An official website of the United States government
Several machine learning methods leverage the idea of locality by using k-nearest neighbor (KNN) techniques to design better pattern recognition models. However, the choice of KNN parameters such as k is often made experimentally, e.g., via cross-validation, leading to local neighborhoods without a clear geometric interpretation. In this paper, we replace KNN with our recently introduced polytope neighborhood scheme - Non Negative Kernel regression (NNK). NNK formulates neighborhood selection as a sparse signal approximation problem and is adaptive to the local distribution of samples in the neighborhood of the data point of interest. We analyze the benefits of local neighborhood construction based on NNK. In particular, we study the generalization properties of local interpolation using NNK and present data dependent bounds in the non asymptotic setting. The applicability of NNK in transductive few shot learning setting and for measuring distance between two datasets is demonstrated. NNK exhibits robust, superior performance in comparison to standard locally weighted neighborhood methods.
more »
« less
Data-Driven Optimal Transport Cost Selection For Distributionally Robust Optimization
Some recent works showed that several machine learning algorithms, such as square-root Lasso, Support Vector Machines, and regularized logistic regression, among many others, can be represented exactly as distributionally robust optimization (DRO) problems. The distributional uncertainty set is defined as a neighborhood centered at the empirical distribution, and the neighborhood is measured by optimal transport distance. In this paper, we propose a methodology which learns such neighborhood in a natural data-driven way. We show rigorously that our framework encompasses adaptive regularization as a particular case. Moreover, we demonstrate empirically that our proposed methodology is able to improve upon a wide range of popular machine learning estimators.
more »
« less
- PAR ID:
- 10175309
- Date Published:
- Journal Name:
- 2019 Winter Simulation Conference
- Page Range / eLocation ID:
- 3740 to 3751
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Not AvailableMachine learning (ML) is increasingly used in high-stakes areas like autonomous driving, finance, and criminal justice. However, it often unintentionally perpetuates biases against marginalized groups. To address this, the software engineering community has developed fairness testing and debugging methods, establishing best practices for fair ML software. These practices focus on training model design, including the selection of sensitive and non-sensitive attributes and hyperparameter configuration. However, the application of these practices across different socio-economic and cultural contexts is challenging, as societal constraints vary. Our study proposes a search-based software engineering approach to evaluate the robustness of these fairness practices. We formulate these practices as the first-order logic properties and search for two neighborhood datasets where the practice satisfies in one dataset, but fail in the other one. Our key observation is that these practices should be general and robust to various uncertainty such as noise, faulty labeling, and demographic shifts. To generate datasets, we sift to the causal graph representations of datasets and apply perturbations over the causal graphs to generate neighborhood datasets. In this short paper, we show our methodology using an example of predicting risks in the car insurance application.more » « less
-
Abstract In this work, we address the question of how to enhance signal-agnostic searches by leveraging multiple testing strategies. Specifically, we consider hypothesis tests relying on machine learning, where model selection can introduce a bias towards specific families of new physics signals. Focusing on the New Physics Learning Machine, a methodology to perform a signal-agnostic likelihood-ratio test, we explore a number of approaches to multiple testing, such as combiningp-values and aggregating test statistics. Our findings show that it is beneficial to combine different tests, characterised by distinct choices of hyperparameters, and that performances comparable to the best available test are generally achieved, while also providing a more uniform response to various types of anomalies. This study proposes a methodology that is valid beyond machine learning approaches and could in principle be applied to a larger class model-agnostic analyses based on hypothesis testing.more » « less
-
Ivrii, Alexander; Strichman, Ofer (Ed.)Artificial Neural Networks (ANNs) have demonstrated remarkable utility in various challenging machine learning applications. While formally verified properties of their behaviors are highly desired, they have proven notoriously difficult to derive and enforce. Existing approaches typically formulate this problem as a post facto analysis process. In this paper, we present a novel learning framework that ensures such formal guarantees are enforced by construction. Our technique enables training provably correct networks with respect to a broad class of safety properties, a capability that goes well-beyond existing approaches, without compromising much accuracy. Our key insight is that we can integrate an optimization-based abstraction refinement loop into the learning process and operate over dynamically constructed partitions of the input space that considers accuracy and safety objectives synergistically. The refinement procedure iteratively splits the input space from which training data is drawn, guided by the efficacy with which such partitions enable safety verification. We have implemented our approach in a tool (ART) and applied it to enforce general safety properties on unmanned aviator collision avoidance system ACAS Xu dataset and the Collision Detection dataset. Importantly, we empirically demonstrate that realizing safety does not come at the price of much accuracy. Our methodology demonstrates that an abstraction refinement methodology provides a meaningful pathway for building both accurate and correct machine learning networks.more » « less
-
Abstract Fractionally doped perovskites oxides (FDPOs) have demonstrated ubiquitous applications such as energy conversion, storage and harvesting, catalysis, sensor, superconductor, ferroelectric, piezoelectric, magnetic, and luminescence. Hence, an accurate, cost-effective, and easy-to-use methodology to discover new compositions is much needed. Here, we developed a function-confined machine learning methodology to discover new FDPOs with high prediction accuracy from limited experimental data. By focusing on a specific application, namely solar thermochemical hydrogen production, we collected 632 training data and defined 21 desirable features. Our gradient boosting classifier model achieved a high prediction accuracy of 95.4% and a high F1 score of 0.921. Furthermore, when verified on additional 36 experimental data from existing literature, the model showed a prediction accuracy of 94.4%. With the help of this machine learning approach, we identified and synthesized 11 new FDPO compositions, 7 of which are relevant for solar thermochemical hydrogen production. We believe this confined machine learning methodology can be used to discover, from limited data, FDPOs with other specific application purposes.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/WSC40007.2019.9004785
