Much of the success of modern machine learning has been measured by improvements in accuracy for various classification tasks. Across domains as diverse as image classification and text translation, machine learning models are achieving incredible levels of accuracy; in some cases, they have outperformed humans in visual recognition tasks (Ewerth et al., 2017). However, accuracy is an aggregate statistic that often obscures the underlying structure of mistaken predictions. Oakden-Rayner et al. (2020) recently raised this concern in the context of medical image analysis. Consider the problem of diagnosing a image as being indicative of lung cancer or not.

We study a stochastic first order primal-dual method for solving convex-concave saddle point problems over real reflexive Banach spaces using Bregman divergences and relative smoothness assumptions, in which we allow for stochastic error in the computation of gradient terms within the algorithm. We show ergodic convergence in expectation of the Lagrangian optimality gap with a rate of O(1/k) and that every almost sure weak cluster point of the ergodic sequence is a saddle point in expectation under mild assumptions. Under slightly stricter assumptions, we show almost sure weak convergence of the pointwise iterates to a saddle point. Under a relative strong convexity assumption on the objective functions and a total convexity assumption on the entropies of the Bregman divergences, we establish almost sure strong convergence of the pointwise iterates to a saddle point. Our framework is general and does not need strong convexity of the entropies inducing the Bregman divergences in the algorithm. Numerical applications are considered including entropically regularized Wasserstein barycenter problems and regularized inverse problems on the simplex.

The malware has been being one of the most damaging threats to computers that span across multiple operating systems and various file formats. To defend against the ever-increasing and ever-evolving threats of malware, tremendous efforts have been made to propose a variety of malware detection methods that attempt to effectively and efficiently detect malware. Recent studies have shown that, on the one hand, existing ML and DL enable the superior detection of newly emerging and previously unseen malware. However, on the other hand, ML and DL models are inherently vulnerable to adversarial attacks in the form of adversarial examples, which are maliciously generated by slightly and carefully perturbing the legitimate inputs to confuse the targeted models. Basically, adversarial attacks are initially extensively studied in the domain of computer vision, and some quickly expanded to other domains, including NLP, speech recognition and even malware detection. In this paper, we focus on malware with the file format of portable executable (PE) in the family of Windows operating systems, namely Windows PE malware, as a representative case to study the adversarial attack methods in such adversarial settings. To be specific, we start by first outlining the general learning framework of Windows PE malware detection based on ML/DL and subsequently highlighting three unique challenges of performing adversarial attacks in the context of PE malware. We then conduct a comprehensive and systematic review to categorize the state-of-the-art adversarial attacks against PE malware detection, as well as corresponding defenses to increase the robustness of PE malware detection. We conclude the paper by first presenting other related attacks against Windows PE malware detection beyond the adversarial attacks and then shedding light on future research directions and opportunities.

Multi-Modal Self-Supervised Learning from videos has been shown to improve model's performance on various downstream tasks. However, such Self-Supervised pre-training requires large batch sizes and a large amount of computation resources due to the noise present in the uncurated data. This is partly due to the fact that the prevalent training scheme is trained on coarse-grained setting, in which vectors representing the whole video clips or natural language sentences are used for computing similarity. Such scheme makes training noisy as part of the video clips can be totally not correlated with the other-modality input such as text description. In this paper, we propose a fine-grained multi-modal self-supervised training scheme that computes the similarity between embeddings at finer-scale (such as individual feature map embeddings and embeddings of phrases), and uses attention mechanisms to reduce noisy pairs' weighting in the loss function. We show that with the proposed pre-training scheme, we can train smaller models, with smaller batch-size and much less computational resources to achieve downstream tasks performances comparable to State-Of-The-Art, for tasks including action recognition and text-image retrievals.

Graph Neural Networks (GNNs) traditionally exhibit poor performance for directed graphs (digraphs) due to notable challenges in 1) modeling neighborhoods and 2) preserving asymmetry. In this paper, we address these challenges in traditional GNNs by leveraging hyperbolic collaborative learning from multi-ordered and partitioned neighborhoods, and regularizers inspired by socio-psychological factors. Our resulting formalism, Digraph Hyperbolic Network (D-HYPR) learns node representations in hyperbolic space to avoid structural and semantic distortion of real-world digraphs. We conduct comprehensive experimentation on 4 tasks: link prediction, node classification, sign prediction, and embedding visualization. D-HYPR statistically significantly outperforms the current state of the art on a majority of tasks and datasets, while achieving competitive performance otherwise. Our code and data will be available.

An AI agent should be able to coordinate with humans to solve tasks. We consider the problem of training a Reinforcement Learning (RL) agent without using any human data, i.e., in a zero-shot setting, to make it capable of collaborating with humans. Standard RL agents learn through self-play. Unfortunately, these agents only know how to collaborate with themselves and normally do not perform well with unseen partners, such as humans. The methodology of how to train a robust agent in a zero-shot fashion is still subject to research. Motivated from the maximum entropy RL, we derive a centralized population entropy objective to facilitate learning of a diverse population of agents, which is later used to train a robust agent to collaborate with unseen partners. The proposed method shows its effectiveness compared to baseline methods, including self-play PPO, the standard Population-Based Training (PBT), and trajectory diversity-based PBT, in the popular Overcooked game environment. We also conduct online experiments with real humans and further demonstrate the efficacy of the method in the real world. A supplementary video showing experimental results is available at https://youtu.be/Xh-FKD0AAKE.

You'll often hear people say that MCMC is too slow for big datasets. For the very biggest datasets with millions of observations, there may be some truth to that. But the developers of PyMC and Stan are constantly refining their samplers, and it's now possible to fit models to much bigger datasets than you might think. In particular, since the PyMC developers introduced it a bit over a year ago, I've been really excited about PyMC's JAX support. I've been a fan of JAX for years and think that it's a great platform for Bayesian modelling.

The HuBERT model architecture follows the wav2vec 2.0 architecture consisting of: The number of each of these components varies between the base, large and x-large variations. Each component and its task will be better explained while explaining the training loop. The first training step consists of discovering the hidden units, and the process begins with extracting MFCCs(Mel frequency cepstrum) from the audio waveform. These are raw acoustic features useful for representing speech. Each segment of audio is then passed to the K-means clustering algorithm, and assigned to one of K clusters.

Analyzing our DNA can give us so many insights into not only how our body works for new drug discoveries but medical information for future care. Advancements in sequencing technology have allowed us to sequence genomes cheaply and efficiently. So, DNA sequence data is currently growing at an exponential rate. By analyzing these gene sequences, we will be able to collect medically useful information for future care. Even in asymptomatic patients, genomic sequencing can offer information on genetic variations that potentially cause disease or increase the risk of disease development.

We develop an Explore-Exploit Markov chain Monte Carlo algorithm ($\operatorname{Ex^2MCMC}$) that combines multiple global proposals and local moves. The proposed method is massively parallelizable and extremely computationally efficient. We prove $V$-uniform geometric ergodicity of $\operatorname{Ex^2MCMC}$ under realistic conditions and compute explicit bounds on the mixing rate showing the improvement brought by the multiple global moves. We show that $\operatorname{Ex^2MCMC}$ allows fine-tuning of exploitation (local moves) and exploration (global moves) via a novel approach to proposing dependent global moves. Finally, we develop an adaptive scheme, $\operatorname{FlEx^2MCMC}$, that learns the distribution of global moves using normalizing flows. We illustrate the efficiency of $\operatorname{Ex^2MCMC}$ and its adaptive versions on many classical sampling benchmarks. We also show that these algorithms improve the quality of sampling GANs as energy-based models.