Publications related to Multi-armed bandit

Robust NAS under adversarial training: benchmark, theory, and beyond

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Yongtao Wu

Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robus ...

2024

Revisiting Character-level Adversarial Attacks for Language Models

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Yongtao Wu, Elias Abad Rocamora

Adversarial attacks in Natural Language Processing apply perturbations in the character or token levels. Token-level attacks, gaining prominence for their use of gradient-based methods, are susceptible to altering sentence semantics, leading to invalid adv ...

2024

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Rachid Guerraoui, Nirupam Gupta, John Stephan, Youssef Allouah, Rafaël Benjamin Pinot

The ubiquity of distributed machine learning (ML) in sensitive public domain applications calls for algorithms that protect data privacy, while being robust to faults and adversarial behaviors. Although privacy and robustness have been extensively studied ...

2023

MULTI-TASK CURRICULUM LEARNING FOR PARTIALLY LABELED DATA

Jiancheng Yang, Stanislav Lukyanenko

Incomplete labels are common in multi-task learning for biomedical applications due to several practical difficulties, e.g., expensive annotation efforts by experts, limit of data collection, different sources of data. A naive approach to enable joint lear ...

New York2023

Alternation makes the adversary weaker in two-player games

Volkan Cevher, Efstratios Panteleimon Skoulakis, Luca Viano, Ali Kavis

Motivated by alternating game-play in two-player games, we study an altenating variant of the Online Linear Optimization (OLO). In alternating OLO, a learner at each round t ∈[n] selects a vector xt and then an adversary selects a cost-vector ct ∈[−1,1]n. ...

2023

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

2023

Why is the winner the best?

Jian Wang, Gabriel Girard, Ho Ling Li, Adrien Raphaël Depeursinge, Yong Yang, Fan Xia, Xiao Wang, Jing Li, Hui Wang

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really gener ...

Los Alamitos2023

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

Daniel Kuhn, Yves Rychener, Tobias Sutter

We develop a principled approach to end-to-end learning in stochastic optimization. First, we show that the standard end-to-end learning algorithm admits a Bayesian interpretation and trains a posterior Bayes action map. Building on the insights of this an ...

2023

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Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

2023

Multi-armed bandit

Robust NAS under adversarial training: benchmark, theory, and beyond

Revisiting Character-level Adversarial Attacks for Language Models

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

MULTI-TASK CURRICULUM LEARNING FOR PARTIALLY LABELED DATA

Alternation makes the adversary weaker in two-player games

Enriching the Computational Toolbox for Organocatalysis

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Universal and adaptive methods for robust stochastic optimization

Why is the winner the best?

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

Graph Chatbot

Chat with Graph Search

Robust NAS under adversarial training: benchmark, theory, and beyond

Enriching the Computational Toolbox for Organocatalysis

Why is the winner the best?

Revisiting Character-level Adversarial Attacks for Language Models

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Alternation makes the adversary weaker in two-player games

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Universal and adaptive methods for robust stochastic optimization

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective