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.
Bayesian methods improve tracking multiple objects through dynamic dependencies.
problem Tracking multiple objects with time-varying cardinality and unordered measurements.
method Employing Bayesian nonparametric models, specifically dependent Dirichlet and Pitman-Yor processes, for state estimation and Monte Carlo sampling for trajectory learning.
result The proposed methods outperform existing algorithms in estimating the time-varying number of objects and identifying object associations.
Paper introduces a Bayesian nonparametric approach for tracking multiple objects with spawning events.
problem Tracking multiple objects with birth and death events (spawning).
method Bayesian nonparametric approach with MCMC sampling for unknown number of objects.
result Advantages of nonparametric modeling for scenarios with spawning events.
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.
We define a concept which we call multiplicity. First, multiplicity of a morphism is defined. Then the multiplicity of an object over another object is defined to be the minimum of the multiplicities of all morphisms from one to another. Based on this multiplicity, we define a pseudo distance on the class of objects. W…
ALIEN detects multiple small objects and estimates their pixel locations and features.
problem Detecting and localizing multiple small objects in a scene.
method Deep-learning network (ALIEN) that performs simultaneous x,y pixel estimation and feature extraction in a single forward pass.
result Efficient detection and localization of hundreds to thousands of small objects.
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.
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.
MT-SGD samples from multiple target distributions using gradient descent.
problem Sampling from multiple unnormalized target distributions.
method Proposes MT-SGD, a flow of intermediate distributions to sample from multiple target distributions.
result Asymptotic analysis shows MT-SGD reduces to multiple-gradient descent for multi-objective optimization.
Autotune optimizes machine learning models with multiple objectives and constraints.
problem Building and selecting optimal machine learning models is a multi-objective optimization problem.
method Derivative-free optimization methods and multi-level parallelism in a distributed computing environment.
result Autotune efficiently captures Pareto fronts and guides the search to more promising regions.
This work improves GAN training by optimizing multiple discriminator losses.
problem Training GANs with multiple discriminators using single-objective methods.
method Formulates multi-objective optimization of multiple discriminator losses.
result Hypervolume maximization outperforms previous methods in sample quality and computational cost.
PBO framework optimizes latent preferences over multiple objectives.
problem Optimizing latent preferences with multiple conflicting objectives.
method Proposes DSTS, a multi-objective generalization of dueling Thompson sampling.
result DSTS outperforms benchmarks and provides asymptotic consistency.
This research improves multimodal systems by adding a second objective and regularisation methods.
problem Improving performance of multimodal systems with multiple objectives and regularisation.
method Introduces a second objective over multimodal fusion using variational inference and regularisation methods.
result Demonstrates potential for multiple objectives and probabilistic methods to lower variance and improve generalisation.
New BO method optimizes multiple objectives under input noise.
problem Optimizing multiple performance metrics in manufacturing processes subject to random input noise.
method Formalizes optimization of multivariate value-at-risk (MVaR) using random scalarizations.
result Significantly outperforms alternative methods in identifying robust designs.
iMOCA optimizes multiple objectives with continuous approximations for resource efficiency.
problem Optimizing multiple objectives with continuous function approximations that balance accuracy and evaluation cost.
method Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations (iMOCA) selects input and function approximations to maximize information gain per unit cost.
result iMOCA significantly improves over existing single-fidelity methods in approximating the optimal Pareto set.
A new method learns to segment and represent objects jointly without supervision.
problem Learning to segment and represent objects jointly without supervision.
method Iterative variational inference for disentangled object representations.
result System learns to inpaint occluded parts and extrapolates to unseen objects.
We study statistical detection of grayscale objects in noisy images. The object of interest is of unknown shape and has an unknown intensity, that can be varying over the object and can be negative. No boundary shape constraints are imposed on the object, only a weak bulk condition for the object's interior is required…
Framework optimizes multiple objectives considering input uncertainty.
problem Efficiently optimizing multiple objectives with input uncertainty.
method Robust Gaussian Process model and two-stage Bayesian optimization process.
result Found a robust Pareto frontier considering input uncertainty.
Algorithm for agents to agree on a single objective in multi-task networks.
problem Decentralized decision-making in multi-task networks with multiple objectives.
method Distributed decision-making algorithm for agents observing different models.
result Agents reach agreement on which model to track for network performance enhancement.
Pareto Testing optimizes model performance under multiple constraints.
problem Optimizing machine learning models with multiple conflicting objectives.
method Two-stage process combining optimization and statistical testing.
result Models can be configured to satisfy multiple statistical guarantees and objectives.
Scalable approach for object pose estimation across domains.
problem Object pose estimation across different datasets and models.
method Multi-path learning: shared encoder, object-specific decoders.
result Generalizes well from synthetic to real data and across various instances.
BoTier optimizes experiments by balancing multiple objectives hierarchically.
problem Balancing multiple competing objectives in scientific experiments.
method Composite objective that flexibly represents a hierarchy of preferences over outcomes and parameters.
result Demonstrates robust applicability across various use cases and seamless integration.
Unified DNN-based precoder for MIMO networks with multiple objectives.
problem Optimizing data transmission, energy harvesting, and security in MIMO networks.
method Rotation-based precoding and DNN for multi-objective optimization.
result DNN-based precoder reduces computational complexity and achieves near-optimal performance.
BOtied optimizes multiple objectives using copulas and CDF indicators.
problem Joint optimization of multiple competing objectives.
method Proposes BOtied, a new acquisition function based on the CDF.
result BOtied outperforms state-of-the-art MOBO acquisition functions.
We develop a novel method for detection of signals and reconstruction of images in the presence of random noise. The method uses results from percolation theory. We specifically address the problem of detection of multiple objects of unknown shapes in the case of nonparametric noise. The noise density is unknown and ca…
New method optimizes multiple objectives using particle dynamics and gradient flow.
problem Optimizing multiple conflicting objectives in complex scenarios.
method Interacting particle method combining Langevin and birth-death dynamics with a dominance potential.
result Method effectively relocates dominated particles, improving Pareto optimality.
The paper explores how regularization can improve multi-objective learning with high-dimensional data.
problem Improving multi-objective learning with high-dimensional and costly data.
method A two-stage MOL framework that leverages low-dimensional structure.
result Vanilla regularization approaches often fail in multi-objective learning, and a two-stage framework can successfully exploit low-dimensional structure.
Paper proposes a deep learning method for online sequential data with multiple objectives.
problem Dealing with multiple loss functions in online deep learning tasks.
method A novel online deep learning training procedure that can handle various neural network architectures.
result Demonstrated effectiveness on the Neyman-Pearson classification problem.
Ranking a set of objects involves establishing an order allowing for comparisons between any pair of objects in the set. Oftentimes, due to the unavailability of a ground truth of ranked orders, researchers resort to obtaining judgments from multiple annotators followed by inferring the ground truth based on the collec…
We define the beta diffusion tree, a random tree structure with a set of leaves that defines a collection of overlapping subsets of objects, known as a feature allocation. A generative process for the tree structure is defined in terms of particles (representing the objects) diffusing in some continuous space, analogou…
Survey of deep learning methods for video multi-object tracking.
problem Tracking multiple objects in video sequences.
method Review of deep learning approaches applied to MOT stages.
result Identification of similarities among top-performing methods.
Generative model generates images with multiple object classes.
problem Generating images with multiple object classes.
method Conditional Deep Convolutional GAN architecture, stabilized against collapse.
result System generates diverse samples with inter-object relationships.
MGDRec optimizes multiple objectives in recommender systems.
problem Optimizing for multiple, often conflicting objectives in recommender systems.
method Stochastic multi-gradient descent approach (MGDRec).
result MGDRec outperforms state-of-the-art methods in traditional objective mixtures.
Proposes using logical specifications for multi-objective reinforcement learning to improve agent behavior.
problem Difficulties in controlling reinforcement learning agents and the need for better generalization.
method Uses propositional logic to specify the importance of multiple objectives, encoding these specifications using a recurrent neural network.
result MORL agents parameterized by logical specifications can generalize to novel combinations of objectives and achieve comparable performance.
A method solves weakly supervised multiclass MIL problems.
problem Weakly supervised multiclass MIL problems in image recognition.
method Maximizing exact likelihood fitting given observations.
result Our method can learn all convolutional neural networks.
New algorithm clusters multiple samples from hidden distributions.
problem Traditional clustering algorithms cannot handle multiple samples from non-Gaussian distributions.
method Proposes a general framework for multiple sample clustering, generating various algorithms.
result Sufficient statistics improve clustering accuracy and stability.
A new objective function using Jensen-Shannon divergence improves generative learning from multiple data types.
problem Learning from multiple data types efficiently and accurately.
method Proposes a novel objective function using Jensen-Shannon divergence to approximate multimodal posteriors directly.
result The mmJSD objective optimizes an ELBO and improves generative learning tasks.
A new method for hyperparameter tuning across multiple datasets and objectives.
problem Hyperparameter tuning for black-box functions, especially across different datasets and objectives.
method Quantile-based regression with Gaussian Copula distribution for semi-parametric modeling, combined with Thompson sampling and Gaussian Copula process.
result Significant improvements in hyperparameter optimization and neural architecture search over state-of-the-art methods.
Proposes a new scoring function for linear classifiers to improve object positioning in feature space.
problem Lack of information about relative positions of recognized objects in feature space.
method Calculates a scoring function based on object distance from decision boundary and class centroid.
result Demonstrates effectiveness of the proposed method compared to other ensemble algorithms on multiple datasets.
Introduces R-SSM for modeling multi-object dynamics with GNNs and normalizing flows.
problem Complex interactions and evolutions in multi-object systems are hard to model.
method Relational state-space model (R-SSM) using graph neural networks (GNNs) and normalizing flows.
result Empirically validated on synthetic and real datasets.
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.
We consider the notion of multiple gap as a finite set of ideals that cannot be separated. We study the different types of such objects that can be found in the Boolean algebra of subsets of the natural numbers modulo finite sets.
We study tensors on Lie groupoids suitably compatible with the groupoid structure, called {\em multiplicative}. Our main result gives a complete description of these objects only in terms of infinitesimal data. Special cases include the infinitesimal counterparts of multiplicative forms, multivector fields and holomorp…
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.
Parallel Bayesian optimization tackles noisy multi-objective problems.
problem Optimizing multiple objectives with noisy data.
method NEHVI and qNEHVI acquisition functions, integrating Bayesian treatment over uncertainty. result Parallel qNEHVI is one-step Bayes-optimal and robust to noise. ConBO optimizes multiple objectives conditional on state variables.
problem Optimizing multiple objectives with conditional state variables.
method Conditional Bayesian Optimization (ConBO) framework.
result Significantly better performance on various problems.
VOPy optimizes multiple objectives with flexible cone-based ordering.
problem Optimizing multiple objectives with partial order constraints.
method Flexible cone-based ordering, modular architecture, integration of existing and novel methods.
result Advances black-box vector optimization in noisy, discrete, or limited budget settings.
The paper proposes a new system ID method from noisy data.
problem System identification of linear and nonlinear non-autonomous systems from noisy and sparse data.
method Bayesian formulation for learning a hidden Markov model with stochastic dynamics, analyzed in the context of least squares and multiple shooting approaches.
result The proposed approach outperforms existing methods in terms of mean squared error and model generalizability.