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Regret Bounds for Satisficing in Multi-Armed Bandit Problems
Published in Transactions on Machine Learning Research, 2023
This paper explores the concept of satisficing in multi-armed bandit problems, where we aim to find solutions that exceed a satisfaction threshold rather than seeking optimal outcomes. Read more
Exploring Flow-Lenia Universes with a Curiosity-driven AI Scientist: Discovering Diverse Ecosystem Dynamics
Published in Artificial Life Conference Proceedings 37 (ALIFE), 2025
We present a method for the automated discovery of system-level dynamics in Flow-Lenia—a continuous cellular automaton (CA) with mass conservation and parameter localization—using a curiosity-driven AI scientist. This method aims to uncover processes leading to self-organization of evolutionary and ecosystemic... Read more
Sequential Membership Inference Attacks
Published in Conference on Neural Information Processing Systems (NeurIPS), 2026
Modern AI models are not static. They go through multiple updates in their lifecycles. We propose to design Sequential Membership Inference (SeMI) attacks leading to tighter privacy audits by exploiting the sequence of models and injecting a target canary at... Read more
Octax: Accelerated CHIP-8 Arcade Environments for Reinforcement Learning in JAX
Published in International Conference on Learning Representations (ICLR), 2026
Reinforcement learning (RL) research requires diverse, challenging environments that are both tractable and scalable. While modern video games may offer rich dynamics, they are computationally expensive and poorly suited for large-scale experimentation due to their CPU-bound execution. We introduce Octax,... Read more
DP-SPRT: Differentially Private Sequential Probability Ratio Tests
Published in International Conference on Artificial Intelligence and Statistics (AISTATS), 2026
We revisit Wald’s celebrated Sequential Probability Ratio Test for sequential tests of two simple hypotheses, under privacy constraints. We propose DP-SPRT, a wrapper that can be calibrated to achieve desired error probabilities and privacy constraints, addressing a gap in previous... Read more