Overview
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces
Motoya Ohnishi, Masahiro Yukawa, Mikael Johansson, Masashi Sugiyama
Neural Information Processing Systems http://nips.cc/
Data Parameters: A New Family of Parameters for Learning a Differentiable Curriculum
Shreyas Saxena, Oncel Tuzel, Dennis DeCoste
Neural Information Processing Systems http://nips.cc/
Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization
Aiming to address this limitation, we present Easy2Hard-Bench, a consistently formatted collection of 6 benchmark datasets spanning various domains, such as mathematics and programming problems, chess puzzles, and reasoning questions.
Shadowing Properties of Optimization Algorithms
Antonio Orvieto, Aurelien Lucchi
Analyzing the convergence properties of these algorithms can be complex, especially for NAG whose convergence proof relies on algebraic tricks that reveal little detail about the acceleration phenomenon, i.e. the celebrated optimality of NAG in convex smooth optimization. Instead, an alternative approach is to view these methods as numerical integrators of some ordinary differential equations (ODEs).