This provides a new characterization of learning algorithms and the natural proofs barrier of Razborov and Rudich. The proof is based on a method of reconstructing Nisan-Wigderson generators introduced by Kraj铆ek (2010) and used to analyze complexity of circuit lower bounds in bounded arithmetic....
2. Based on the research progress of SPN structure learning, we summarize the existing SPN structure learning algorithms into four types. For ease of description and differentiation, we name the four types of SPN structure learning methods as Handcrafted structure learning, Data-based structure ...
the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced ...
regarding the convergence of our algorithms to a solution of the given IE (see Theorem4.1and Corollary4.2). We also refer to another work3for an elementary and computationally driven introduction to the theory behind the methods that motivate this procedure; a more detailed account is also provide...
cost and system instability caused by the multi-agent system, we propose a novel communication mechanism and analyse the accuracy of the estimation of value functions and policy gradients through the following theoretical proofs, which is completely different from the general model-based algorithms. ...
Although, historically, larger data sets have driven model performance improvements, researchers and practitioners are debating whether this trend can hold. Some have suggested that, for certain tasks and populations, model performance plateaus -- or even worsens -- as algorithms are fed more data. ...
, both ppt algorithms. both hold a secret \(\mathbf {x}\) (generated using a key generation algorithm \(\mathcal {k}\) executed on the security parameter \(\lambda \) in unary) that has been shared in an initial phase. after the execution of the authentication protocol, \(\mathcal...
LightDP: Danfeng Zhang, Daniel Kifer: LightDP: towards automating differential privacy proofs. POPL 2017: 888-901 🔝 Private Benchmark DPBench: Michael Hay, Ashwin Machanavajjhala, Gerome Miklau, Yan Chen, Dan Zhang: Principled Evaluation of Differentially Private Algorithms using DPBench. SIGMOD...
Statistical learning theory is a branch of artificial intelligence that provides the theoretical foundation for machine learning algorithms. It focuses on understanding how valid conclusions can be drawn from empirical data and selects the best hypothesis from a given set of hypotheses based on the dat...
Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David Large Language Models A Visual Guide to Quantization: Demystifying the Compression of Large Language Models by Maarten Grootendorst Foundations of Large Language Models by Tong Xiao and Jingbo Zhu Ma...