanism Design is to design...
Given a metric (V,d) and a root∈ V, the classic
k-TSP problem is to find...
Consider a unit interval [0,1] in which n points arrive one-by-one
indep...
Although gradient-based saliency maps are popular methods for deep learn...
We consider a revenue-maximizing seller with n items facing a single buy...
We consider the online problem of scheduling jobs on identical machines,...
Suppose there are n Markov chains and we need to pay a per-step
price to...
Consider a kidney-exchange application where we want to find a max-match...
Current methods to interpret deep learning models by generating saliency...
While neural networks have achieved high performance in different learni...
In this paper we study how to optimally balance cheap inflexible resourc...
Online contention resolution schemes (OCRSs) were proposed by Feldman,
S...
Consider a network design application where we wish to lay down a
minimu...
The secretary and the prophet inequality problems are central to the fie...