The ocean is a crucial component of the Earth's system. It profoundly influences human activities and plays a critical role in climate regulation. Our understanding has significantly improved over the last decades with the advent of satellite remote sensing data, allowing us to capture essential sea surface quantities over the globe, e.g., sea surface height (SSH). Despite their ever-increasing abundance, ocean satellite data presents challenges for information extraction due to their sparsity and irregular sampling, signal complexity, and noise. Machine learning (ML) techniques have demonstrated their capabilities in dealing with large-scale, complex signals.

Machine Learning ML agents have been increasingly used in decision-making across a wide range of tasks and environments. These ML agents are typically designed to balance multiple objectives when making choices. Understanding how their decision-making processes align with or diverge from human reasoning is essential. Human agents often encounter hard choices, that is, situations where options are incommensurable; neither option is preferred, yet the agent is not indifferent between them. In such cases, human agents can identify hard choices and resolve them through deliberation. In contrast, current ML agents, due to fundamental limitations in Multi-Objective Optimisation or MOO methods, cannot identify hard choices, let alone resolve them. Neither Scalarised Optimisation nor Pareto Optimisation, the two principal MOO approaches, can capture incommensurability. This limitation generates three distinct alignment problems: the alienness of ML decision-making behaviour from a human perspective; the unreliability of preference-based alignment strategies for hard choices; and the blockage of alignment strategies pursuing multiple objectives. Evaluating two potential technical solutions, I recommend an ensemble solution that appears most promising for enabling ML agents to identify hard choices and mitigate alignment problems. However, no known technique allows ML agents to resolve hard choices through deliberation, as they cannot autonomously change their goals. This underscores the distinctiveness of human agency and urges ML researchers to reconceptualise machine autonomy and develop frameworks and methods that can better address this fundamental gap.

Missing data is prevalent in tabular machine learning (ML) models, and different missing data treatment methods can significantly affect ML model training results. However, little is known about how ML researchers and engineers choose missing data treatment methods and what factors affect their choices. To this end, we conducted a survey of 70 ML researchers and engineers. Our results revealed that most participants were not making informed decisions regarding missing data treatment, which could significantly affect the validity of the ML models trained by these researchers. We advocate for better education on missing data, more standardized missing data reporting, and better missing data analysis tools.

Weaknesses: My primary concern with this paper is that the problem it is addressing is *extremely* niche --- Modulo cameras are a somewhat obscure problem even within the realm of the computational imaging community. If I was reviewing this paper for a computational imaging/photography conference, I would be more charitable towards this paper. But this subject is unlikely to be of interest to the general NeurIPS audience, and this paper seems unlikely to reach its intended audience if presented at NeurIPS. And the specifics of this neural network architecture are so specifically tailored to this particular problem that I'm not sure what a general ML researcher could come away from this paper with, nor am I convinced that this is a problem that should be popularized with ML researchers as, again, a solution to this problem has limited practical value given that modulo cameras are still a largely hypothetical concept. My other concern with this paper (which would be a significant concern even if I were reviewing this paper in a computational imaging conference) is that the baseline evaluation is misleading.

The ocean is a crucial component of the Earth's system. It profoundly influences human activities and plays a critical role in climate regulation. Our understanding has significantly improved over the last decades with the advent of satellite remote sensing data, allowing us to capture essential sea surface quantities over the globe, e.g., sea surface height (SSH). Despite their ever-increasing abundance, ocean satellite data presents challenges for information extraction due to their sparsity and irregular sampling, signal complexity, and noise. Machine learning (ML) techniques have demonstrated their capabilities in dealing with large-scale, complex signals.

Machine learning models often learn latent embedding representations that capture the domain semantics of their training data. These embedding representations are valuable for interpreting trained models, building new models, and analyzing new datasets. However, interpreting and using embeddings can be challenging due to their opaqueness, high dimensionality, and the large size of modern datasets. To tackle these challenges, we present WizMap, an interactive visualization tool to help researchers and practitioners easily explore large embeddings. With a novel multi-resolution embedding summarization method and a familiar map-like interaction design, WizMap enables users to navigate and interpret embedding spaces with ease. Leveraging modern web technologies such as WebGL and Web Workers, WizMap scales to millions of embedding points directly in users' web browsers and computational notebooks without the need for dedicated backend servers. WizMap is open-source and available at the following public demo link: https://poloclub.github.io/wizmap.

Lightning Fabric is a new, open-source library that allows you to quickly and easily scale models while maintaining full control over your training loop. In the past, getting PyTorch code to run efficiently on GPUs and scaling it up to many machines and large datasets was possible with PyTorch Lightning. As time went on, however, we became aware of the need to provide a scaling option that landed somewhere between a raw deep learning framework like PyTorch on the one hand, and a high-level, feature-rich framework like PyTorch Lightning. Lightning Fabric is just that. While PyTorch Lightning provides many features to save time and improve readability and collaboration, there are complex use cases where full control over the training loop is needed.

My conviction for a product has never been so high for any ML Ops framework as for Ray. Let's be honest, most "ML Ops" libraries suck, not just suck they will probably slow down your ML scientists and data scientists vs not even using anything. Occasionally (surprisingly quite often now), someone asks me about ray and why I think is going to win. I spent plenty of hours trying to formalize my thoughts and here is a summary of it. In layman's terms, through a set of beautifully designed libraries and easy-to-use decorators (@ray.remote),

Machine learning is a critical pillar of Jane Street's global business, and our ever-changing trading environment serves as a unique, rapid-feedback platform for ML experimentation. Researchers at Jane Street are responsible for building models, strategies, and systems that price and trade a variety of financial instruments. As a mix of the trading and software engineering roles, this work involves many things: analyzing large datasets, building and testing models, creating new trading strategies, and writing the code that implements them. We're looking for people to join the research team with deep ML experience in either an applied or academic context. A good candidate should have a deep understanding of a wide variety of ML techniques, and a passion for tinkering with model architectures, feature transformations, and hyperparameters to generate robust inferences.

This article was originally posted on our company website. Lakera's developer platform enables ML teams to ship fail-safe computer vision models. We recently interviewed Tom Dyer, who is one of our product users and brings a lot of experience building computer vision solutions for the healthcare industry. We took away a bunch of knowledge bytes and would love to share the same with our readers, especially those looking to explore building AI for the healthcare industry. In ML research, doubling down on a given metric is well-accepted. However, we must step away from exactly that in the healthcare industry.