Timothy A Mann — accepted AI-conference papers

2022

Defending Against Image Corruptions Through Adversarial Augmentations

Dan Andrei Calian, Florian Stimberg, Olivia Wiles, Sylvestre-Alvise Rebuffi, András György, Timothy A Mann +1

ICLR 2022poster

Modern neural networks excel at image classification, yet they remain vulnerable to common image corruptions such as blur, speckle noise or fog. Recent methods that focus on this problem, such as AugMix and DeepAugment, introduce defenses that operate in expectation over a distribution of image corr…

Cited by 54 Source ↗PDF ↗Scholar ↗

2019

Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates

Carlos Riquelme, Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy A Mann +2

NeurIPS 2019poster

We consider the core reinforcement-learning problem of on-policy value function approximation from a batch of trajectory data, and focus on various issues of Temporal Difference (TD) learning and Monte Carlo (MC) policy evaluation. The two methods are known to achieve complementary bias-variance tra…

Cited by 10 Source ↗PDF ↗Scholar ↗

2016

Adaptive Skills Adaptive Partitions (ASAP)

Daniel J Mankowitz, Timothy A Mann, Shie Mannor

NeurIPS 2016poster

We introduce the Adaptive Skills, Adaptive Partitions (ASAP) framework that (1) learns skills (i.e., temporally extended actions or options) as well as (2) where to apply them. We believe that both (1) and (2) are necessary for a truly general skill learning framework, which is a key building block…

Cited by 73 Source ↗PDF ↗code ↗Scholar ↗