Optimization
Learning to Correlate in Multi-Player General-Sum Sequential Games
Andrea Celli, Alberto Marchesi, Tommaso Bianchi, Nicola Gatti
Neural Information Processing Systems http://nips.cc/
Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning
Ruiyi Zhang, Tong Yu, Yilin Shen, Hongxia Jin, Changyou Chen
Neural Information Processing Systems http://nips.cc/
AUnifiedConvergenceTheoremforStochastic OptimizationMethods
In this work, we provide a fundamental unified convergence theorem used for deriving expected and almost sure convergence results for a series of stochastic optimization methods.
Escaping Saddle Points in Constrained Optimization
Aryan Mokhtari, Asuman Ozdaglar, Ali Jadbabaie
Asymptotic constrained (1)has [6, 18, 21, 20]. R and closedsetC Rd introduced(1).
Dynamic Local Regret for Non-convex Online Forecasting
Sergul Aydore, Tianhao Zhu, Dean P. Foster
Neural Information Processing Systems http://nips.cc/
Comparing Apples to Oranges: Learning Similarity Functions for Data Produced by Different Distributions
AI/ML problems, and in each such case σ is assumed to be known .
L VM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching Duy M. H. Nguyen
Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated samples has remained an open challenge for medical imaging data.
