arXiv research

Bayesian framework learns latent preference archetypes for many-objective optimization.

Many objective Bayesian optimization tackles redundant objectives in expensive black-box functions.

Using piezoelectric impedance/admittance sensing for structural health monitoring is promising, owing to the simplicity in circuitry design as well as the high-frequency interrogation capability. The actual identification of fault location and severity using impedance/admittance measurements, nevertheless, remains to b…

BOtied optimizes multiple objectives using copulas and CDF indicators.

The ability to detect and track objects in the visual world is a crucial skill for any intelligent agent, as it is a necessary precursor to any object-level reasoning process. Moreover, it is important that agents learn to track objects without supervision (i.e. without access to annotated training videos) since this w…

Similarity measure for Gaussian process predictive distributions.

Neural network approximates weakly efficient frontier of convex vector optimization problems.

Scalarizing functions have been widely used to convert a multiobjective optimization problem into a single objective optimization problem. However, their use in solving (computationally) expensive multi- and many-objective optimization problems in Bayesian multiobjective optimization is scarce. Scalarizing functions ca…

Most systems and learning algorithms optimize average performance or average loss -- one reason being computational complexity. However, many objectives of practical interest are more complex than simply average loss. This arises, for example, when balancing performance or loss with fairness across people. We prove tha…

A notion of an algebroid - a generalization of a Lie algebroid structure is introduced. We show that many objects of the differential calculus on a manifold M associated with the canonical Lie algebroid structure on T^M can be obtained in the framework of a general algebroid. Also a compatibility condition which leads,…

The study quantifies how many objects can be linearly classified under all views.

We define the notion of whiskered categories and groupoids, showing that whiskered groupoids have a commutator theory. So also do whiskered $R$-categories, thus answering questions of what might be `commutative versions' of these theories. We relate these ideas to the theory of Leibniz algebras, but the commutator theo…

An ongoing aim of research in multiobjective Bayesian optimization is to extend its applicability to a large number of objectives. While coping with a limited budget of evaluations, recovering the set of optimal compromise solutions generally requires numerous observations and is less interpretable since this set tends…

We study a game-theoretic variant of the maximum circulation problem. In a flow allocation game, we are given a directed flow network. Each node is a rational agent and can strategically allocate any incoming flow to the outgoing edges. Given the strategy choices of all agents, a maximal circulation that adheres to the…

We can define a neural network that can learn to recognize objects in less than 100 lines of code. However, after training, it is characterized by millions of weights that contain the knowledge about many object types across visual scenes. Such networks are thus dramatically easier to understand in terms of the code th…

New algorithm improves insurance company's asset allocation decisions.

We construct a map from the suspension $G$-spectrum $Σ_G^\infty M$ of a smooth compact $G$-manifold to the equivariant $A$-theory spectrum $A_G(M)$, and we show that its fiber is, on fixed points, a wedge of stable $h$-cobordism spectra. This map is constructed as a map of spectral Mackey functors, which is compatible …

We study a spectral generalization of classical combinatorial graph spanners to the spectral setting. Given a set of vectors $V\subseteq \Re^d$, we say a set $U\subseteq V$ is an $α$-spectral spanner if for all $v\in V$ there is a probability distribution $μ_v$ supported on $U$ such that $$vv^\intercal \preceq α\cdot\m…

Non-convex optimization with local search heuristics has been widely used in machine learning, achieving many state-of-art results. It becomes increasingly important to understand why they can work for these NP-hard problems on typical data. The landscape of many objective functions in learning has been conjectured to …

New models explain heavy-tailed behavior in neural networks.

Forward-prediction models enhance physical reasoning, but only for specific tasks.

Skew parallelogram nets factorize, encompassing discrete differential geometry.

New algorithm combines curriculum learning with HER for complex object manipulation tasks.

Building models, or maps, of robot environments is a highly active research area; however, most existing techniques construct unstructured maps and assume static environments. In this paper, we present an algorithm for learning object models of non-stationary objects found in office-type environments. Our algorithm exp…

This thesis bridges Lie theory and sketch theory using tangent categories.

Many objects in the real world are difficult to describe by a single numerical vector of a fixed length, whereas describing them by a set of vectors is more natural. Therefore, Multiple instance learning (MIL) techniques have been constantly gaining on importance throughout last years. MIL formalism represents each obj…

Statistical models outperform mechanistic models in short-term COVID-19 incidence forecasts.

In this paper, which is mostly a research announcement, we give a new algebraic construction of knot contact homology in the sense of L. Ng [Ng05a]. For a link $L$ in ${\mathbb R}^3$, we define a differential graded (DG) $k$-category $\tilde{\mathscr A}$ with finitely many objects, whose quasi-equivalence class is …

The Familiarity Hypothesis explains deep open set methods' success in detecting novel objects.

Kauffman's bracket is an invariant of regular isotopy of knots and links which since its discovery in 1985 it has been used in many different directions: (a) it implies an easy proof of the invariance of (in fact, it is equivalent to) the Jones polynomial; (b) it is the basic ingredient in a completely combinatorial co…

The paper builds interpretable models for property markets using machine learning.

Perception is fundamental to many robot application areas especially in service robotics. Our aim is to perceive and model an unprepared kitchen scenario with many objects. We start with the perception of a single target object. The modeling relies especially on fusing and merging of weak information from the sensors o…

Bayesian optimization reduces computational effort in aircraft design optimization.

Bayesian optimization method tackles combinatorial spaces, scalable for large data.

New algorithm solves complex stopping problems with robust optimization.

L2O uses ML to optimize traditional optimization techniques.

When hyperparameter optimization of a machine learning algorithm is repeated for multiple datasets it is possible to transfer knowledge to an optimization run on a new dataset. We develop a new hyperparameter-free ensemble model for Bayesian optimization that is a generalization of two existing transfer learning extens…

Meta algorithm solves multivariate optimization using univariate optimizers.

A novel neural network approach for optimization problems.

New algorithms ensure reproducibility and optimal convergence in convex optimization.

New algorithm selects robust martingale for optimal stopping problems.

Numerical optimization is an important tool in the field of computational physics in general and in nano-optics in specific. It has attracted attention with the increase in complexity of structures that can be realized with nowadays nano-fabrication technologies for which a rational design is no longer feasible. Also, …

This paper shows how to combine optimal tests into log-optimal processes.

Learning optimal feedback control laws capable of executing optimal trajectories is essential for many robotic applications. Such policies can be learned using reinforcement learning or planned using optimal control. While reinforcement learning is sample inefficient, optimal control only plans an optimal trajectory fr…

Paper studies optimal control for a specific geometric problem.

New algorithm AG-OG optimizes separable convex-concave problems efficiently.

Adapts Bayesian optimization for mixed constraints in aircraft design.

Adam optimizer converges to zeros of a new vector field, not just gradient zeros.