Section: Research Program

The Four Pillars of TAO

This Section describes TAO main research directions at the crossroad of Machine Learning and Evolutionary Computation. Since 2008, TAO has been structured in several special interest groups (SIGs) to enable the agile investigation of long-term or emerging theoretical and applicative issues. The comparatively small size of TAO SIGs enables in-depth and lively discussions; the fact that all TAO members belong to several SIGs, on the basis of their personal interests, enforces the strong and informal collaboration of the groups, and the fast information dissemination.

The first two SIGs consolidate the key TAO scientific pillars, while the others evolve and adapt to new topics.

The Stochastic Continuous Optimization SIG (OPT-SIG) takes advantage of the fact that TAO is acknowledged the best French research group and one of the top international groups in evolutionary computation from a theoretical and algorithmic standpoint. A main priority on the OPT-SIG research agenda is to provide theoretical and algorithmic guarantees for the current world state-of-the-art continuous stochastic optimizer, CMA-ES, ranging from convergence analysis (Youhei Akimoto's post-docs) to a rigorous benchmarking methodology. Incidentally, this benchmark platform COCO has been acknowledged since 2009 as “the“ international continuous optimization benchmark, and its extension is at the core of the ANR project NumBBO (started end 2012). Another priority is to address the current limitations of CMA-ES in terms of high-dimensional or expensive optimization and constraint handling (respectively Ouassim Ait El Hara's, Ilya Loshchilov's PhDs and Asma Atamna's).

The Optimal Decision Making under Uncertainty SIG (UCT-SIG) benefits from the MoGo expertise (see Section 5.2 and the team previous activity reports) and its past and present world records in the domain of computer-Go, establishing the international visibility of TAO in sequential decision making. Since 2010, UCT-SIG resolutely moves to address the problems of energy management from a fundamental and applied perspective. On the one hand, energy management offers a host of challenging issues, ranging from long-horizon policy optimization to the combinatorial nature of the search space, from the modeling of prior knowledge to non-stationary environment to name a few. On the other hand, the energy management issue can hardly be tackled in a pure academic perspective: tight collaborations with industrial partners are needed to access the true operational constraints. Such international and national collaborations have been started by Olivier Teytaud during his three stays (1 year, 6 months, 6 months) in Taiwan, and witnessed by the FP7 STREP Citines, the ADEME Post contract, and the METIS I-lab with SME Artelys.

The E-Science SIG (E-S-SIG) replaces and extends the former Distributed systems SIG, that was devoted to the modeling and optimization of (large scale) distributed systems, and itself was extending the goals of the original Autonomic Computing SIG, initiated by Cécile Germain-Renaud and investigating the use of statistical Machine Learning for large scale computational architectures, from data acquisition (the Grid Observatory in the European Grid Initiative) to grid management and fault detection. Indeed, how to model and manage network-based activities has been acknowledged a key topic per se, including the modeling of multi-agent systems and the exploitation of simulation results in the SimTools RNSC network frame. Further extensions are still being developped in the context of the TIMCO FUI project (started end 2012); the challenge is not only to port ML algorithms on massively distributed architectures, but to see how these architectures can inspire new ML criteria and methodologies. But these activities have become more and more application-driven, from High Energy Physics for the highly distributed computation to the Social Sciences for the multi-agents approaches – hence the change of focus of this SIG. A major result of this theme is the creation of the Paris-Saclay Center for Data Science, co-chaired by Balázs Kégl, and the organization of the Higgs-ML challenge (http://higgsml.lal.in2p3.fr/ ), most popular challenge ever on the Kaggle platform.

The Designing Criteria SIG (CRI-SIG) focuses on the design of learning and optimization criteria. It elaborates on the lessons learned from the former Complex Systems SIG, showing that the key issue in challenging applications often is to design the objective itself. Such targeted criteria are pervasive in the study and building of autonomous cognitive systems, ranging from intrinsic rewards in robotics to the notion of saliency in vision and image understanding. The desired criteria can also result from fundamental requirements, such as scale invariance in a statistical physics perspective, and guide the algorithmic design. Additionally, the criteria can also be domain-driven and reflect the expert priors concerning the structure of the sought solution (e.g., spatio-temporal consistency); the challenge is to formulate such criteria in a mixed convex/non differentiable objective function, amenable to tractable optimization.

The activity of the former Crossing the Chasm SIG gradually decreased after the completion of the 2 PhD theses funded by the Microsoft/Inria joint lab (Adapt project) and devoted to hyper-parameter tuning. As a matter of fact, though not a major research topic any more, hyper-parameter tuning has become pervasive in TAO, chiefly for continuous optimization (OPT-SIG, Section 6.3 ), AI planning (CRI-SIG, Section 6.5 ) and Air Traffic Control Optimization (Section 4.2 ). Recent work addressing algorithm selection using Collaborative Filtering algorithms (CRI-SIG, Section 6.5 ) can (and will) indeed be applied to hyper-parameter tuning for optimization algorithms.