PiVoT improves real-time multi-object detection and tracking in clutter.
problem Challenges in multi-object detection and tracking from noisy point clouds.
method Variational inference for fast, clutter-resilient multi-object tracking.
result Substantial performance improvement over existing Bayesian trackers.
End-to-end multi-object tracking learns object interactions.
problem Object tracking ignores interactions between objects.
method End-to-end relational reasoning model MOHART.
result Relational reasoning improves tracking and prediction.
End-to-end unsupervised MOT from videos.
problem Challenging online Multi-Object Tracking from videos.
method Differentiable neural model for tracking and animation, end-to-end learning.
result End-to-end learning of MOT from videos without labeled data.
Bayesian nonparametric models improve multi-object tracking robustness and flexibility.
problem Challenges in multi-object tracking, including time-dependent cardinality, unordered measurements, and object labeling.
method Robust nonparametric methods for state prior modeling, incorporating dynamic object birth/death and survival.
result Proposed models estimate time-dependent object cardinality, provide object labeling, and identify associated measurements.
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.
A novel tracking method for dense honeybee colonies using pixel personality.
problem Tracking large numbers of densely-arranged, interacting objects in a 2D environment.
method Segmentation-based object detection followed by adaptive object recognition through visual appearance.
result Reconstructed ~46% of trajectories in 5 minutes and 71% of tracks for at least 2 minutes.
A drone-based MOT algorithm tracks vehicles using neural network detections and TPMBM filter.
problem Tracking multiple vehicles from drone-mounted cameras.
method Neural network for object detection, TPMBM filter for trajectory estimation, von-Mises Fisher distribution for DOA.
result TPMBM filter optimally estimates vehicle trajectories.
Bayesian nonparametrics adapt model complexity to diverse datasets.
problem Complex challenges across statistics, computer science, and engineering.
method Flexible Bayesian nonparametric models that adapt model complexity.
result Bayesian nonparametrics offer innovative solutions to multi-object tracking.
Boosting theory extended to handle cost-sensitive and multi-objective losses.
problem Real-world prediction problems with different error costs.
method Developed a comprehensive theory of cost-sensitive and multi-objective boosting.
result Established a dichotomy for binary classification and a more intricate landscape for multiclass settings.
Modern computer vision algorithms typically require expensive data acquisition and accurate manual labeling. In this work, we instead leverage the recent progress in computer graphics to generate fully labeled, dynamic, and photo-realistic proxy virtual worlds. We propose an efficient real-to-virtual world cloning meth…
FraPPE efficiently identifies Pareto optimal arms in multi-objective bandits.
problem Efficiently identifying Pareto optimal arms in multi-objective bandits with confidence.
method Deriving structural properties and using Frank-Wolfe optimisation to solve the maxmin optimisation problem.
result FraPPE achieves optimal sample complexity and identifies the exact Pareto set.
New Gibbs sampling reduces GLMB filtering complexity to linear time.
problem NP-hard GLMB density computation in multi-object systems.
method Tempered Gibbs sampler exploiting GLMB structure.
result Linear complexity O ( T ( P + M ) ) \mathcal{O}(T(P+M)) O ( T ( P + M )) for GLMB filtering. New security patch for object tracking in self-driving cars mitigates vulnerabilities.
problem Vulnerability of object tracking in self-driving cars, especially Kalman Filter-based trackers.
method Adaptive strategy to balance focus on observations and predictions based on deviation anomaly index.
result Certified effectiveness against a generalized hijacking attack model.
In this work we introduce a mixture of GPs to address the data association problem, i.e. to label a group of observations according to the sources that generated them. Unlike several previously proposed GP mixtures, the novel mixture has the distinct characteristic of using no gating function to determine the associati…
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.
Bayesian optimization for multi-objective and multi-point search.
problem Efficiently optimize unknown multi-objective functions with high evaluation costs.
method Proposes a Bayesian optimization algorithm that considers both multi-objective and multi-point search problems, using an acquisition function and an efficient gradient estimation method.
result Shows comparable or superior performance to heuristic methods via numerical experiments.
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.
This paper treats multi-task learning as a multi-objective optimization problem.
problem Conflict between tasks in multi-task learning.
method Explicitly cast multi-task learning as multi-objective optimization and use gradient-based multi-objective optimization algorithms.
result Optimizing an upper bound of the multi-objective loss yields a Pareto optimal solution.
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.
Method uses neural networks to solve combinatorial problems.
problem Solving combinatorial problems on raw input data.
method Integrates blackbox combinatorial solvers into neural networks.
result Efficient backward pass through blackbox solvers implemented.
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…
Multi-objective optimization for hyperparameters and features.
problem Optimizing hyperparameters and selecting features for machine learning models.
method Two approaches: model-based optimization and NSGA-II-based wrapper approach using filter ensembles.
result NSGA-II approach is computationally less expensive but may require more evaluations.
Novel RL-based NPG improves multi-objective NAS efficiency and performance.
problem Discovering optimal neural architectures with multiple conflicting objectives.
method Non-stationary policy gradient with adaptive reward functions and shared model.
result Framework efficiently approximates full Pareto front and achieves superior performance.
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.
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.