Alexander Levine | DeepAI

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∙ 11/18/2022

Invariant Learning via Diffusion Dreamed Distribution Shifts

Though the background is an important signal for image classification, o...

0 Priyatham Kattakinda, et al. ∙

∙ 08/28/2022

Goal-Conditioned Q-Learning as Knowledge Distillation

Many applications of reinforcement learning can be formalized as goal-co...

0 Alexander Levine, et al. ∙

∙ 03/16/2022

Provable Adversarial Robustness for Fractional Lp Threat Models

In recent years, researchers have extensively studied adversarial robust...

0 Alexander Levine, et al. ∙

∙ 01/28/2022

Certifying Model Accuracy under Distribution Shifts

Certified robustness in machine learning has primarily focused on advers...

0 Aounon Kumar, et al. ∙

∙ 12/08/2021

Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection

Object detection plays a key role in many security-critical systems. Adv...

7 Jiang Liu, et al. ∙

∙ 06/21/2021

Policy Smoothing for Provably Robust Reinforcement Learning

The study of provable adversarial robustness for deep neural network (DN...

0 Aounon Kumar, et al. ∙

∙ 03/17/2021

Improved, Deterministic Smoothing for L1 Certified Robustness

Randomized smoothing is a general technique for computing sample-depende...

0 Alexander Levine, et al. ∙

∙ 10/20/2020

Tight Second-Order Certificates for Randomized Smoothing

Randomized smoothing is a popular way of providing robustness guarantees...

0 Alexander Levine, et al. ∙

∙ 09/17/2020

Certifying Confidence via Randomized Smoothing

Randomized smoothing has been shown to provide good certified-robustness...

0 Aounon Kumar, et al. ∙

∙ 09/05/2020

Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks

Adversarial training is a popular defense strategy against attack threat...

10 Wei-An Lin, et al. ∙

∙ 06/26/2020

Deep Partition Aggregation: Provable Defense against General Poisoning Attacks

Adversarial poisoning attacks distort training data in order to corrupt ...

0 Alexander Levine, et al. ∙

∙ 02/25/2020

(De)Randomized Smoothing for Certifiable Defense against Patch Attacks

Patch adversarial attacks on images, in which the attacker can distort p...

6 Alexander Levine, et al. ∙

∙ 02/08/2020

Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness

Randomized smoothing, using just a simple isotropic Gaussian distributio...

0 Aounon Kumar, et al. ∙

∙ 11/21/2019

Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation

Recently, techniques have been developed to provably guarantee the robus...

18 Alexander Levine, et al. ∙

∙ 10/23/2019

Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks

In the last couple of years, several adversarial attack methods based on...

0 Alexander Levine, et al. ∙

∙ 05/28/2019

Certifiably Robust Interpretation in Deep Learning

Although gradient-based saliency maps are popular methods for deep learn...

0 Alexander Levine, et al. ∙

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