4.3k citations · 8.6k across the 159 of their papers we have counts for
16 papers · 1 filter
Adversarial Music: Real World Audio Adversary Against Wake-word Detection System
Juncheng B. Li, Shuhui Qu, Xinjian Li +3
Voice Assistants (VAs) such as Amazon Alexa or Google Assistant rely on wake-word detection to respond to people's commands, which could potentially be vulnerable to audio adversar…
AP-Perf: Incorporating Generic Performance Metrics in Differentiable Learning
Rizal Fathony, J. Zico Kolter
We propose a method that enables practitioners to conveniently incorporate custom non-decomposable performance metrics into differentiable learning pipelines, notably those based u…
Dynamic Modeling and Equilibria in Fair Decision Making
Joshua Williams, J. Zico Kolter
Recent studies on fairness in automated decision making systems have both investigated the potential future impact of these decisions on the population at large, and emphasized tha…
Differentiable Convex Optimization Layers
Akshay Agrawal, Brandon Amos, Shane Barratt +3
Recent work has shown how to embed differentiable optimization problems (that is, problems whose solutions can be backpropagated through) as layers within deep learning architectur…
Black-box Adversarial Attacks with Bayesian Optimization
Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott +1
We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples using information limited to loss function evaluations of input-output p…
Adversarial Robustness Against the Union of Multiple Perturbation Models
Pratyush Maini, Eric Wong, J. Zico Kolter
Owing to the susceptibility of deep learning systems to adversarial attacks, there has been a great deal of work in developing (both empirically and certifiably) robust classifiers…
Perceptual Based Adversarial Audio Attacks
Joseph Szurley, J. Zico Kolter
Recent work has shown the possibility of adversarial attacks on automatic speechrecognition (ASR) systems. However, in the vast majority of work in this area, theattacks have been…
The Limited Multi-Label Projection Layer
Brandon Amos, Vladlen Koltun, J. Zico Kolter
We propose the Limited Multi-Label (LML) projection layer as a new primitive operation for end-to-end learning systems. The LML layer provides a probabilistic way of modeling multi…
Multimodal Transformer for Unaligned Multimodal Language Sequences
Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang +3
Human language is often multimodal, which comprehends a mixture of natural language, facial gestures, and acoustic behaviors. However, two major challenges in modeling such multimo…
Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience
Vaishnavh Nagarajan, J. Zico Kolter
The ability of overparameterized deep networks to generalize well has been linked to the fact that stochastic gradient descent (SGD) finds solutions that lie in flat, wide minima i…
Adversarial camera stickers: A physical camera-based attack on deep learning systems
Juncheng Li, Frank R. Schmidt, J. Zico Kolter
Recent work has documented the susceptibility of deep learning systems to adversarial examples, but most such attacks directly manipulate the digital input to a classifier. Althoug…
Large Scale Learning of Agent Rationality in Two-Player Zero-Sum Games
Chun Kai Ling, Fei Fang, J. Zico Kolter
With the recent advances in solving large, zero-sum extensive form games, there is a growing interest in the inverse problem of inferring underlying game parameters given only acce…
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
Eric Wong, Frank R. Schmidt, J. Zico Kolter
A rapidly growing area of work has studied the existence of adversarial examples, datapoints which have been perturbed to fool a classifier, but the vast majority of these works ha…
Uniform convergence may be unable to explain generalization in deep learning
Vaishnavh Nagarajan, J. Zico Kolter
Aimed at explaining the surprisingly good generalization behavior of overparameterized deep networks, recent works have developed a variety of generalization bounds for deep learni…
Certified Adversarial Robustness via Randomized Smoothing
Jeremy M Cohen, Elan Rosenfeld, J. Zico Kolter
We show how to turn any classifier that classifies well under Gaussian noise into a new classifier that is certifiably robust to adversarial perturbations under the norm.…
Generalization in Deep Networks: The Role of Distance from Initialization
Vaishnavh Nagarajan, J. Zico Kolter
Why does training deep neural networks using stochastic gradient descent (SGD) result in a generalization error that does not worsen with the number of parameters in the network? T…