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.
Neural-optimized objective functions simplify optimization tasks.
A new method for incorporating preferences in multi-objective Bayesian optimization.
A tutorial on optimizing complex functions with partial knowledge.
Many objective Bayesian optimization tackles redundant objectives in expensive black-box functions.
We propose a new neural sequence model training method in which the objective function is defined by -divergence. We demonstrate that the objective function generalizes the maximum-likelihood (ML)-based and reinforcement learning (RL)-based objective functions as special cases (i.e., ML corresponds to and R…
Cost-aware multi-objective Bayesian optimization for non-uniformly expensive functions.
Simulated annealing improves candidate optimization for multi-objective Bayesian optimization.
New framework converts multi-objective to single-objective optimisation.
BOtied optimizes multiple objectives using copulas and CDF indicators.
Generative adversarial networks (GAN) approximate a target data distribution by jointly optimizing an objective function through a "two-player game" between a generator and a discriminator. Despite their empirical success, however, two very basic questions on how well they can approximate the target distribution remain…
Proposes a method to learn both constraints and objective functions from data.
The inverse Ising problem seeks to reconstruct the parameters of an Ising Hamiltonian on the basis of spin configurations sampled from the Boltzmann measure. Over the last decade, many applications of the inverse Ising problem have arisen, driven by the advent of large-scale data across different scientific disciplines…
We prove the local convergence to minima and estimates on the rate of convergence for the stochastic gradient descent method in the case of not necessarily globally convex nor contracting objective functions. In particular, the results are applicable to simple objective functions arising in machine learning.
Improves Bayesian optimization by focusing on well-behaved structure in objectives.
Object-based factorizations provide a useful level of abstraction for interacting with the world. Building explicit object representations, however, often requires supervisory signals that are difficult to obtain in practice. We present a paradigm for learning object-centric representations for physical scene understan…
In this paper a new connection between the discrete conformal geometry problem of disk pattern construction and the continuous conformal geometry problem of metric uniformization is presented. In a nutshell, we discuss how to construct disk patterns by optimizing an objective function, which turns out to be intimately …
A new method for multi-objective Bayesian optimization.
This paper studies continuum-armed bandits under Besov smoothness conditions and derives minimax rates.
New approach categorizes objective functions for embodied agents.
Proposes a new objective function to learn robust deep features.
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…
A key drawback of the current generation of artificial decision-makers is that they do not adapt well to changes in unexpected situations. This paper addresses the situation in which an AI for aerial dog fighting, with tunable parameters that govern its behavior, will optimize behavior with respect to an objective func…
Regularized empirical risk minimization with constrained labels (in contrast to fixed labels) is a remarkably general abstraction of learning. For common loss and regularization functions, this optimization problem assumes the form of a mixed integer program (MIP) whose objective function is non-convex. In this form, t…
We present PESMO, a Bayesian method for identifying the Pareto set of multi-objective optimization problems, when the functions are expensive to evaluate. The central idea of PESMO is to choose evaluation points so as to maximally reduce the entropy of the posterior distribution over the Pareto set. Critically, the PES…
Classifies spherical objects filled with water or air using acoustic echoes and Form Function.
Similarity measure for Gaussian process predictive distributions.
Human computation or crowdsourcing involves joint inference of the ground-truth-answers and the worker-abilities by optimizing an objective function, for instance, by maximizing the data likelihood based on an assumed underlying model. A variety of methods have been proposed in the literature to address this inference …
Improved convergence speed of principal component analysis through modified learning rules.
New objective function preserves Bellman's principle for policy gradient.
MOBO-OSD optimizes multi-objective functions using orthogonal search directions.
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…
Object ranking is an important problem in the realm of preference learning. On the basis of training data in the form of a set of rankings of objects, which are typically represented as feature vectors, the goal is to learn a ranking function that predicts a linear order of any new set of objects. Current approaches co…
New tensor kernels reduce mismatch between clustering and reconstruction objectives in deep learning.
Meta-model framework improves efficiency in parameter estimation of dynamical systems.
Given only information in the form of similarity triplets "Object A is more similar to object B than to object C" about a data set, we propose two ways of defining a kernel function on the data set. While previous approaches construct a low-dimensional Euclidean embedding of the data set that reflects the given similar…
Bayesian optimization (BO) is a widely-used method for optimizing expensive (to evaluate) problems. At the core of most BO methods is the modeling of the objective function using a Gaussian Process (GP) whose covariance is selected from a set of standard covariance functions. From a weight-space view, this models the o…
MWGraD solves multi-objective distributional optimization using particle-based gradient descent.
Recently, deep neural network (DNN) has made a breakthrough in monaural source enhancement. Through a training step by using a large amount of data, DNN estimates a mapping between mixed signals and clean signals. At this time, we use an objective function that numerically expresses the quality of a mapping by DNN. In …
A novel Bayesian optimization framework tackles multi-objective constrained problems.
New objective function reduces posterior collapse in generative models.
This paper calculates the exact probability distribution of hypervolume improvement for bi-objective problems.
Bayesian optimization with preference learning identifies preferred solutions in multi-objective problems.
We present a multi-objective Bayesian optimisation algorithm that allows the user to express preference-order constraints on the objectives of the type "objective A is more important than objective B". These preferences are defined based on the stability of the obtained solutions with respect to preferred objective fun…
We consider the problem of learning object arrangements in a 3D scene. The key idea here is to learn how objects relate to human poses based on their affordances, ease of use and reachability. In contrast to modeling object-object relationships, modeling human-object relationships scales linearly in the number of objec…
New deep neural network method improves change point detection.
This article analyzes the weak error of SGD optimization schemes.
New scalarizing functions improve multi-objective Bayesian optimisation.
New objective reduces bias and variance in reinforcement learning derivatives.