5 accepted papers
Current data-driven scientific modeling struggles with a functional dichotomy: neural operators exhibit spectral bias in high-frequency regimes, while physics-constrained paradigms suffer from optimization pathologies. To bridge this gap, we propose Adaptive Physics-Informed Computing (APIC), a neur…
We present an evolutionary algorithm evo-SMC for the problem of Submodular Maximization under Cost constraints (SMC). Our algorithm achieves 1/2-approximation with a high probability 1-1/n within O(n2 K) iterations, where K denotes the maximum size of a feasible solution set with cost constraint B.…
We investigate the problem of stochastic, combinatorial multi-armed bandits where the learner only has access to bandit feedback and the reward function can be non-linear. We provide a general framework for adapting discrete offline approximation algorithms into sublinear $\alpha$-regret methods tha…
We consider the problem of maximizing submodular functions under submodular constraints by formulating the problem in two ways: SCSKC and DiffC. Given two submodular functions f and g where f is monotone, the objective of SCSKC problem is to find a set S of size at most k that maximizes f(S) under…
We investigate the problems of maximizing k-submodular functions over total size constraints and over individual size constraints. k-submodularity is a generalization of submodularity beyond just picking items of a ground set, instead associating one of k types to chosen items. For sensor selection…