The paper tackles the problem of predicting the outcome of an action chosen from a set of possible actions, The outcome is a function of the action, having a linear component, non-linear component and some additive noise. The idea is first finding a linear function minimizing the deviation from the outcomes, for every distribution which is "close" to the empirical distribution (by the Wasserstein distance). Idea which was analyzed before. The idea added in the paper is using the resulting linear-regression coefficient to build a metric upon samples from the same group and then produce prediction which is the average of the outcomes for the K-nearest neighbors. This way the prediction can leverage not only the private history of the specific instance but also the outcomes of "close" instances.

Novelty Detection (ND) plays a crucial role in machine learning by identifying new or unseen data during model inference. This capability is especially important for the safe and reliable operation of automated systems. Despite advances in this field, existing techniques often fail to maintain their performance when subject to adversarial attacks. Our research addresses this gap by marrying the merits of nearest-neighbor algorithms with robust features obtained from models pretrained on ImageNet. We focus on enhancing the robustness and performance of ND algorithms. Experimental results demonstrate that our approach significantly outperforms current state-of-the-art methods across various benchmarks, particularly under adversarial conditions. By incorporating robust pretrained features into the k-NN algorithm, we establish a new standard for performance and robustness in the field of robust ND. This work opens up new avenues for research aimed at fortifying machine learning systems against adversarial vulnerabilities. Our implementation is publicly available at https://github.com/rohban-lab/ZARND.

Hydrodynamic pattern formation phenomena in printing and coating processes are still not fully understood. However, fundamental understanding is essential to achieve high-quality printed products and to tune printed patterns according to the needs of a specific application like printed electronics, graphical printing, or biomedical printing. The aim of the paper is to develop an automated pattern classification algorithm based on methods from supervised machine learning and reduced-order modeling. We use the HYPA-p dataset, a large image dataset of gravure-printed images, which shows various types of hydrodynamic pattern formation phenomena. It enables the correlation of printing process parameters and resulting printed patterns for the first time. 26880 images of the HYPA-p dataset have been labeled by a human observer as dot patterns, mixed patterns, or finger patterns; 864000 images (97%) are unlabeled. A singular value decomposition (SVD) is used to find the modes of the labeled images and to reduce the dimensionality of the full dataset by truncation and projection. Selected machine learning classification techniques are trained on the reduced-order data. We investigate the effect of several factors, including classifier choice, whether or not fast Fourier transform (FFT) is used to preprocess the labeled images, data balancing, and data normalization. The best performing model is a k-nearest neighbor (kNN) classifier trained on unbalanced, FFT-transformed data with a test error of 3%, which outperforms a human observer by 7%. Data balancing slightly increases the test error of the kNN-model to 5%, but also increases the recall of the mixed class from 90% to 94%. Finally, we demonstrate how the trained models can be used to predict the pattern class of unlabeled images and how the predictions can be correlated to the printing process parameters, in the form of regime maps.

Missing feature values are a significant hurdle for downstream machine-learning tasks such as classification and regression. However, they are pervasive in multiple real-life use cases, for instance, in drug discovery research. Moreover, imputation methods might be time-consuming and offer few guarantees on the imputation quality, especially for not-missing-at-random mechanisms. We propose an imputation approach named F3I based on the iterative improvement of a K-nearest neighbor imputation that learns the weights for each neighbor of a data point, optimizing for the most likely distribution of points over data points. This algorithm can also be jointly trained with a downstream task on the imputed values. We provide a theoretical analysis of the imputation quality by F3I for several types of missing mechanisms. We also demonstrate the performance of F3I on both synthetic data sets and real-life drug repurposing and handwritten-digit recognition data.

There are some issues with the conditions imposed on distributions in this paper. In particular, regarding the local bound hypothesis: in the proof of Lemma 2 epsilon may depend on x, so p * will be smaller than stated. As an example, if p(x) goes to 0 as x a when x goes to 0, then p *(x) goes to 0 as x (a 1). AFTER REBUTTAL: 1) The rebuttal here is helpful, but there seem to still be some issues with the local lower bounds hypothesis. They claim that it is very mild in light of Lemma 2, but Lemma 2 doesn't apply very well to the situation of p x a. I think that they should at least include what they've written in the rebuttal.

The mathematical derivation in this work is novel and interesting, but the writing can be improved. The derivation uses Hoeffding's inequality for noise on labels, with a mild assumption on the maximum noise. The analysis using KKT condition is straightforward, and it results in an interesting strategy for finding weights. I want to support the acceptance of this paper, but I have several concerns: 1) According to the derivation, the upper bound of the risk (P1) is given as C\lambda (line 112). With some simple calculations using appropriate parameters, this upper bound does not give a small value.

I am not qualified to evaluate this work in term of its relevance within the literature. Therefore my judgment is only about the paper content itself. Also, I have only reviewed the proofs contained in the main paper the one of Lemma A.1. Theorem 3.2 guarantees a significant improvement upon the passive learner characterized by 3.1. I find the example in line2 141-143 about the 1/sqrt(m) order very helpful and I suggest the authors to include it in the introduction as well.

Automating analog and radio-frequency (RF) circuit design using machine learning (ML) significantly reduces the time and effort required for parameter optimization. This study explores supervised ML-based approaches for designing circuit parameters from performance specifications across various circuit types, including homogeneous and heterogeneous designs. By evaluating diverse ML models, from neural networks like transformers to traditional methods like random forests, we identify the best-performing models for each circuit. Our results show that simpler circuits, such as low-noise amplifiers, achieve exceptional accuracy with mean relative errors as low as 0.3% due to their linear parameter-performance relationships. In contrast, complex circuits, like power amplifiers and voltage-controlled oscillators, present challenges due to their non-linear interactions and larger design spaces. For heterogeneous circuits, our approach achieves an 88% reduction in errors with increased training data, with the receiver achieving a mean relative error as low as 0.23%, showcasing the scalability and accuracy of the proposed methodology. Additionally, we provide insights into model strengths, with transformers excelling in capturing non-linear mappings and k-nearest neighbors performing robustly in moderately linear parameter spaces, especially in heterogeneous circuits with larger datasets. This work establishes a foundation for extending ML-driven design automation, enabling more efficient and scalable circuit design workflows.

Data pruning, or instance selection, is an important problem in machine learning especially in terms of nearest neighbour classifier. However, in data pruning which speeds up the prediction phase, there is an issue related to the speed and efficiency of the process itself. In response, the study proposes an approach involving transforming the instance selection process into a classification task conducted in a unified meta-feature space where each instance can be classified and assigned to either the "to keep" or "to remove" class. This approach requires training an appropriate meta-classifier, which can be developed based on historical instance selection results from other datasets using reference instance selection methods as a labeling tool. This work proposes constructing the meta-feature space based on properties extracted from the nearest neighbor graph. Experiments conducted on 17 datasets of varying sizes and five reference instance selection methods (ENN, Drop3, ICF, HMN-EI, and CCIS) demonstrate that the proposed solution achieves results comparable to reference instance selection methods while significantly reducing computational complexity. In the proposed approach, the computational complexity of the system depends only on identifying the k-nearest neighbors for each data sample and running the meta-classifier. Additionally, the scaling law turns into the requirement of huge compute power both during training and prediction which is not always applicable in real live scenarios where the compute resources are limited. In that case, both the training data and the prediction model should require small computing resources. Therefore the training set should ensure a possible small size but keep the prediction accuracy of the original training set. This issue is not new, along with its development has been started primarily for the nearest neighbor classifier under the name of instance selection. Thus, already in the late 1960s and early 1970s, algorithms such as Condensed Nearest Neighbor (CNN), Edited Nearest Neighbor (ENN), and many others were developed. The benchmarks of instance selection indicate the Drop3 [3] and ICF [4] algorithms as the most wildly used, which, despite not being new, are characterized by excellent properties in terms of the balance between the prediction accuracy of the kNN algorithm and the reduction of the size of the stored set of prototypes (reduction_rate) [5]. These algorithms are also applicable not only as elements of the learning process for the kNN algorithm (prototype selection as part of the learning process) and hence could also be used as universal algorithms for reducing the size of the training set for any classifier, thereby accelerating the learning process of complex predictive models, the process of finding optimal parameters, etc. Examples of such applications can be found in [6, 7] or in [5].

The real-world deployment of an autonomous driving system requires its components to run on-board and in real-time, including the motion prediction module that predicts the future trajectories of surrounding traffic participants. Existing agent-centric methods have demonstrated outstanding performance on public benchmarks. However, they suffer from high computational overhead and poor scalability as the number of agents to be predicted increases. To address this problem, we introduce the K-nearest neighbor attention with relative pose encoding (KNARPE), a novel attention mechanism allowing the pairwise-relative representation to be used by Transformers. Then, based on KNARPE we present the Heterogeneous Polyline Transformer with Relative pose encoding (HPTR), a hierarchical framework enabling asynchronous token update during the online inference.