14,353 research outputs found
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent
In this paper, we study differentially private empirical risk minimization
(DP-ERM). It has been shown that the worst-case utility of DP-ERM reduces
polynomially as the dimension increases. This is a major obstacle to privately
learning large machine learning models. In high dimension, it is common for
some model's parameters to carry more information than others. To exploit this,
we propose a differentially private greedy coordinate descent (DP-GCD)
algorithm. At each iteration, DP-GCD privately performs a coordinate-wise
gradient step along the gradients' (approximately) greatest entry. We show
theoretically that DP-GCD can achieve a logarithmic dependence on the dimension
for a wide range of problems by naturally exploiting their structural
properties (such as quasi-sparse solutions). We illustrate this behavior
numerically, both on synthetic and real datasets
A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching
An efficient team is essential for the company to successfully complete new
projects. To solve the team formation problem considering person-job matching
(TFP-PJM), a 0-1 integer programming model is constructed, which considers both
person-job matching and team members' willingness to communicate on team
efficiency, with the person-job matching score calculated using intuitionistic
fuzzy numbers. Then, a reinforcement learning-assisted genetic programming
algorithm (RL-GP) is proposed to enhance the quality of solutions. The RL-GP
adopts the ensemble population strategies. Before the population evolution at
each generation, the agent selects one from four population search modes
according to the information obtained, thus realizing a sound balance of
exploration and exploitation. In addition, surrogate models are used in the
algorithm to evaluate the formation plans generated by individuals, which
speeds up the algorithm learning process. Afterward, a series of comparison
experiments are conducted to verify the overall performance of RL-GP and the
effectiveness of the improved strategies within the algorithm. The
hyper-heuristic rules obtained through efficient learning can be utilized as
decision-making aids when forming project teams. This study reveals the
advantages of reinforcement learning methods, ensemble strategies, and the
surrogate model applied to the GP framework. The diversity and intelligent
selection of search patterns along with fast adaptation evaluation, are
distinct features that enable RL-GP to be deployed in real-world enterprise
environments.Comment: 16 page
Bayesian Optimization with Conformal Prediction Sets
Bayesian optimization is a coherent, ubiquitous approach to decision-making
under uncertainty, with applications including multi-arm bandits, active
learning, and black-box optimization. Bayesian optimization selects decisions
(i.e. objective function queries) with maximal expected utility with respect to
the posterior distribution of a Bayesian model, which quantifies reducible,
epistemic uncertainty about query outcomes. In practice, subjectively
implausible outcomes can occur regularly for two reasons: 1) model
misspecification and 2) covariate shift. Conformal prediction is an uncertainty
quantification method with coverage guarantees even for misspecified models and
a simple mechanism to correct for covariate shift. We propose conformal
Bayesian optimization, which directs queries towards regions of search space
where the model predictions have guaranteed validity, and investigate its
behavior on a suite of black-box optimization tasks and tabular ranking tasks.
In many cases we find that query coverage can be significantly improved without
harming sample-efficiency.Comment: For code, see
https://www.github.com/samuelstanton/conformal-bayesopt.gi
Convergence Rate of Nonconvex Douglas-Rachford splitting via merit functions, with applications to weakly convex constrained optimization
We analyze Douglas-Rachford splitting techniques applied to solving weakly
convex optimization problems. Under mild regularity assumptions, and by the
token of a suitable merit function, we show convergence to critical points and
local linear rates of convergence. The merit function, comparable to the Moreau
envelope in Variational Analysis, generates a descent sequence, a feature that
allows us to extend to the non-convex setting arguments employed in convex
optimization. A by-product of our approach is a ADMM-like method for
constrained problems with weakly convex objective functions. When specialized
to multistage stochastic programming, the proposal yields a nonconvex version
of the Progressive Hedging algorithm that converges with linear speed. The
numerical assessment on a battery of phase retrieval problems shows promising
numerical performance of our method, when compared to existing algorithms in
the literature.Comment: 24 pages, 1 figur
Model Diagnostics meets Forecast Evaluation: Goodness-of-Fit, Calibration, and Related Topics
Principled forecast evaluation and model diagnostics are vital in fitting probabilistic models and forecasting outcomes of interest. A common principle is that fitted or predicted distributions ought to be calibrated, ideally in the sense that the outcome is indistinguishable from a random draw from the posited distribution. Much of this thesis is centered on calibration properties of various types of forecasts.
In the first part of the thesis, a simple algorithm for exact multinomial goodness-of-fit tests is proposed. The algorithm computes exact -values based on various test statistics, such as the log-likelihood ratio and Pearson\u27s chi-square. A thorough analysis shows improvement on extant methods. However, the runtime of the algorithm grows exponentially in the number of categories and hence its use is limited.
In the second part, a framework rooted in probability theory is developed, which gives rise to hierarchies of calibration, and applies to both predictive distributions and stand-alone point forecasts. Based on a general notion of conditional T-calibration, the thesis introduces population versions of T-reliability diagrams and revisits a score decomposition into measures of miscalibration, discrimination, and uncertainty. Stable and efficient estimators of T-reliability diagrams and score components arise via nonparametric isotonic regression and the pool-adjacent-violators algorithm. For in-sample model diagnostics, a universal coefficient of determination is introduced that nests and reinterprets the classical in least squares regression.
In the third part, probabilistic top lists are proposed as a novel type of prediction in classification, which bridges the gap between single-class predictions and predictive distributions. The probabilistic top list functional is elicited by strictly consistent evaluation metrics, based on symmetric proper scoring rules, which admit comparison of various types of predictions
Transfer learning for operator selection: A reinforcement learning approach
In the past two decades, metaheuristic optimisation algorithms (MOAs) have been increasingly popular, particularly in logistic, science, and engineering problems. The fundamental characteristics of such algorithms are that they are dependent on a parameter or a strategy. Some online and offline strategies are employed in order to obtain optimal configurations of the algorithms. Adaptive operator selection is one of them, and it determines whether or not to update a strategy from the strategy pool during the search process. In the field of machine learning, Reinforcement Learning (RL) refers to goal-oriented algorithms, which learn from the environment how to achieve a goal. On MOAs, reinforcement learning has been utilised to control the operator selection process. However, existing research fails to show that learned information may be transferred from one problem-solving procedure to another. The primary goal of the proposed research is to determine the impact of transfer learning on RL and MOAs. As a test problem, a set union knapsack problem with 30 separate benchmark problem instances is used. The results are statistically compared in depth. The learning process, according to the findings, improved the convergence speed while significantly reducing the CPU time
Life-Cycle Portfolio Choice with Stock Market Loss Framing: Explaining the Empirical Evidence
We develop a life-cycle model with optimal consumption, portfolio choice, and flexible work hours for households with loss-framing preferences giving them disutility if they experience losses from stock investments. Structural estimation using U.S. data shows that the model tracks the empirical age-pattern of stock market participants’ financial wealth, stock shares, and work hours remarkably well. Including stock market participation costs in the model allows us to also predict low stock market participations rates observed in the overall population. Allowing for heterogeneous agents further improves explanatory power and accounts for the observed discrepancy in wealth accumulation between stockholders and non-stockholders
Cost-effective non-destructive testing of biomedical components fabricated using additive manufacturing
Biocompatible titanium-alloys can be used to fabricate patient-specific medical components using additive manufacturing (AM). These novel components have the potential to improve clinical outcomes in various medical scenarios. However, AM introduces stability and repeatability concerns, which are potential roadblocks for its widespread use in the medical sector. Micro-CT imaging for non-destructive testing (NDT) is an effective solution for post-manufacturing quality control of these components. Unfortunately, current micro-CT NDT scanners require expensive infrastructure and hardware, which translates into prohibitively expensive routine NDT. Furthermore, the limited dynamic-range of these scanners can cause severe image artifacts that may compromise the diagnostic value of the non-destructive test. Finally, the cone-beam geometry of these scanners makes them susceptible to the adverse effects of scattered radiation, which is another source of artifacts in micro-CT imaging.
In this work, we describe the design, fabrication, and implementation of a dedicated, cost-effective micro-CT scanner for NDT of AM-fabricated biomedical components. Our scanner reduces the limitations of costly image-based NDT by optimizing the scanner\u27s geometry and the image acquisition hardware (i.e., X-ray source and detector). Additionally, we describe two novel techniques to reduce image artifacts caused by photon-starvation and scatter radiation in cone-beam micro-CT imaging.
Our cost-effective scanner was designed to match the image requirements of medium-size titanium-alloy medical components. We optimized the image acquisition hardware by using an 80 kVp low-cost portable X-ray unit and developing a low-cost lens-coupled X-ray detector. Image artifacts caused by photon-starvation were reduced by implementing dual-exposure high-dynamic-range radiography. For scatter mitigation, we describe the design, manufacturing, and testing of a large-area, highly-focused, two-dimensional, anti-scatter grid.
Our results demonstrate that cost-effective NDT using low-cost equipment is feasible for medium-sized, titanium-alloy, AM-fabricated medical components. Our proposed high-dynamic-range strategy improved by 37% the penetration capabilities of an 80 kVp micro-CT imaging system for a total x-ray path length of 19.8 mm. Finally, our novel anti-scatter grid provided a 65% improvement in CT number accuracy and a 48% improvement in low-contrast visualization. Our proposed cost-effective scanner and artifact reduction strategies have the potential to improve patient care by accelerating the widespread use of patient-specific, bio-compatible, AM-manufactured, medical components
Annals [...].
Pedometrics: innovation in tropics; Legacy data: how turn it useful?; Advances in soil sensing; Pedometric guidelines to systematic soil surveys.Evento online. Coordenado por: Waldir de Carvalho Junior, Helena Saraiva Koenow Pinheiro, Ricardo SimĂŁo Diniz Dalmolin
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