44 citations · 93 across the 5 of their papers we have counts for
10 papers
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries
Arjun Nitin Bhagoji, Daniel Cullina, Vikash Sehwag +1
Understanding the fundamental limits of robust supervised learning has emerged as a problem of immense interest, from both practical and theoretical standpoints. In particular, it…
SSD: A Unified Framework for Self-Supervised Outlier Detection
Vikash Sehwag, Mung Chiang, Prateek Mittal
We ask the following question: what training information is required to design an effective outlier/out-of-distribution (OOD) detector, i.e., detecting samples that lie far away fr…
RobustBench: a standardized adversarial robustness benchmark
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag +5
As a research community, we are still lacking a systematic understanding of the progress on adversarial robustness which often makes it hard to identify the most promising ideas in…
A Critical Evaluation of Open-World Machine Learning
Liwei Song, Vikash Sehwag, Arjun Nitin Bhagoji +1
Open-world machine learning (ML) combines closed-world models trained on in-distribution data with out-of-distribution (OOD) detectors, which aim to detect and reject OOD inputs. P…
Fast-Convergent Federated Learning
Hung T. Nguyen, Vikash Sehwag, Seyyedali Hosseinalipour +3
Federated learning has emerged recently as a promising solution for distributing machine learning tasks through modern networks of mobile devices. Recent studies have obtained lowe…
Time for a Background Check! Uncovering the impact of Background Features on Deep Neural Networks
Vikash Sehwag, Rajvardhan Oak, Mung Chiang +1
With increasing expressive power, deep neural networks have significantly improved the state-of-the-art on image classification datasets, such as ImageNet. In this paper, we invest…