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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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0111 · Apr 202019922001200920172026
7 results for “scalarisation”
Scalarisation-based risk concepts for robust multi-objective optimisationmath.OC

This work explores robust multi-objective optimisation with scalarisation and robustification.

problem Optimizing functions with uncertainty and varying objectives.
method Identifies the importance of robustification and scalarisation in multi-objective optimisation.
result Different orders of scalarisation and robustification lead to different solutions.
robust optimisationmulti-objective optimisationscalarisationrobustificationuncertainty
2024-05-16abs ↗pdf ↗
R-MBO: A Multi-surrogate Approach for Preference Incorporation in Multi-objective Bayesian Optimisationstat.ML

A new method for incorporating preferences in multi-objective Bayesian optimization.

problem Incorporating preferences in computationally expensive multi-objective optimization problems.
method Building independent surrogate models on each objective function and using Generalised value distribution to approximate the scalarizing function.
result The proposed multi-surrogate approach outperforms the mono-surrogate approach on benchmark and real-world problems.
Bayesian optimizationMulti-objective optimizationPreference incorporationMulti-surrogate approachGeneralised value distribution
2022-04-27abs ↗pdf ↗
MBORE: Multi-objective Bayesian Optimisation by Density-Ratio Estimationcs.LG

MBORE optimizes multi-objective problems using density-ratio estimation.

problem Optimizing complex, multi-objective functions with expensive evaluations.
method Extends BORE to multi-objective Bayesian optimisation, using density-ratio estimation.
result MBORE outperforms BO on high-dimensional and real-world problems.
Bayesian optimisationMulti-objectiveDensity-ratio estimationOptimizationExpensive evaluations
2022-03-31abs ↗pdf ↗
A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitionscs.LG

New issue found in value-based reinforcement learning for stochastic environments.

problem Value-based reinforcement learning struggles with stochastic state transitions.
method Demonstrated using a multiobjective Markov Decision Process (MOMDP).
result Approaches may converge to Pareto-dominated solutions instead of optimal ones.
reinforcement learningmultiobjectivestochasticMarkov Decision Processvalue-based
2020-04-14abs ↗pdf ↗