GroundedML: Workshop on Anchoring Machine Learning in Classical Algorithmic Theory
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 251
Special Issue Editors
Interests: learning from graph-like data; relational reasoning; causal discovery
Interests: Machine learning; graphs; stochastic processes
Interests: Artificial Intelligence; Computer Vision; Deep Learning; Low Data Learning; NLP
Interests: deep learning, reinforcement learning; graph representation learning and reasoning; recommender systems; natural language understanding; drug discovery
Special Issue Information
Dear Colleagues,
Recent advances in machine learning (ML) have revolutionized our ability to solve complex problems in a myriad of application domains, yet just as empirical data play a fundamental role in the development of such applications, the process of designing these methods has also remained empirical: we have learned which of the known methods tend to perform better for certain types of problems and have developed an intuition guiding our discovery of new methods.
In contrast, classical algorithmic theory provides tools directly addressing the mathematical core of a problem, and clear theoretical justifications motivate powerful design techniques. At the heart of this process is the analysis of the correctness and time/space efficiency of an algorithm, providing actionable bounds and guarantees. Problems themselves may be characterized by bounding the performance of any algorithm, providing a meaningful reference point to which concrete algorithms may be compared. While ML models may appear to be an awkward fit for such techniques, some research in the area has succeeded in obtaining results with the “definitive” flavor associated with algorithms, complementary to empirical ones. Are such discoveries bound to be exceptions, or can they be part of a new algorithmic theory?
The GoundedML workshop seeks to bring together researchers from both the algorithmic theory and machine learning communities, starting a dialogue on how ideas from theoretical algorithm design can inspire and guide future research in ML.
Topics for submissions include but are not limited to:
* Frameworks for algorithmic guarantees and time complexity bounds of ML models;
* Study of inherent complexity class of ML problems and bounds on learning capacity of certain model design paradigms;
* Develop methods for designing algorithms from problem specifications (e.g., dynamic programming) and techniques for algorithmic alignment, modularity, and compositionality of ML models.
For more information, please refer to https://sites.google.com/view/groundedml2022
Dr. Perouz Taslakian
Dr. Pierre-André Noël
Dr. David Vázquez
Dr. Jian Tang
Prof. Dr. Xavier Bresson
Guest Editors
Manuscript Submission Information
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