arXiv research

New method finds exact Pareto front for MO-MDPs efficiently.

New Bayesian method improves Pareto front estimation in multitask finetuning.

Proposes a new way to represent and analyze Pareto front surfaces.

Algorithm identifies Pareto front in multi-objective bandits efficiently.

Algorithm identifies Pareto front using multiple context directions and reuses exploration samples.

Optimizing nonlinear systems involving expensive computer experiments with regard to conflicting objectives is a common challenge. When the number of experiments is severely restricted and/or when the number of objectives increases, uncovering the whole set of Pareto optimal solutions is out of reach, even for surrogat…

New ABC method improves Bézier simplex fitting for noisy data.

Optimization of conflicting functions is of paramount importance in decision making, and real world applications frequently involve data that is uncertain or unknown, resulting in multi-objective optimization (MOO) problems of stochastic type. We study the stochastic multi-gradient (SMG) method, seen as an extension of…

New method computes optimal fairness-performance trade-off without complex models.

SURF steers scalarization weights to uniformly traverse the Pareto front.

Most content-based image retrieval systems consider either one single query, or multiple queries that include the same object or represent the same semantic information. In this paper we consider the content-based image retrieval problem for multiple query images corresponding to different image semantics. We propose a…

Paper tackles entity matching over multi-source data, optimizing alignment and mitigating negative transfer.

The paper tackles the trade-off between fairness and accuracy in machine learning models.

We solve a portfolio selection problem with four objectives, finding convex scalarizations for part of the Pareto front.

A multiobjective optimization problem is simplicial if the Pareto set and front are homeomorphic to a simplex and, under the homeomorphisms, each face of the simplex corresponds to the Pareto set and front of a subproblem. In this paper, we show that strongly convex problems are simplicial under a mild assumption on th…

Many real world applications can be framed as multi-objective optimization problems, where we wish to simultaneously optimize for multiple criteria. Bayesian optimization techniques for the multi-objective setting are pertinent when the evaluation of the functions in question are expensive. Traditional methods for mult…

MOL-TS uses Thompson Sampling for multi-objective linear bandits with Pareto guarantees.

A new method for faster multi-objective optimization by evaluating objectives separately.

A multiobjective optimization problem is $C^r$ simplicial if the Pareto set and the Pareto front are $C^r$ diffeomorphic to a simplex and, under the $C^r$ diffeomorphisms, each face of the simplex corresponds to the Pareto set and the Pareto front of a subproblem, where $0\leq r\leq \infty$. In the paper titled "Topolo…

We propose a strategy for approximating Pareto optimal sets based on the global analysis framework proposed by Smale (Dynamical systems, New York, 1973, pp. 531-544). The method highlights and exploits the underlying manifold structure of the Pareto sets, approximating Pareto optima by means of simplicial complexes. Th…

We present a multi-objective Bayesian optimisation algorithm that allows the user to express preference-order constraints on the objectives of the type "objective A is more important than objective B". These preferences are defined based on the stability of the obtained solutions with respect to preferred objective fun…

A Human-in-the-Loop Bayesian Optimization framework for constraint-aware bioprocess development.

A new Adamize method improves multi-objective recommender systems.

Multi-objective Neural Architecture Search (NAS) aims to discover novel architectures in the presence of multiple conflicting objectives. Despite recent progress, the problem of approximating the full Pareto front accurately and efficiently remains challenging. In this work, we explore the novel reinforcement learning …

Novel NAS method balances performance and hardware metrics efficiently.

In this paper we propose the multi-objective contextual bandit problem with similarity information. This problem extends the classical contextual bandit problem with similarity information by introducing multiple and possibly conflicting objectives. Since the best arm in each objective can be different given the contex…

Paper approximates solutions for complex decision processes with limited precision.

iMOCA optimizes multiple objectives with continuous approximations for resource efficiency.

In this paper, we study the multi-objective bandits (MOB) problem, where a learner repeatedly selects one arm to play and then receives a reward vector consisting of multiple objectives. MOB has found many real-world applications as varied as online recommendation and network routing. On the other hand, these applicati…

A game-theoretic framework identifies influential hyperparameters for neural networks.

A-GPS learns to generate Pareto sets efficiently with user preferences.

MOBO-OSD optimizes multi-objective functions using orthogonal search directions.

A new method for diverse Pareto solutions in multi-objective learning.

New approach to fairness in machine learning through stochastic optimization.

New method generates continuous Pareto sets for multi-task learning.

New method compares classifiers using GSD-front, addressing statistical uncertainty and robustness.

PAC-MOO optimizes constrained multi-objective problems with preferences.

This paper develops a method to approximate the whole Pareto set for expensive multi-objective optimization.

A new framework optimizes manufacturing decisions with less data and time.

Stochastic algorithm achieves sublinear convergence for bi-objective optimization.

This work improves cost-aware Bayesian optimization by introducing Pareto-efficient acquisition functions.

Optimizes glmnet configuration for better accuracy and efficiency.

This paper improves fraud prevention rule sets in fintech by generating diverse rules and finding Pareto-optimal subsets.

A new algorithm balances fairness in clustering to avoid discrimination.

New algorithms optimize multiple machine learning metrics in real-world tasks.

Automated machine learning has gained a lot of attention recently. Building and selecting the right machine learning models is often a multi-objective optimization problem. General purpose machine learning software that simultaneously supports multiple objectives and constraints is scant, though the potential benefits …

This paper improves traffic flow modeling by using multi-gradient descent algorithms for physics-informed machine learning.

The performance of acquisition functions for Bayesian optimisation to locate the global optimum of continuous functions is investigated in terms of the Pareto front between exploration and exploitation. We show that Expected Improvement (EI) and the Upper Confidence Bound (UCB) always select solutions to be expensively…