Vision models trained on multimodal datasets have recently proved very efficient, both in terms of the wide availability of large image-caption datasets, and in terms of the resulting model's ability to generalize to multiple downstream tasks (e.g. zero-shot learning). One might assume that these abilities are derived, at least in part, from a "semantic grounding" of the visual feature space, learning meaningful structure by mirroring the space of linguistic representations. Contrary to this intuition, we show that a visual model (BiT-M) trained on a very large supervised image dataset (ImageNet-21k) can be as efficient for generalization (few-shot learning, unsupervised clustering) as its multimodal counterpart (CLIP). When compared to other standard visual or language models, the latent representations of BiT-M were found to be just as "linguistic" as those of CLIP. Overall, these findings suggest that the main factor driving improvements of generalization in current models is the size of the training dataset, not (solely) the multimodal grounding property.

Bayesian Optimization (BO) is a successful methodology to tune the hyperparameters of machine learning algorithms. The user defines a metric of interest, such as the validation error, and BO finds the optimal hyperparameters that minimize it. However, the metric improvements on the validation set may not translate to the test set, especially on small datasets. In other words, BO can overfit. While cross-validation mitigates this, it comes with high computational cost. In this paper, we carry out the first systematic investigation of overfitting in BO and demonstrate that this is a serious yet often overlooked concern in practice. We propose the first problem-adaptive and interpretable criterion to early stop BO, reducing overfitting while mitigating the cost of cross-validation. Experimental results on real-world hyperparameter optimization tasks show that our approach can substantially reduce compute time with little to no loss of test accuracy,demonstrating a clear practical advantage over existing techniques.

We propose a novel approach for solving inverse-problems with high-dimensional inputs and an expensive forward mapping. It leverages joint deep generative modelling to transfer the original problem spaces to a lower dimensional latent space. By jointly modelling input and output variables and endowing the latent with a prior distribution, the fitted probabilistic model indirectly gives access to the approximate conditional distributions of interest. Since model error and observational noise with unknown distributions are common in practice, we resort to likelihood-free inference with Approximate Bayesian Computation (ABC). Our method calls on ABC by Subset Simulation to explore the regions of the latent space with dissimilarities between generated and observed outputs below prescribed thresholds. We diagnose the diversity of approximate posterior solutions by monitoring the probability content of these regions as a function of the threshold. We further analyze the curvature of the resulting diagnostic curve to propose an adequate ABC threshold. When applied to a cross-borehole tomography example from geophysics, our approach delivers promising performance without using prior knowledge of the forward nor of the noise distribution.

We propose a novel surrogate-assisted Evolutionary Algorithm for solving expensive combinatorial optimization problems. We integrate a surrogate model, which is used for fitness value estimation, into a state-of-the-art P3-like variant of the Gene-Pool Optimal Mixing Algorithm (GOMEA) and adapt the resulting algorithm for solving non-binary combinatorial problems. We test the proposed algorithm on an ensemble learning problem. Ensembling several models is a common Machine Learning technique to achieve better performance. We consider ensembles of several models trained on disjoint subsets of a dataset. Finding the best dataset partitioning is naturally a combinatorial non-binary optimization problem. Fitness function evaluations can be extremely expensive if complex models, such as Deep Neural Networks, are used as learners in an ensemble. Therefore, the number of fitness function evaluations is typically limited, necessitating expensive optimization techniques. In our experiments we use five classification datasets from the OpenML-CC18 benchmark and Support-vector Machines as learners in an ensemble. The proposed algorithm demonstrates better performance than alternative approaches, including Bayesian optimization algorithms. It manages to find better solutions using just several thousand fitness function evaluations for an ensemble learning problem with up to 500 variables.

Less than four months after the revelation of one of the biggest hacking events in history--Russia's massive breach of thousands of networks that's come to be known as the SolarWinds hack--the US has now sent the Kremlin a message in the form of a punishing package of diplomatic and economic measures. But even as the retribution for SolarWinds becomes clear, the question remains: What exactly is that message? By most any interpretation, it doesn't seem to be based on a rule that the United States has ever spelled out before. On Thursday, the Biden administration fulfilled its repeated promises of retaliation for both the SolarWinds hacking campaign and a broad array of other Russian misbehavior that includes the Kremlin's continuing disinformation operations and other interference in the 2020 election, the poisoning of Putin political adversary Aleksey Navalny, and even older Russian misdeeds including the NotPetya worm and the cyberattack on the 2018 Winter Olympics. The Treasury Department has leveled new sanctions at six cybersecurity companies with purported ties to Russian intelligence services, as well as four organizations associated with its disinformation operations.

The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its ensemble from classifiers of multiple domains, including phase-independent shapelets, bag-of-words based dictionaries and phase-dependent intervals. Since it was first proposed in 2016, the algorithm has remained state of the art for accuracy on the UCR time series classification archive. Over time it has been incrementally updated, culminating in its current state, HIVE-COTE 1.0. During this time a number of algorithms have been proposed which match the accuracy of HIVE-COTE. We propose comprehensive changes to the HIVE-COTE algorithm which significantly improve its accuracy and usability, presenting this upgrade as HIVE-COTE 2.0. We introduce two novel classifiers, the Temporal Dictionary Ensemble (TDE) and Diverse Representation Canonical Interval Forest (DrCIF), which replace existing ensemble members. Additionally, we introduce the Arsenal, an ensemble of ROCKET classifiers as a new HIVE-COTE 2.0 constituent. We demonstrate that HIVE-COTE 2.0 is significantly more accurate than the current state of the art on 112 univariate UCR archive datasets and 26 multivariate UEA archive datasets.

Generalised Bayesian inference updates prior beliefs using a loss function, rather than a likelihood, and can therefore be used to confer robustness against possible misspecification of the likelihood. Here we consider generalised Bayesian inference with a Stein discrepancy as a loss function, motivated by applications in which the likelihood contains an intractable normalisation constant. In this context, the Stein discrepancy circumvents evaluation of the normalisation constant and produces generalised posteriors that are either closed form or accessible using standard Markov chain Monte Carlo. On a theoretical level, we show consistency, asymptotic normality, and bias-robustness of the generalised posterior, highlighting how these properties are impacted by the choice of Stein discrepancy. Then, we provide numerical experiments on a range of intractable distributions, including applications to kernel-based exponential family models and non-Gaussian graphical models.

We consider the problem of jointly modeling and clustering populations of tensors by introducing a flexible high-dimensional tensor mixture model with heterogeneous covariances. The proposed mixture model exploits the intrinsic structures of tensor data, and is assumed to have means that are low-rank and internally sparse as well as heterogeneous covariances that are separable and conditionally sparse. We develop an efficient high-dimensional expectation-conditional-maximization (HECM) algorithm that breaks the challenging optimization in the M-step into several simpler conditional optimization problems, each of which is convex, admits regularization and has closed-form updating formulas. We show that the proposed HECM algorithm, with an appropriate initialization, converges geometrically to a neighborhood that is within statistical precision of the true parameter. Such a theoretical analysis is highly nontrivial due to the dual non-convexity arising from both the EM-type estimation and the non-convex objective function in the M-step. The efficacy of our proposed method is demonstrated through simulation studies and an application to an autism spectrum disorder study, where our analysis identifies important brain regions for diagnosis.

Topological data analysis (TDA) studies the shape patterns of data. Persistent homology (PH) is a widely used method in TDA that summarizes homological features of data at multiple scales and stores this in persistence diagrams (PDs). As TDA is commonly used in the analysis of high dimensional data sets, a sufficiently large amount of PDs that allow performing statistical analysis is typically unavailable or requires inordinate computational resources. In this paper, we propose random persistence diagram generation (RPDG), a method that generates a sequence of random PDs from the ones produced by the data. RPDG is underpinned (i) by a parametric model based on pairwise interacting point processes for inference of persistence diagrams and (ii) by a reversible jump Markov chain Monte Carlo (RJ-MCMC) algorithm for generating samples of PDs. The parametric model combines a Dirichlet partition to capture spatial homogeneity of the location of points in a PD and a step function to capture the pairwise interaction between them. The RJ-MCMC algorithm incorporates trans-dimensional addition and removal of points and same-dimensional relocation of points across samples of PDs. The efficacy of RPDG is demonstrated via an example and a detailed comparison with other existing methods is presented.

Representation learning has significantly been developed with the advance of contrastive learning methods. Most of those methods have benefited from various data augmentations that are carefully designated to maintain their identities so that the images transformed from the same instance can still be retrieved. However, those carefully designed transformations limited us to further explore the novel patterns exposed by other transformations. Meanwhile, as found in our experiments, the strong augmentations distorted the images' structures, resulting in difficult retrieval. Thus, we propose a general framework called Contrastive Learning with Stronger Augmentations~(CLSA) to complement current contrastive learning approaches. Here, the distribution divergence between the weakly and strongly augmented images over the representation bank is adopted to supervise the retrieval of strongly augmented queries from a pool of instances. Experiments on the ImageNet dataset and downstream datasets showed the information from the strongly augmented images can significantly boost the performance. For example, CLSA achieves top-1 accuracy of 76.2% on ImageNet with a standard ResNet-50 architecture with a single-layer classifier fine-tuned, which is almost the same level as 76.5% of supervised results. The code and pre-trained models are available in https://github.com/maple-research-lab/CLSA.