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Special Issue on New Theoretical Challenges in Machine Learning.

, and . Algorithmica, 72 (1): 191-192 (2015)

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Martingale Boosting., and . COLT, volume 3559 of Lecture Notes in Computer Science, page 79-94. Springer, (2005)A Theoretical Analysis of Query Selection for Collaborative Filtering., and . COLT/EuroCOLT, volume 2111 of Lecture Notes in Computer Science, page 517-528. Springer, (2001)New Bounds for Learning Intervals with Implications for Semi-Supervised Learning., and . COLT, volume 23 of JMLR Proceedings, page 30.1-30.15. JMLR.org, (2012)Oracle lower bounds for stochastic gradient sampling algorithms., , and . CoRR, (2020)Gradient Descent with Identity Initialization Efficiently Learns Positive-Definite Linear Transformations by Deep Residual Networks., , and . Neural Comput., (2019)Structural Results About On-line Learning Models With and Without Queries., and . Mach. Learn., 36 (3): 147-181 (1999)Guest Editor's Introduction.. Mach. Learn., 27 (1): 5 (1997)On the Global Convergence of Training Deep Linear ResNets., , and . ICLR, OpenReview.net, (2020)Random classification noise defeats all convex potential boosters., and . ICML, volume 307 of ACM International Conference Proceeding Series, page 608-615. ACM, (2008)On-Line Learning with Linear Loss Constraints., and . COLT, page 412-421. ACM, (1993)