Anthony Bardou

selected publications

1. ICLR

   Symmetry-Aware Bayesian Optimization via Max Kernels

   Anthony Bardou, Antoine Gonon, Aryan Ahadinia, and 1 more author

   In ICLR’26: Fourteenth International Conference on Learning Representations 2026

   Abs Bib PDF

   Bayesian Optimization (BO) is a powerful framework for optimizing noisy, expensive-to-evaluate black-box functions. When the objective exhibits invariances under a group action, exploiting these symmetries can substantially improve BO efficiency. While using maximum similarity across group orbits has long been considered in other domains, the fact that the max kernel is not positive semidefinite (PSD) has prevented its use in BO. In this work, we revisit this idea by considering a PSD projection of the max kernel. Compared to existing invariant (and non-invariant) kernels, we show it achieves significantly lower regret on both synthetic and real-world BO benchmarks, without increasing computational complexity.

   @inproceedings{iclr26,
     author = {Bardou, Anthony and Gonon, Antoine and Ahadinia, Aryan and Thiran, Patrick},
     booktitle = {ICLR'26: Fourteenth International Conference on Learning Representations},
     title = {Symmetry-Aware Bayesian Optimization via Max Kernels},
     year = {2026},
   }

2. NeurIPS

   This Too Shall Pass: Removing Stale Observations in Dynamic Bayesian Optimization

   Anthony Bardou, Patrick Thiran, and Giovanni Ranieri

   In NeurIPS’24: Thirty-Eighth Annual Conference on Neural Information Processing Systems 2024

   Abs Bib PDF Video

   Bayesian Optimization (BO) has proven to be very successful at optimizing a static, noisy, costly-to-evaluate black-box function f : \mathcalS \to \mathbbR. However, optimizing a black-box which is also a function of time (i.e., a dynamic function) f : \mathcalS \times \mathcalT \to \mathbbR remains a challenge, since a dynamic Bayesian Optimization (DBO) algorithm has to keep track of the optimum over time. This changes the nature of the optimization problem in at least three aspects: (i) querying an arbitrary point in \mathcalS \times \mathcalT is impossible, (ii) past observations become less and less relevant for keeping track of the optimum as time goes by and (iii) the DBO algorithm must have a high sampling frequency so it can collect enough relevant observations to keep track of the optimum through time. In this paper, we design a Wasserstein distance-based criterion able to quantify the relevancy of an observation with respect to future predictions. Then, we leverage this criterion to build W-DBO, a DBO algorithm able to remove irrelevant observations from its dataset on the fly, thus maintaining simultaneously a good predictive performance and a high sampling frequency, even in continuous-time optimization tasks with unknown horizon. Numerical experiments establish the superiority of W-DBO, which outperforms state-of-the-art methods by a comfortable margin.

   @inproceedings{wdbo,
     author = {Bardou, Anthony and Thiran, Patrick and Ranieri, Giovanni},
     booktitle = {NeurIPS'24: Thirty-Eighth Annual Conference on Neural Information Processing Systems},
     title = {This Too Shall Pass: Removing Stale Observations in Dynamic Bayesian Optimization},
     year = {2024},
     video = {PermSix-Hump_Camel_25.0_240.mp4},
   }

3. MSWiMBest Paper Award

   INSPIRE: Distributed Bayesian Optimization for ImproviNg SPatIal REuse in Dense WLANs

   Anthony Bardou, and Thomas Begin

   In MSWiM ’22: Int’l ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems 2022

   Abs Bib PDF

   WLANs, which have overtaken wired networks to become the primary means of connecting devices to the Internet, are prone to performance issues due to the scarcity of space in the radio spectrum. As a response, IEEE 802.11ax and subsequent amendments aim at increasing the spatial reuse of a radio channel by allowing the dynamic update of two key parameters in wireless transmission: the transmission power (TX_POWER) and the sensitivity threshold (OBSS_PD). In this paper, we present INSPIRE, a distributed online learning solution performing local Bayesian optimizations based on Gaussian processes to improve the spatial reuse in WLANs. INSPIRE makes no explicit assumptions about the topology of WLANs and favors altruistic behaviors of the access points, leading them to find adequate configurations of their TX_POWER and OBSS_PD parameters for the "greater good" of the WLANs. We demonstrate the superiority of INSPIRE over other state-of-the-art strategies using the ns-3 simulator and two examples inspired by real-life deployments of dense WLANs. Our results show that, in only a few seconds, INSPIRE is able to drastically increase the quality of service of operational WLANs by improving their fairness and throughput.

   @inproceedings{inspire,
     author = {Bardou, Anthony and Begin, Thomas},
     booktitle = {MSWiM '22: Int'l ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems},
     editor = {ACM},
     title = {INSPIRE: Distributed Bayesian Optimization for ImproviNg SPatIal REuse in Dense WLANs},
     year = {2022},
     bp = {},
   }