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.
Bayesian optimisation is improved by incorporating expert prior through space warping.
Bayesian Optimization with a Prior for the Optimum (BOPrO) improves efficiency and accuracy.
Optimizes expensive experiments by incorporating expert knowledge.
Optimum-statistical collaboration improves black-box optimization efficiency.
πBO augments BO with user beliefs for better hyperparameter optimization.
A single algebraic identity unifies information-theoretic variational results.
Optimizes sampling in continuous domains by adjusting search distribution.
Bayesian optimization stops when a solution is within ε of the optimum with high probability.
Bayesian optimization improves with nonstationary covariance functions.
Bayesian optimization has demonstrated impressive success in finding the optimum input x* and output f* = f(x*) = max f(x) of a black-box function f. In some applications, however, the optimum output f* is known in advance and the goal is to find the corresponding optimum input x*. In this paper, we consider a new sett…
RWR converges to global optimum in certain settings.
OPFython simplifies Optimum-Path Forest for Python users.
We consider the problem of search through comparisons, where a user is presented with two candidate objects and reveals which is closer to her intended target. We study adaptive strategies for finding the target, that require knowledge of rank relationships but not actual distances between objects. We propose a new str…
Optimal transport offers an alternative to maximum likelihood for learning generative autoencoding models. We show that minimizing the p-Wasserstein distance between the generator and the true data distribution is equivalent to the unconstrained min-min optimization of the p-Wasserstein distance between the encoder agg…
Numerous empirical evidence has corroborated that the noise plays a crucial rule in effective and efficient training of neural networks. The theory behind, however, is still largely unknown. This paper studies this fundamental problem through training a simple two-layer convolutional neural network model. Although trai…
New framework tackles deep learning issues like local traps and miscalibration.
Mixed membership factorization is a popular approach for analyzing data sets that have within-sample heterogeneity. In recent years, several algorithms have been developed for mixed membership matrix factorization, but they only guarantee estimates from a local optimum. Here, we derive a global optimization (GOP) algor…
FLOP algorithm speeds up causal structure learning for linear models.
Study uses Bayesian Optimization to analyze noise effects in materials research.
In this paper, a new sequential surrogate-based optimization (SSBO) algorithm is developed, which aims to improve the global search ability and local search efficiency for the global optimization of expensive black-box models. The proposed method involves three basic sub-criteria to infill new samples asynchronously to…
Normalization layers are widely used in deep neural networks to stabilize training. In this paper, we consider the training of convolutional neural networks with gradient descent on a single training example. This optimization problem arises in recent approaches for solving inverse problems such as the deep image prior…
Novel method for high-dimensional BO using CMA to define local regions.
Meta-BO method clusters and learns from prior tasks to optimize heterogeneous functions.
This paper compares three portfolio designs for Indian stocks.
Postprocessing reduces Bayesian optimization steps for global optima.
Residual Network (ResNet) is undoubtedly a milestone in deep learning. ResNet is equipped with shortcut connections between layers, and exhibits efficient training using simple first order algorithms. Despite of the great empirical success, the reason behind is far from being well understood. In this paper, we study a …
Gradient descent with noise converges to a unique optimum in nonconvex matrix factorization.
Community detection in graphs has been the subject of many algorithms. Recent methods want to optimize a modularity function which shows a maximum of relationships within communities and found a minimum of inter-community relations. these algorithms are applied to unipartite, multipartite and directed graphs. However, …
Convolutional Neural Network is known as ConvNet have been extensively used in many complex machine learning tasks. However, hyperparameters optimization is one of a crucial step in developing ConvNet architectures, since the accuracy and performance are reliant on the hyperparameters. This multilayered architecture pa…
New findings on the max margin problem in neural networks.
A new Bayesian framework simplifies stochastic optimization by focusing on key parameters.
Community detection using both graphs and social networks is the focus of many algorithms. Recent methods aimed at optimizing the so-called modularity function proceed by maximizing relations within communities while minimizing inter-community relations. However, given the NP-completeness of the problem, these algorith…
A novel regularizer of the PARAFAC decomposition factors capturing the tensor's rank is proposed in this paper, as the key enabler for completion of three-way data arrays with missing entries. Set in a Bayesian framework, the tensor completion method incorporates prior information to enhance its smoothing and predictio…
MINTS uses a minimalist Bayesian framework to tackle multi-armed bandits with structural constraints.
New algorithms improve likelihood of finding global optima in Bayesian inference.
Contemporary global optimization algorithms are based on local measures of utility, rather than a probability measure over location and value of the optimum. They thus attempt to collect low function values, not to learn about the optimum. The reason for the absence of probabilistic global optimizers is that the corres…
A new framework for performative prediction robust to distributional misspecification.
This paper analyzes popular time-nonseparable utility functions that describe "habit formation" consumer preferences comparing current consumption with the time averaged past consumption of the same individual and "catching up with the Joneses" (CuJ) models comparing individual consumption with a cross-sectional averag…
Paper explores how uncertainty quantification improves Transformer's in-context learning ability.
In linear regression we wish to estimate the optimum linear least squares predictor for a distribution over -dimensional input points and real-valued responses, based on a small sample. Under standard random design analysis, where the sample is drawn i.i.d. from the input distribution, the least squares solution for…
Enhanced aerodynamic design using machine learning and Gaussian processes.
Proposes qPO, a new acquisition strategy for batched Bayesian optimization that maximizes the probability of including the optimum.
Exact causal network discovery is polynomial for sparse networks.
We propose an optimum mechanism for providing monetary incentives to the data sources of a statistical estimator such as linear regression, so that high quality data is provided at low cost, in the sense that the sum of payments and estimation error is minimized. The mechanism applies to a broad range of estimators, in…
Variational inference is a very efficient and popular heuristic used in various forms in the context of latent variable models. It's closely related to Expectation Maximization (EM), and is applied when exact EM is computationally infeasible. Despite being immensely popular, current theoretical understanding of the eff…
In online learning, the dynamic regret metric chooses the reference (optimal) solution that may change over time, while the typical (static) regret metric assumes the reference solution to be constant over the whole time horizon. The dynamic regret metric is particularly interesting for applications such as online reco…
New framework for consistent submodular maximization with insertions and deletions.
SOLO uses DNN to optimize complex topology problems with reduced FEM calculations.