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.
Simple greedy algorithms can excel in multi-objective bandits with multiple good arms.
Bayesian methods improve tracking multiple objects through dynamic dependencies.
Paper introduces a Bayesian nonparametric approach for tracking multiple objects with spawning events.
The paper introduces a new metric to quantify uncertainty's impact on multiple objectives.
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…
Develops a new method to improve performance in multi-objective learning problems.
MWGraD solves multi-objective distributional optimization using particle-based gradient descent.
MT-SGD samples from multiple target distributions using gradient descent.
PBO framework optimizes latent preferences over multiple objectives.
This research improves multimodal systems by adding a second objective and regularisation methods.
New BO method optimizes multiple objectives under input noise.
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 …
iMOCA optimizes multiple objectives with continuous approximations for resource efficiency.
Human perception is structured around objects which form the basis for our higher-level cognition and impressive systematic generalization abilities. Yet most work on representation learning focuses on feature learning without even considering multiple objects, or treats segmentation as an (often supervised) preprocess…
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…
Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in contrast to the traditional game involving one generator against a single adversary. Such methods perform single-objective optimization on some simple consolidation of the losses, e…
Framework optimizes multiple objectives considering input uncertainty.
We introduce a scalable approach for object pose estimation trained on simulated RGB views of multiple 3D models together. We learn an encoding of object views that does not only describe an implicit orientation of all objects seen during training, but can also relate views of untrained objects. Our single-encoder-mult…
Pareto Testing optimizes model performance under multiple constraints.
BoTier optimizes experiments by balancing multiple objectives hierarchically.
Unified DNN-based precoder for MIMO networks with multiple objectives.
BOtied optimizes multiple objectives using copulas and CDF indicators.
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.
The paper explores how regularization can improve multi-objective learning with high-dimensional data.
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…
It is notoriously difficult to control the behavior of reinforcement learning agents. Agents often learn to exploit the environment or reward signal and need to be retrained multiple times. The multi-objective reinforcement learning (MORL) framework separates a reward function into several objectives. An ideal MORL age…
In important applications involving multi-task networks with multiple objectives, agents in the network need to decide between these multiple objectives and reach an agreement about which single objective to follow for the network. In this work we propose a distributed decision-making algorithm. The agents are assumed …
A new objective function using Jensen-Shannon divergence improves generative learning from multiple data types.
Proposes a new scoring function for linear classifiers to improve object positioning in feature space.
The clustering algorithms that view each object data as a single sample drawn from a certain distribution, Gaussian distribution, for example, has been a hot topic for decades. Many clustering algorithms: such as k-means and spectral clustering are proposed based on the single sample assumption. However, in real life, …
Proposes new stochastic algorithms for multi-objective optimization.
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…
Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other. Modeling and forecasting the behavior of such dynamics are generally not easy, due to the inherent hardness in understanding the complicated interactions and evolutions of their constituents. This paper introduce…
MORBO improves multi-objective BO for high-dimensional problems.
Parallel Bayesian optimization tackles noisy multi-objective problems.
ConBO optimizes multiple objectives conditional on state variables.
VOPy optimizes multiple objectives with flexible cone-based ordering.
We introduce a rich model for multi-objective clustering with lexicographic ordering over objectives and a slack. The slack denotes the allowed multiplicative deviation from the optimal objective value of the higher priority objective to facilitate improvement in lower-priority objectives. We then propose an algorithm …
The paper proposes a new system ID method from noisy data.
We present a deep-learning network that detects multiple small objects (hundreds to thousands) in a scene while simultaneously estimating their x,y pixel locations together with a characteristic feature-set (for instance, target orientation and color). All estimations are performed in a single, forward pass which makes…
Proposes KMvDA for object recognition from multi-view data.
Object tracking is an ubiquitous problem that appears in many applications such as remote sensing, audio processing, computer vision, human-machine interfaces, human-robot interaction, etc. Although thoroughly investigated in computer vision, tracking a time-varying number of persons remains a challenging open problem.…
Introduces VB-structures for geometric objects on manifolds.
The paper tackles robust policy learning from multiple data sources.
Parallel BO method for multi-objective optimization with constraints.