Search Results for author: Andras Gyorgy

Found 12 papers, 2 papers with code

A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits

no code implementations17 Jan 2022 Yasin Abbasi-Yadkori, Andras Gyorgy, Nevena Lazic

We propose a method that achieves, in $K$-armed bandit problems, a near-optimal $\widetilde O(\sqrt{K N(S+1)})$ dynamic regret, where $N$ is the time horizon of the problem and $S$ is the number of times the identity of the optimal arm changes, without prior knowledge of $S$.

Learning to Minimize Age of Information over an Unreliable Channel with Energy Harvesting

no code implementations30 Jun 2021 Elif Tugce Ceran, Deniz Gunduz, Andras Gyorgy

The time average expected age of information (AoI) is studied for status updates sent over an error-prone channel from an energy-harvesting transmitter with a finite-capacity battery.

Reinforcement Learning (RL) Scheduling

On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning

no code implementations15 Jun 2021 Abbas Abdolmaleki, Sandy H. Huang, Giulia Vezzani, Bobak Shahriari, Jost Tobias Springenberg, Shruti Mishra, Dhruva TB, Arunkumar Byravan, Konstantinos Bousmalis, Andras Gyorgy, Csaba Szepesvari, Raia Hadsell, Nicolas Heess, Martin Riedmiller

Many advances that have improved the robustness and efficiency of deep reinforcement learning (RL) algorithms can, in one way or another, be understood as introducing additional objectives or constraints in the policy optimization step.

Offline RL reinforcement-learning +1

Defending Against Image Corruptions Through Adversarial Augmentations

no code implementations ICLR 2022 Dan A. Calian, Florian Stimberg, Olivia Wiles, Sylvestre-Alvise Rebuffi, Andras Gyorgy, Timothy Mann, Sven Gowal

Modern neural networks excel at image classification, yet they remain vulnerable to common image corruptions such as blur, speckle noise or fog.

Image Classification

A Reinforcement Learning Approach to Age of Information in Multi-User Networks with HARQ

no code implementations19 Feb 2021 Elif Tugce Ceran, Deniz Gunduz, Andras Gyorgy

Scheduling the transmission of time-sensitive information from a source node to multiple users over error-prone communication channels is studied with the goal of minimizing the long-term average age of information (AoI) at the users.

reinforcement-learning Reinforcement Learning (RL) +1

Perceptually Constrained Adversarial Attacks

no code implementations14 Feb 2021 Muhammad Zaid Hameed, Andras Gyorgy

Motivated by previous observations that the usually applied $L_p$ norms ($p=1, 2,\infty$) do not capture the perceptual quality of adversarial examples in image classification, we propose to replace these norms with the structural similarity index (SSIM) measure, which was developed originally to measure the perceptual similarity of images.

Image Classification SSIM

Non-Stationary Delayed Bandits with Intermediate Observations

no code implementations3 Jun 2020 Claire Vernade, Andras Gyorgy, Timothy Mann

In fact, if the timescale of the change is comparable to the delay, it is impossible to learn about the environment, since the available observations are already obsolete.

Recommendation Systems


no code implementations ICLR 2020 Krishnamurthy (Dj) Dvijotham, Jamie Hayes, Borja Balle, Zico Kolter, Chongli Qin, Andras Gyorgy, Kai Xiao, Sven Gowal, Pushmeet Kohli

Formal verification techniques that compute provable guarantees on properties of machine learning models, like robustness to norm-bounded adversarial perturbations, have yielded impressive results.

Audio Classification BIG-bench Machine Learning +1

The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection

no code implementations27 Feb 2019 Muhammad Zaid Hameed, Andras Gyorgy, Deniz Gunduz

We consider a communication scenario, in which an intruder tries to determine the modulation scheme of the intercepted signal.

Image Classification

LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration

no code implementations ICML 2018 Gellert Weisz, Andras Gyorgy, Csaba Szepesvari

We consider the problem of configuring general-purpose solvers to run efficiently on problem instances drawn from an unknown distribution.

Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection

1 code implementation8 Feb 2018 Andrea Paudice, Luis Muñoz-González, Andras Gyorgy, Emil C. Lupu

We show empirically that the adversarial examples generated by these attack strategies are quite different from genuine points, as no detectability constrains are considered to craft the attack.

Anomaly Detection BIG-bench Machine Learning +3

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