Alaa Saade — accepted AI-conference papers

2024

Unlocking the Power of Representations in Long-term Novelty-based Exploration

Alaa Saade, Steven Kapturowski, Daniele Calandriello, Charles Blundell, Pablo Sprechmann, Leopoldo Sarra +3

ICLR 2024spotlight

We introduce Robust Exploration via Clustering-based Online Density Estimation (RECODE), a non-parametric method for novelty-based exploration that estimates visitation counts for clusters of states based on their similarity in a chosen embedding space. By adapting classical clustering to the nonsta…

Cited by 6 Source ↗PDF ↗Scholar ↗

2023

Quantile Credit Assignment

Thomas Mesnard, Wenqi Chen, Alaa Saade, Yunhao Tang, Mark Rowland, Theophane Weber +7

ICML 2023oral

In reinforcement learning, the credit assignment problem is to distinguish luck from skill, that is, separate the inherent randomness in the environment from the controllable effects of the agent's actions. This paper proposes two novel algorithms, Quantile Credit Assignment (QCA) and Hindsight QCA…

Cited by 5 Source ↗PDF ↗Scholar ↗

2022

BYOL-Explore: Exploration by Bootstrapped Prediction

Zhaohan Daniel Guo, Shantanu Thakoor, Miruna Pislar, Bernardo Avila Pires, Florent Altché, Corentin Tallec +8

NeurIPS 2022accept

We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven exploration in visually complex environments. BYOL-Explore learns the world representation, the world dynamics and the exploration policy all-together by optimizing a single prediction loss in the latent space w…

Cited by 76 Source ↗PDF ↗Scholar ↗

2021

Counterfactual Credit Assignment in Model-Free Reinforcement Learning

Thomas Mesnard, Theophane Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan +8

ICML 2021spotlight

Credit assignment in reinforcement learning is the problem of measuring an action’s influence on future rewards. In particular, this requires separating skill from luck, i.e. disentangling the effect of an action on rewards from that of external factors and subsequent actions. To achieve this, we ad…

Cited by 78 Source ↗PDF ↗Scholar ↗

2016

Random projections through multiple optical scattering: Approximating Kernels at the speed of light

Alaa Saade, Francesco Caltagirone, Igor Carron, Laurent Daudet, Angélique Dremeau, Sylvain Gigan +1

ICASSP 2016accepted

Random projections have proven extremely useful in many signal processing and machine learning applications. However, they often require either to store a very large random matrix, or to use a different, structured matrix to reduce the computational and memory costs. Here, we overcome this difficult…

Cited by 0 Source ↗Scholar ↗

2015

Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation

Alaa Saade, Florent Krzakala, Lenka Zdeborová

NeurIPS 2015poster

The completion of low rank matrices from few entries is a task with many practical applications. We consider here two aspects of this problem: detectability, i.e. the ability to estimate the rank $r$ reliably from the fewest possible random entries, and performance in achieving small reconstruction…

Cited by 22 Source ↗PDF ↗Scholar ↗