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Found 10 papers, 3 papers with code
Smooth Tchebycheff Scalarization for Multi-Objective Optimization
Multi-objective optimization problems can be found in many real-world applications, where the objectives often conflict each other and cannot be optimized by a single solution.
PMGDA: A Preference-based Multiple Gradient Descent Algorithm
It is desirable in many multi-objective machine learning applications, such as multi-task learning with conflicting objectives and multi-objective reinforcement learning, to find a Pareto solution that can match a given preference of a decision maker.
Multi-Objective Reinforcement Learning
Multi-Task Learning
+1
UMOEA/D: A Multiobjective Evolutionary Algorithm for Uniform Pareto Objectives based on Decomposition
Multiobjective optimization (MOO) is prevalent in numerous applications, in which a Pareto front (PF) is constructed to display optima under various preferences.
Panacea: Pareto Alignment via Preference Adaptation for LLMs
Our work marks a step forward in effectively and efficiently aligning models to diverse and intricate human preferences in a controllable and Pareto-optimal manner.
CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents
Within CivRealm, we provide interfaces for two typical agent types: tensor-based agents that focus on learning, and language-based agents that emphasize reasoning.
Evolutionary Pareto Set Learning with Structure Constraints
In our approach, the Pareto optimality can be traded off with a preferred structure among the whole solution set, which could be crucial for many real-world problems.
Continuation Path Learning for Homotopy Optimization
Homotopy optimization is a traditional method to deal with a complicated optimization problem by solving a sequence of easy-to-hard surrogate subproblems.
Pareto Set Learning for Expensive Multi-Objective Optimization
This work represents the first attempt to model the Pareto set for expensive multi-objective optimization.
A novel MDPSO-SVR hybrid model for feature selection in electricity consumption forecasting
Electricity consumption forecasting has vital importance for the energy planning of a country.
Simplifying Polylogarithms with Machine Learning
Polylogrithmic functions, such as the logarithm or dilogarithm, satisfy a number of algebraic identities.
