Contrastive learning has recently demonstrated superior performance to supervised learning, despite requiring no training labels. We explore how contrastive learning can be applied to hundreds of thousands of unlabeled Mars terrain images, collected from the Mars rovers Curiosity and Perseverance, and from the Mars Reconnaissance Orbiter. Such methods are appealing since the vast majority of Mars images are unlabeled as manual annotation is labor intensive and requires extensive domain knowledge. Contrastive learning, however, assumes that any given pair of distinct images contain distinct semantic content. This is an issue for Mars image datasets, as any two pairs of Mars images are far more likely to be semantically similar due to the lack of visual diversity on the planet's surface. Making the assumption that pairs of images will be in visual contrast - when they are in fact not - results in pairs that are falsely considered as negatives, impacting training performance. In this study, we propose two approaches to resolve this: 1) an unsupervised deep clustering step on the Mars datasets, which identifies clusters of images containing similar semantic content and corrects false negative errors during training, and 2) a simple approach which mixes data from different domains to increase visual diversity of the total training dataset. Both cases reduce the rate of false negative pairs, thus minimizing the rate in which the model is incorrectly penalized during contrastive training. These modified approaches remain fully unsupervised end-to-end. To evaluate their performance, we add a single linear layer trained to generate class predictions based on these contrastively-learned features and demonstrate increased performance compared to supervised models; observing an improvement in classification accuracy of 3.06% using only 10% of the labeled data.

We propose a novel formulation of the triplet objective function that improves metric learning without additional sample mining or overhead costs. Our approach aims to explicitly regularize the distance between the positive and negative samples in a triplet with respect to the anchor-negative distance. As an initial validation, we show that our method (called No Pairs Left Behind [NPLB]) improves upon the traditional and current state-of-the-art triplet objective formulations on standard benchmark datasets. To show the effectiveness and potentials of NPLB on real-world complex data, we evaluate our approach on a large-scale healthcare dataset (UK Biobank), demonstrating that the embeddings learned by our model significantly outperform all other current representations on tested downstream tasks. Additionally, we provide a new model-agnostic single-time health risk definition that, when used in tandem with the learned representations, achieves the most accurate prediction of subjects' future health complications. Our results indicate that NPLB is a simple, yet effective framework for improving existing deep metric learning models, showcasing the potential implications of metric learning in more complex applications, especially in the biological and healthcare domains. Metric learning is the task of encoding similarity-based embeddings where similar samples are mapped closer in space and dissimilar ones afar (Xing et al., 2002; Wang et al., 2019; Roth et al., 2020). Deep metric learning (DML) has shown success in many domains, including computer vision (Hermans et al., 2017; Vinyals et al., 2016; Wang et al., 2018b) and natural language processing (Reimers & Gurevych, 2019; Mueller & Thyagarajan, 2016; Benajiba et al., 2019). Many DML models utilize paired samples to learn useful embeddings based on distance comparisons. The most common architectures among these techniques are the Siamese (Bromley et al., 1993) and triplet networks (Hoffer & Ailon, 2015). The main components of these models are the: (1) Strategies for constructing training tuples and (2) objectives that the model must minimize. Though many studies have focused on improving sampling strategies (Wu et al., 2017; Ge, 2018; Shrivastava et al., 2016; Kalantidis et al., 2020; Zhu et al., 2021), modifying the objective function has attracted less attention.

A burgeoning paradigm in algorithm design is the field of algorithms with predictions, in which algorithms can take advantage of a possibly-imperfect prediction of some aspect of the problem. While much work has focused on using predictions to improve competitive ratios, running times, or other performance measures, less effort has been devoted to the question of how to obtain the predictions themselves, especially in the critical online setting. We introduce a general design approach for algorithms that learn predictors: (1) identify a functional dependence of the performance measure on the prediction quality and (2) apply techniques from online learning to learn predictors, tune robustness-consistency trade-offs, and bound the sample complexity. We demonstrate the effectiveness of our approach by applying it to bipartite matching, ski-rental, page migration, and job scheduling. In several settings we improve upon multiple existing results while utilizing a much simpler analysis, while in the others we provide the first learning-theoretic guarantees.

Decision tree models such as CART (Breiman et al., 1984) and their ensembles such as Random Forests (Breiman, 2001) and Gradient Boosted Trees (Chen & Guestrin, 2016; Friedman, 2001) have proved to be enormously successful supervised learning algorithms, because they are able to combine non-parametric model fitting with implicit dimension reduction. It is often difficult to quantify the uncertainty of their predictions and due to their greedy local splitting criteria, there is no guarantee for the optimality of the constructed decision trees. An alternative approach is to construct the decision trees in a Bayesian manner (H. A. Chipman et al., 1998; Denison et al., 1998; Wu et al., 2007) To address these issues, H. A. Chipman et al., 1998 proposed a Bayesian adaptation of CART, Bayesian CART, and later, a sum of Bayesian CART trees, which they called Bayesian Additive Regression Trees (BART) (H. A. Chipman et al., 2010). One perspective views these algorithms as non-greedy stochastic versions of their deterministic equivalents, where the randomness inside the fitting process allows the algorithm to explore the space of possible decision trees in ways the CART algorithm cannot. An alternative perspective views these algorithms as Bayesian non-parametric regression models, in which we put a prior on the space of decision trees, assume a likelihood for the observed data, and then obtain a posterior distribution over the possible decision trees based on the training data. The posterior distribution can be used to provide posterior predictive credible intervals and other forms of uncertainty quantification.

Prior work on differential privacy analysis of randomized SGD algorithms relies on composition theorems, where the implicit (unrealistic) assumption is that the internal state of the iterative algorithm is revealed to the adversary. As a result, the R\'enyi DP bounds derived by such composition-based analyses linearly grow with the number of training epochs. When the internal state of the algorithm is hidden, we prove a converging privacy bound for noisy stochastic gradient descent (on strongly convex smooth loss functions). We show how to take advantage of privacy amplification by sub-sampling and randomized post-processing, and prove the dynamics of privacy bound for "shuffle and partition" and "sample without replacement" stochastic mini-batch gradient descent schemes. We prove that, in these settings, our privacy bound converges exponentially fast and is substantially smaller than the composition bounds, notably after a few number of training epochs. Thus, unless the DP algorithm converges fast, our privacy analysis shows that hidden state analysis can significantly amplify differential privacy.

Load forecasts have become an integral part of energy security. Due to the various influencing factors that can be considered in such a forecast, there is also a wide range of models that attempt to integrate these parameters into a system in various ways. Due to the growing importance of probabilistic load forecast models, different approaches are presented in this analysis. The focus is on different models from the short-term sector. After that, another model from the long-term sector is presented. Then, the presented models are put in relation to each other and examined with reference to advantages and disadvantages. Afterwards, the presented papers are analyzed with focus on their comparability to each other. Finally, an outlook on further areas of development in the literature will be discussed.

This paper introduces the methods and the results of AIM 2022 challenge on Instagram Filter Removal. Social media filters transform the images by consecutive non-linear operations, and the feature maps of the original content may be interpolated into a different domain. This reduces the overall performance of the recent deep learning strategies. The main goal of this challenge is to produce realistic and visually plausible images where the impact of the filters applied is mitigated while preserving the content. The proposed solutions are ranked in terms of the PSNR value with respect to the original images. There are two prior studies on this task as the baseline, and a total of 9 teams have competed in the final phase of the challenge. The comparison of qualitative results of the proposed solutions and the benchmark for the challenge are presented in this report.

Video streams tremendously occupied the highest portion of online traffic. Multiple versions of a video are created to fit the user's device specifications. In cloud storage, Keeping all versions of frequently accessed video streams in the repository for the long term imposes a significant cost paid by video streaming providers. Generally, the popularity of a video changes each period of time, which means the number of views received by a video could be dropped, thus, the video must be deleted from the repository. Therefore, in this paper, we develop a method that predicts the popularity of each video stream in the repository in the next period. On the other hand, we propose an algorithm that utilizes the predicted popularity of a video to compute the storage cost, and then it decides whether the video will be kept or deleted from the cloud repository. The experiment results show a cost reduction of the cloud services by 15% compared to keeping all video streams.

This article presents a new design for autonomous artificial intelligence (AI), based on the state-of-the-art algorithms, and describes a new autonomous AI system called AutoAI. The methodology is used to assemble the design founded on self-improved algorithms that use new and emerging sources of data (NEFD). The objective of the article is to conceptualise the design of a novel AutoAI algorithm. The conceptual approach is used to advance into building new and improved algorithms. The article integrates and consolidates the findings from existing literature and advances the AutoAI design into (1) using new and emerging sources of data for teaching and training AI algorithms and (2) enabling AI algorithms to use automated tools for training new and improved algorithms. This approach is going beyond the state-of-the-art in AI algorithms and suggests a design that enables autonomous algorithms to self-optimise and self-adapt, and on a higher level, be capable to self-procreate.

Random matrix theory has become a widely useful tool in high-dimensional statistics and theoretical machine learning. However, random matrix theory is largely focused on the proportional asymptotics in which the number of columns grows proportionally to the number of rows of the data matrix. This is not always the most natural setting in statistics where columns correspond to covariates and rows to samples. With the objective to move beyond the proportional asymptotics, we revisit ridge regression ($\ell_2$-penalized least squares) on i.i.d. data $(x_i, y_i)$, $i\le n$, where $x_i$ is a feature vector and $y_i = \beta^\top x_i +\epsilon_i \in\mathbb{R}$ is a response. We allow the feature vector to be high-dimensional, or even infinite-dimensional, in which case it belongs to a separable Hilbert space, and assume either $z_i := \Sigma^{-1/2}x_i$ to have i.i.d. entries, or to satisfy a certain convex concentration property. Within this setting, we establish non-asymptotic bounds that approximate the bias and variance of ridge regression in terms of the bias and variance of an `equivalent' sequence model (a regression model with diagonal design matrix). The approximation is up to multiplicative factors bounded by $(1\pm \Delta)$ for some explicitly small $\Delta$. Previously, such an approximation result was known only in the proportional regime and only up to additive errors: in particular, it did not allow to characterize the behavior of the excess risk when this converges to $0$. Our general theory recovers earlier results in the proportional regime (with better error rates). As a new application, we obtain a completely explicit and sharp characterization of ridge regression for Hilbert covariates with regularly varying spectrum. Finally, we analyze the overparametrized near-interpolation setting and obtain sharp `benign overfitting' guarantees.