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Found 5 papers, 1 papers with code
Adaptive multi-gradient methods for quasiconvex vector optimization and applications to multi-task learning
We present an adaptive step-size method, which does not include line-search techniques, for solving a wide class of nonconvex multiobjective programming problems on an unbounded constraint set.
A Hyper-Transformer model for Controllable Pareto Front Learning with Split Feasibility Constraints
Controllable Pareto front learning (CPFL) approximates the Pareto solution set and then locates a Pareto optimal solution with respect to a given reference vector.
A Novel Approach in Solving Stochastic Generalized Linear Regression via Nonconvex Programming
Generalized linear regressions, such as logistic regressions or Poisson regressions, are long-studied regression analysis approaches, and their applications are widely employed in various classification problems.
Building Footprint Extraction in Dense Areas using Super Resolution and Frame Field Learning
Despite notable results on standard aerial datasets, current state-of-the-arts fail to produce accurate building footprints in dense areas due to challenging properties posed by these areas and limited data availability.
Improving Pareto Front Learning via Multi-Sample Hypernetworks
Pareto Front Learning (PFL) was recently introduced as an effective approach to obtain a mapping function from a given trade-off vector to a solution on the Pareto front, which solves the multi-objective optimization (MOO) problem.
