Paper tackles multi-object reinforcement learning, improving skill extrapolation.
problem Learning robust manipulation tasks in multi-object settings.
method Introduces a linear relation network module to enhance skill generalization.
result Agents can extrapolate and generalize to any new object number, scaling linearly.
New attacks inflate earnings while reducing fraud scores, potentially millions at stake.
problem Manipulating financial reports to hide distress and gain.
method Maximum Violated Multi-Objective (MVMO) attacks that adapt search direction.
result Inflation of earnings by 100-200% while reducing fraud scores by 15% in 50% of cases.
A new multi-objective RL framework improves intrinsic exploration performance.
problem Sub-optimal exploration performance due to ad-hoc handling of intrinsic exploration.
method A multi-objective RL framework where both exploration and exploitation are optimized as separate objectives.
result EMU-Q method outperforms classic and other intrinsic RL methods on benchmarks.
Mastering robotic manipulation skills through reinforcement learning (RL) typically requires the design of shaped reward functions. Recent developments in this area have demonstrated that using sparse rewards, i.e. rewarding the agent only when the task has been successfully completed, can lead to better policies. Howe…
Unified framework for multi-objective curriculum learning in robotics.
problem Improving sample efficiency and final performance in robotic policy learning.
method Unified automatic curriculum learning framework with multi-task hyper-net and flexible memory mechanism.
result Superior performance compared to state-of-the-art methods in robotic manipulation tasks.
A new Adamize method improves multi-objective recommender systems.
problem Improving recommendation systems with multiple conflicting objectives.
method Developed a multi-objective model-agnostic Adamize method that corrects and stabilizes gradients.
result Significant improvements in recommendation systems, measured by hypervolume, coverage, and spacing.
Proof of convergence for multi-objective optimization using inverse reinforcement learning.
problem Proving convergence in multi-objective optimization problems.
method Wasserstein inverse reinforcement learning with projective subgradient method and gradient descent.
result Convergence of inverse reinforcement learning for multi-objective optimization.
Proof shows imitation of expert's reward and solutions in multi-objective optimization.
problem Multi-objective optimization with reward and solution imitation.
method Wasserstein inverse reinforcement learning.
result Wasserstein inverse reinforcement learning enables imitation of expert's reward and solutions in multi-objective optimization.
Pessimistic estimator improves multi-objective policy optimization.
problem Optimizing multi-objective policies from existing data.
method Pessimistic estimator based on inverse propensity scores (IPS).
result Pessimistic estimator outperforms naive IPS estimator in theory and experiments.
Study on Pareto optimality in multi-objective bandit problems.
problem Pareto optimality in multi-objective multi-armed bandit problems.
method Formulated adversarial multi-objective multi-armed bandit, defined Pareto regrets, presented algorithms, established upper and lower bounds.
result New algorithms are optimal in adversarial settings and nearly optimal in stochastic settings.
Enhances portfolio optimization under uncertainty using robust multi-objective methods.
problem Uncertainties in real-world portfolio optimization scenarios.
method Robust multi-objective optimization with benchmark comparisons.
result More reliable and adaptable portfolio strategies for market uncertainties.
Develops a new method to improve performance in multi-objective learning problems.
problem Gradient bias in multi-objective learning leading to degraded performance.
method Stochastic Multi-objective gradient Correction (MoCo) method that guarantees convergence without increasing batch size.
result Demonstrates effectiveness of MoCo method in simulations on multi-task learning.
This paper surveys gradient-based multi-objective deep learning methods.
problem Balancing multiple conflicting objectives in deep learning models.
method Gradient-based techniques adapted from Multi-Objective Optimization.
result Comprehensive survey of gradient-based multi-objective deep learning algorithms.
The paper explores a Multi-Objective RL approach for trading that generalizes reward functions.
problem Improving performance in single-asset trading through adaptive reward functions.
method Developed a Multi-Objective Deep Reinforcement Learning algorithm to generalize reward functions and discount factors.
result The Multi-Objective algorithm demonstrates increased predictive stability and better performance in sparse reward scenarios.
Simple greedy algorithms can excel in multi-objective bandits with multiple good arms.
problem Optimizing multiple objectives in bandits is traditionally harder.
method Introduced greedy algorithms that exploit multiple good arms for multiple objectives.
result Simple greedy algorithms achieve strong performance in multi-objective bandits.
A structured understanding of our world in terms of objects, relations, and hierarchies is an important component of human cognition. Learning such a structured world model from raw sensory data remains a challenge. As a step towards this goal, we introduce Contrastively-trained Structured World Models (C-SWMs). C-SWMs…
Study KKT conditions for multi-objective optimization on Hadamard manifolds.
problem Optimizing multi-objective interval-valued functions on Hadamard manifolds.
method Developed KKT conditions for Pareto optimal solutions under different ordering and convexity notions.
result Results are more general than on Euclidean spaces.
The paper introduces a new metric to quantify uncertainty's impact on multiple objectives.
problem Quantifying the impact of uncertainty on multiple objectives in complex systems.
method Proposes the mean multi-objective cost of uncertainty (multi-objective MOCU) to quantify uncertainty.
result Demonstrates the effectiveness of the multi-objective MOCU in real-world applications.
New method improves neural architecture search by optimizing for both performance and diversity.
problem Traditional multi-objective NAS fails to address practical constraints and niches.
method Formulated as quality diversity optimization, introduces multifidelity optimizers.
result Quality diversity NAS outperforms multi-objective NAS in quality and efficiency.
This work fills the gap in understanding multi-objective learning generalization.
problem Lack of statistical learning theory insights into multi-objective learning generalization.
method Established generalization bounds and excess bounds for multi-objective learning.
result Showed that all Pareto-optimal solutions can be approximated by empirically Pareto-optimal ones, but not vice versa.
This paper introduces multi-objective hyperparameter optimization in machine learning.
problem Optimizing machine learning pipelines for multiple objectives, not just accuracy.
method Survey of optimization strategies and applications in multi-objective hyperparameter optimization.
result The importance and utility of multi-objective hyperparameter optimization in applied machine learning.
Bayesian optimisation framework for multi-objective decision-making from choice data.
problem Optimizing multi-objective functions via choice judgements.
method Gaussian process prior and novel likelihood model for choice data.
result Proposes a novel Bayesian framework for learning latent functions from choice data.
A new method for diverse Pareto solutions in multi-objective learning.
problem Maximizing diversity while maximizing hypervolume in Pareto solutions.
method Annealed Stein Variational Gradient Descent (SVGD) with diverse gradient directions.
result SVH-MOL achieves superior performance in multi-objective and multi-task learning.
A new method for multi-objective Bayesian optimization.
problem Finding optimal compromises between competing objectives.
method Joint Entropy Search (JES) acquisition function for multi-objective Bayesian optimization.
result JES outperforms existing methods in terms of hypervolume and its variants.
This paper develops a method to approximate the whole Pareto set for expensive multi-objective optimization.
problem Finding an approximate Pareto front with limited expensive evaluations.
method A novel learning-based method to approximate the whole Pareto set for multi-objective Bayesian optimization (MOBO).
result The method approximates the whole Pareto set, not just a finite set, for MOBO.
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.
MORBO improves multi-objective BO for high-dimensional problems.
problem Optimizing multiple objectives in high-dimensional spaces with expensive evaluations.
method Parallel local BO in multiple regions with coordinated strategy.
result Significant improvement in sample efficiency for high-dimensional problems.
Paper approximates solutions for complex decision processes with limited precision.
problem Approximating the set of all solutions for Multi-objective Markov Decision Processes.
method Limited precision approach based on White's multi-objective value-iteration dynamic programming algorithm.
result The number of calculated solutions is tractable and approximates the true Pareto front.
MOL-TS uses Thompson Sampling for multi-objective linear bandits with Pareto guarantees.
problem Optimizing multiple conflicting objectives in linear contextual bandits.
method Proposes MOL-TS, a Thompson Sampling algorithm with Pareto regret guarantees.
result Achieves a worst-case Pareto regret bound of O ~ ( d 3 / 2 T ) \widetilde{O}(d^{3/2}\sqrt{T}) O ( d 3/2 T ) . New framework converts multi-objective to single-objective optimisation.
problem Solving multi-objective optimisation problems.
method Formalises scalarisation into mathematical framework, uses R2 utilities.
result R2 utilities are monotone and submodular, optimised by greedy algorithms.
A new parallel BO method with exact gradients for multi-objective optimization.
problem Efficiently optimizing multiple objectives in a sample-efficient manner.
method Derive q-Expected Hypervolume Improvement (qEHVI) for parallel, constrained evaluation.
result qEHVI is computationally tractable and outperforms state-of-the-art methods.
Many real-world applications are characterized by a number of conflicting performance measures. As optimizing in a multi-objective setting leads to a set of non-dominated solutions, a preference function is required for selecting the solution with the appropriate trade-off between the objectives. The question is: how g…
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…
Deep learning agent improves pedestrian navigation in urban environments.
problem Autonomous driving among pedestrians in urban areas.
method Multi-objective deep reinforcement learning using a deep Q-learning variant.
result The multi-objective DQN agent outperforms single-objective DQN in various environments.
MESMOC optimizes constrained multi-objective problems efficiently.
problem Constrained multi-objective optimization with expensive function evaluations.
method Max-value Entropy Search in the output space.
result MESMOC selects high-quality Pareto solutions efficiently.
Reducing barriers to entry in large-scale ML markets, study shows multi-objective learning can lower data requirements.
problem Barriers to entry in emerging markets for large-scale machine learning models.
method Defined a multi-objective high-dimensional regression framework to study reputational damage and data requirements.
result The number of data points needed for a new company to enter the market can be significantly smaller than the incumbent company's dataset size.
Wind farm layout optimisation tackles space constraints with Bayesian multi-objective approach.
problem Optimizing wind farm layout due to limited space and conflicting objectives.
method Set-based multi-objective Bayesian optimisation using Gaussian process.
result Demonstrates potential of set-based Bayesian multi-objective optimisation for wind farm layout.
This article addresses the problem of derivative-free (single- or multi-objective) optimization subject to multiple inequality constraints. Both the objective and constraint functions are assumed to be smooth, non-linear and expensive to evaluate. As a consequence, the number of evaluations that can be used to carry ou…
Student- t t t processes have recently been proposed as an appealing alternative non-parameteric function prior. They feature enhanced flexibility and predictive variance. In this work the use of Student- t t t processes are explored for multi-objective Bayesian optimization. In particular, an analytical expression for the h…
Proposes new stochastic algorithms for multi-objective optimization.
problem Multi-objective optimization in machine learning problems.
method Direction-oriented multi-objective formulation and Stochastic Direction-oriented Multi-objective Gradient descent (SDMGrad).
result Stochastic algorithms converge to Pareto stationary points with improved complexities.
MWGraD solves multi-objective distributional optimization using particle-based gradient descent.
problem Simultaneously minimize multiple objective functionals over probability distributions.
method Iterative particle-based algorithm MWGraD, estimating and aggregating Wasserstein gradients.
result Demonstrates effectiveness on synthetic and real-world datasets.
MOSS optimizes decision rules for accuracy and stability.
problem Constructing stable sets of decision rules.
method Multi-objective optimization framework incorporating sparsity, accuracy, and stability.
result MOSS outperforms state-of-the-art rule ensembles in predictive performance and stability.
FanG-HPO optimizes machine learning models for fairness and low energy consumption.
problem Bias in machine learning models and high energy consumption in hyperparameter optimization.
method Combines multi-objective and multiple information source Bayesian optimization.
result FanG-HPO identifies fair and energy-efficient machine learning models.
MO-CBO optimizes multiple outcomes in causal systems with minimal data.
problem Optimizing multiple outcomes in causal systems with limited data.
method Decomposes MO-CBO into multi-objective optimization tasks and uses relative hypervolume improvement for sequential intervention balancing.
result MO-CBO outperforms traditional multi-objective Bayesian optimization in causal settings.
YAHPO Gym introduces a new benchmark for evaluating hyperparameter optimization methods.
problem Evaluating and comparing hyperparameter optimization methods on well-curated benchmark suites.
method Surrogate-based benchmark collection of 14 scenarios, each with multi-fidelity and multi-objective hyperparameter optimization problems.
result Surrogate-based benchmarks produce more faithful results than tabular benchmarks.
Bayesian optimization is an effective method to efficiently optimize unknown objective functions with high evaluation costs. Traditional Bayesian optimization algorithms select one point per iteration for single objective function, whereas in recent years, Bayesian optimization for multi-objective optimization or multi…
New research shows Multi-Task Learning does not behave like Multi-Objective Optimization.
problem Applying MOO methods to MTL problems is not equivalent.
method Comparing MTL and MOO approaches on Multi-Fashion-MNIST datasets.
result A single model can perform as well as optimizing multiple objectives.
This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks. We develop a high-performance MODRL framework that supports both single-policy and multi-policy strategies, as well as both linear and non-linear approaches to action selection. The experimental …