The second version of the Efficient Natural Language and Speech Processing (ENLSP-II) workshop focuses on fundamental and challenging problems to make natural language and speech processing (especially pre-trained models) more efficient in terms of Data, Model, Training, and Inference. The workshop program offers an interactive platform for gathering different experts and talents from academia and industry through invited talks, panel discussion, paper submissions, reviews, interactive
posters, oral presentations and a mentorship program. This will be a unique opportunity to address the efficiency issues of current models, build connections, exchange ideas and brainstorm solutions, and foster future collaborations. The topics of this workshop can be of interest for people working on general machine learning, deep learning, optimization, theory and NLP & Speech applications.

The recent advances in deep learning and artificial intelligence have equipped autonomous agents with increasing intelligence, which enables human-level performance in challenging tasks. In particular, these agents with advanced intelligence have shown great potential in interacting and collaborating with humans (e.g., self-driving cars, industrial robot co-worker, smart homes and domestic robots). However, the opaque nature of deep learning models makes it difficult to decipher the decision-making process of the agents, thus preventing stakeholders from readily trusting the autonomous agents, especially for safety-critical tasks requiring physical human interactions. In this workshop, we bring together experts with diverse and interdisciplinary backgrounds, to build a roadmap for developing and deploying trustworthy interactive autonomous systems at scale. Specifically, we aim to the following questions: 1) What properties are required for building trust between humans and interactive autonomous systems? How can we assess and ensure these properties without compromising the expressiveness of the models and performance of the overall systems? 2) How can we develop and deploy trustworthy autonomous agents under an efficient and trustful workflow? How should we transfer from development to deployment? 3) How to define standard metrics to quantify trustworthiness, from regulatory, theoretical, and experimental perspectives? How do we know that the trustworthiness metrics can scale to the broader population? 4) What are the most pressing aspects and open questions for the development of trustworthy autonomous agents interacting with humans? Which research areas are prime for research in academia and which are better suited for industry research?

Advances in machine learning owe much to the public availability of high-quality benchmark datasets and the well-defined problem settings that they encapsulate. Examples are abundant: CIFAR-10 for image classification, COCO for object detection, SQuAD for question answering, BookCorpus for language modelling, etc. There is a general belief that the accessibility of high-quality benchmark datasets is central to the thriving of our community.

However, three prominent issues affect benchmark datasets: data scarcity, privacy, and bias. They already manifest in many existing benchmarks, and also make the curation and publication of new benchmarks difficult (if not impossible) in numerous high-stakes domains, including healthcare, finance, and education. Hence, although ML holds strong promise in these domains, the lack of high-quality benchmark datasets creates a significant hurdle for the development of methodology and algorithms and leads to missed opportunities.

Synthetic data is a promising solution to the key issues of benchmark dataset curation and publication. Specifically, high-quality synthetic data generation could be done while addressing the following major issues.

1. Data Scarcity. The training and evaluation of ML algorithms require datasets with a sufficient sample size. Note that even if the algorithm can learn from very few samples, we still need sufficient validation data for model evaluation. However, it is often challenging to obtain the desired number of samples due to the inherent data scarcity (e.g. people with unique characteristics, patients with rare diseases etc.) or the cost and feasibility of certain data collection. There has been very active research in cross-domain and out-of-domain data generation, as well as generation from a few samples. Once the generator is trained, one could obtain arbitrarily large synthetic datasets.

2. Privacy. In many key applications, ML algorithms rely on record-level data collected from human subjects, which leads to privacy concerns and legal risks. As a result, data owners are often hesitant to publish datasets for the research community. Even if they are willing to, accessing the datasets often requires significant time and effort from the researchers. Synthetic data is regarded as one potential way to promote privacy. The 2019 NeurIPS Competition "Synthetic data hide and seek challenge" demonstrates the difficulty in performing privacy attacks on synthetic data. Many recent works look further into the theoretical and practical aspects of synthetic data and privacy.

3. Bias and under-representation. The benchmark dataset may be subject to data collection bias and under-represent certain groups (e.g. people with less-privileged access to technology). Using these datasets as benchmarks would (implicitly) encourage the community to build algorithms that reflect or even exploit the existing bias. This is likely to hamper the adoption of ML in high-stake applications that require fairness, such as finance and justice. Synthetic data provides a way to curate less biased benchmark data. Specifically, (conditional) generative models can be used to augment any under-represented group in the original dataset. Recent works have shown that training on synthetically augmented data leads to consistent improvements in robustness and generalisation.

Why do we need this workshop? Despite the growing interest in using synthetic data to empower ML, this agenda is still challenging because it involves multiple research fields and various industry stakeholders. Specifically, it calls for the collaboration of the researchers in generative models, privacy, and fairness. Existing research in generative models focuses on generating high-fidelity data, often neglecting the privacy and fairness aspect. On the other hand, the existing research in privacy and fairness often focuses on the discriminative setting rather than the generative setting. Finally, while generative modelling in images and tabular data has matured, the generation of time series and multi-modal data is still a vibrant area of research, especially in complex domains in healthcare and finance. The data modality and characteristics differ significantly across application domains and industries. It is therefore important to get the inputs from the industry experts such that the benchmark reflects reality.

The goal of this workshop is to provide a platform for vigorous discussion with researchers in various fields of ML and industry experts in the hope to progress the idea of using synthetic data to empower ML research. The workshop also provides a forum for constructive debates and identifications of strengths and weaknesses with respect to alternative approaches, e.g. federated learning

Self-Driving Materials Laboratories have greatly advanced the automation of material design and discovery. They require the integration of diverse fields and consist of three primary components, which intersect with many AI-related research topics:

- AI-Guided Design. This component intersects heavily with algorithmic research at NeurIPS, including (but not limited to) various topic areas such as: Reinforcement Learning and data-driven modeling of physical phenomena using Neural Networks (e.g. Graph Neural Networks and Machine Learning For Physics).

- Automated Chemical Synthesis. This component intersects significantly with robotics research represented at NeurIPS, and includes several parts of real-world robotic systems such as: managing control systems (e.g. Reinforcement Learning) and different sensor modalities (e.g. Computer Vision), as well as predictive models for various phenomena (e.g. Data-Based Prediction of Chemical Reactions).

- Automated Material Characterization. This component intersects heavily with a diverse set of supervised learning techniques that are well-represented at NeurIPS such as: computer vision for microscopy images and automated machine learning based analysis of data generated from different kinds of instruments (e.g. X-Ray based diffraction data for determining material structure).

Optimization is a cornerstone of nearly all modern machine learning (ML) and deep learning (DL). Simple first-order gradient-based methods dominate the field for convincing reasons: low computational cost, simplicity of implementation, and strong empirical results.

Yet second- or higher-order methods are rarely used in DL, despite also having many strengths: faster per-iteration convergence, frequent explicit regularization on step-size, and better parallelization than SGD. Additionally, many scientific fields use second-order optimization with great success.

A driving factor for this is the large difference in development effort. By the time higher-order methods were tractable for DL, first-order methods such as SGD and it’s main varients (SGD + Momentum, Adam, …) already had many years of maturity and mass adoption.

The purpose of this workshop is to address this gap, to create an environment where higher-order methods are fairly considered and compared against one-another, and to foster healthy discussion with the end goal of mainstream acceptance of higher-order methods in ML and DL.

While offline RL focuses on learning solely from fixed datasets, one of the main learning points from the previous edition of offline RL workshop was that large-scale RL applications typically want to use offline RL as part of a bigger system as opposed to being the end-goal in itself. Thus, we propose to shift the focus from algorithm design and offline RL applications to how offline RL can be a launchpad , i.e., a tool or a starting point, for solving challenges in sequential decision-making such as exploration, generalization, transfer, safety, and adaptation. Particularly, we are interested in studying and discussing methods for learning expressive models, policies, skills and value functions from data that can help us make progress towards efficiently tackling these challenges, which are otherwise often intractable.


Submission site: https://openreview.net/group?id=NeurIPS.cc/2022/Workshop/Offline_RL. The submission deadline is September 25, 2022 (Anywhere on Earth). Please refer to the submission page for more details.

Language is one of the most impressive human accomplishments and is believed to be the core to our ability to learn, teach, reason and interact with others. Learning many complex tasks or skills would be significantly more challenging without relying on language to communicate, and language is believed to have a structuring impact on human thought. Written language has also given humans the ability to store information and insights about the world and pass it across generations and continents. Yet, the ability of current state-of-the art reinforcement learning agents to understand natural language is limited.

Practically speaking, the ability to integrate and learn from language, in addition to rewards and demonstrations, has the potential to improve the generalization, scope and sample efficiency of agents. For example, agents that are capable of transferring domain knowledge from textual corpora might be able to much more efficiently explore in a given environment or to perform zero or few shot learning in novel environments. Furthermore, many real-world tasks, including personal assistants and general household robots, require agents to process language by design, whether to enable interaction with humans, or simply use existing interfaces.

To support this field of research, we are interested in fostering the discussion around:

- Methods that can effectively link language to actions and observations in the environment;
- Research into language roles beyond encoding goal states, such as structuring hierarchical policies,
- Communicating domain knowledge or reward shaping;
- Methods that can help identify and incorporate outside textual information about the task, or general-purpose semantics learned from outside corpora;
- Novel environments and benchmarks enabling such research and approaching complexity of real-world problem settings.

The aim of the workshop on Language in Reinforcement Learning (LaReL) is to steer discussion and research of these problems by bringing together researchers from several communities, including reinforcement learning, robotics, natural language processing, computer vision and cognitive psychology.

We develop large models to “understand” images, videos and natural language that fuel many intelligent applications from text completion to self-driving cars. But tabular data has long been overlooked despite its dominant presence in data-intensive systems. By learning latent representations from (semi-)structured tabular data, pretrained table models have shown preliminary but impressive performance for semantic parsing, question answering, table understanding, and data preparation. Considering that such tasks share fundamental properties inherent to tables, representation learning for tabular data is an important direction to explore further. These works also surfaced many open challenges such as finding effective data encodings, pretraining objectives and downstream tasks.

Key questions that we aim to address in this workshop are:
- How should tabular data be encoded to make learned Table Models generalize across tasks?
- Which pre-training objectives, architectures, fine-tuning and prompting strategies, work for tabular data?
- How should the varying formats, data types, and sizes of tables be handled?
- To what extend can Language Models be adapted towards tabular data tasks and what are their limits?
- What tasks can existing Table Models accomplish well and what opportunities lie ahead?
- How do existing Table Models perform, what do they learn, where and how do they fall short?
- When and how should Table Models be updated in contexts where the underlying data source continuously evolves?

The First Table Representation Learning workshop is the first workshop in this emerging research area and is centered around three main goals:
1) Motivate tabular data as primal modality for representation learning and further shaping this area.
2) Showcase impactful applications of pretrained table models and discussing future opportunities thereof.
3) Foster discussion and collaboration across the machine learning, natural language processing, and data management communities.

Speakers
Alon Halevy (keynote), Meta AI
Graham Neubig (keynote), Carnegie Mellon University
Carsten Binnig, TU Darmstadt
Çağatay Demiralp, Sigma Computing
Huan Sun, Ohio State University
Xinyun Chen, Google Brain

Panelists
TBA

Scope
We invite submissions that address, but are not limited to, any of the following topics on machine learning for tabular data:
Representation Learning Representation learning techniques for structured (e.g., relational databases) or semi-structured (Web tables, spreadsheet tables) tabular data and interfaces to it. This includes developing specialized data encodings or adaptation of general-purpose ones (e.g., GPT-3) for tabular data, multimodal learning across tables, and other modalities (e.g., natural language, images, code), and relevant fine-tuning and prompting strategies.
Downstream Applications Machine learning applications involving tabular data, such as data preparation (e.g. data cleaning, integration, cataloging, anomaly detection), retrieval (e.g., semantic parsing, question answering, fact-checking), information extraction, and generation (e.g., table-to-text).
Upstream Applications Applications that use representation learning to optimize tabular data processing systems, such as table parsers (extracting tables from documents, spreadsheets, presentations, images), storage (e.g. compression, indexing), and querying (e.g. query plan optimization, cost estimation).
Industry Papers Applications of tabular representation models in production. Challenges of maintaining and managing table representation models in a fast evolving context, e.g. data updating, error correction, monitoring.
New Resources Survey papers, analyses, benchmarks and datasets for tabular representation models and their applications, visions and reflections to structure and guide future research.

Important dates
Submission open: 20 August 2022
Submission deadline: 26 September 2022
Notifications: 20 October 2022
Camera-ready, slides and recording upload: 3 November 2022
Workshop: 2 December 2022

Submission formats
Abstract: 1 page + references.
Extended abstract: at most 4 pages + references.
Regular paper: at least 6 pages + references.

Questions:
table-representation-learning-workshop@googlegroups.com (public)
m.hulsebos@uva.nl (private)

Goals

Interpolation regularizers are an increasingly popular approach to regularize deep models. For example, the mixup data augmentation method constructs synthetic examples by linearly interpolating random pairs of training data points. During their half-decade lifespan, interpolation regularizers have become ubiquitous and fuel state-of-the-art results in virtually all domains, including computer vision and medical diagnosis. This workshop brings together researchers and users of interpolation regularizers to foster research and discussion to advance and understand interpolation regularizers. This inaugural meeting will have no shortage of interactions and energy to achieve these exciting goals. Suggested topics include, but are not limited to the intersection between interpolation regularizers and:

* Domain generalization
* Semi-supervised learning
* Privacy-preserving ML
* Theory
* Robustness
* Fairness
* Vision
* NLP
* Medical applications

## Important dates

* Paper submission deadline: September 22, 2022
* Paper acceptance notification: October 14, 2022
* Workshop: December 2, 2022

## Call for papers

Authors are invited to submit short papers with up to 4 pages, but unlimited number of pages for references and supplementary materials. The submissions must be anonymized as the reviewing process will be double-blind. Please use the NeurIPS template for submissions. We welcome submissions that have been already published during COVID in order to foster discussion. The venue of publication should be clearly indicated during submission for such papers. Submission Link: https://openreview.net/group?id=NeurIPS.cc/2022/Workshop/INTERPOLATE

## Invited Speakers

Chelsea Finn, form Stanford, on "Repurposing Mixup for Robustness and Regression"
Sanjeev Arora, from Princeton, on "Using Interpolation Ideas to provide privacy in Federated Learning settings"
Kenji Kawaguchi, from NUS, on "The developments of the theory of Mixup"
Youssef Mroueh, from IBM, on "Fairness and mixing"
Alex Lamb, from MSR, on "What matters in the world? Exploring algorithms for provably ignoring irrelevant details"

Causality has a long history, providing it with many principled approaches to identify a causal effect (or even distill cause from effect). However, these approaches are often restricted to very specific situations, requiring very specific assumptions. This contrasts heavily with recent advances in machine learning. Real-world problems aren’t granted the luxury of making strict assumptions, yet still require causal thinking to solve. Armed with the rigor of causality, and the can-do-attitude of machine learning, we believe the time is ripe to start working towards solving real-world problems.

Recent years have witnessed the rising need for machine learning systems that can interact with humans in the learning loop. Such systems can be applied to computer vision, natural language processing, robotics, and human-computer interaction. Creating and running such systems call for interdisciplinary research of artificial intelligence, machine learning, and software engineering design, which we abstract as Human in the Loop Learning (HiLL).

The HiLL workshop aims to bring together researchers and practitioners working on the broad areas of HiLL, ranging from interactive/active learning algorithms for real-world decision-making systems (e.g., autonomous driving vehicles, robotic systems, etc.), human-inspired learning that mitigates the gap between human intelligence and machine intelligence, human-machine collaborative learning that creates a more powerful learning system, lifelong learning that transfers knowledge to learn new tasks over a lifetime, as well as interactive system designs (e.g., data visualization, annotation systems, etc.).

The HiLL workshop continues the previous effort to provide a platform for researchers from interdisciplinary areas to share their recent research. In this year’s workshop, a special feature is to encourage the discussion on the interactive and collaborative learning between human and machine learning agents: Can they be organically combined to create a more powerful learning system? We believe the theme of the workshop will be of interest to broad NeurIPS attendees, especially those who are interested in interdisciplinary study.

Workshop Description

Training contemporary neural networks is a lengthy and often costly process, both in human designer time and compute resources. Although the field has invented numerous approaches, neural network training still usually involves an inconvenient amount of “babysitting” to get the model to train properly. This not only requires enormous compute resources but also makes deep learning less accessible to outsiders and newcomers. This workshop will be centered around the question “How can we train neural networks faster” by focusing on the effects algorithms (not hardware or software developments) have on the training time of neural networks. These algorithmic improvements can come in the form of novel methods, e.g. new optimizers or more efficient data selection strategies, or through empirical experience, e.g. best practices for quickly identifying well-working hyperparameter settings or informative metrics to monitor during training.

We all think we know how to train deep neural networks, but we all seem to have different ideas. Ask any deep learning practitioner about the best practices of neural network training, and you will often hear a collection of arcane recipes. Frustratingly, these hacks vary wildly between companies and teams. This workshop offers a platform to talk about these ideas, agree on what is actually known, and what is just noise. In this sense, this will not be an “optimization workshop” in the mathematical sense (of which there have been several in the past, of course).

To this end, the workshop’s goal is to connect two communities: Researchers who develop new algorithms for faster neural network training, such as new optimization methods or deep learning architectures. Practitioners who, through their work on real-world problems, are increasingly relying on “tricks of the trade”. The workshop aims to close the gap between research and applications, identifying the most relevant current issues that hinder faster neural network training in practice.

Topics

Among the topics addressed by the workshop are:

- What “best practices” for faster neural network training are used in practice and can we learn from them to build better algorithms?
- What are painful lessons learned while training deep learning models?
- What are the most needed algorithmic improvements for neural network training?
- How can we ensure that research on training methods for deep learning has practical relevance?

Important Dates

- Submission Deadline: September 30, 2022, 07:00am UTC (updated!)
- Accept/Reject Notification Date: October 20, 2022, 07:00am UTC (updated!)
- Workshop Date: December 2, 2022

Training machine learning models in a centralized fashion often faces significant challenges due to regulatory and privacy concerns in real-world use cases. These include distributed training data, computational resources to create and maintain a central data repository, and regulatory guidelines (GDPR, HIPAA) that restrict sharing sensitive data. Federated learning (FL) is a new paradigm in machine learning that can mitigate these challenges by training a global model using distributed data, without the need for data sharing. The extensive application of machine learning to analyze and draw insight from real-world, distributed, and sensitive data necessitates familiarization with and adoption of this relevant and timely topic among the scientific community.

Despite the advantages of FL, and its successful application in certain industry-based cases, this field is still in its infancy due to new challenges that are imposed by limited visibility of the training data, potential lack of trust among participants training a single model, potential privacy inferences, and in some cases, limited or unreliable connectivity.

The goal of this workshop is to bring together researchers and practitioners interested in FL. This day-long event will facilitate interaction among students, scholars, and industry professionals from around the world to understand the topic, identify technical challenges, and discuss potential solutions. This will lead to an overall advancement of FL and its impact in the community, while noting that FL has become an increasingly popular topic in the machine learning community in recent years.

One of the key challenges for AI is to understand, predict, and model data over time. Pretrained networks should be able to temporally generalize, or adapt to shifts in data distributions that occur over time. Our current state-of-the-art (SOTA) still struggles to model and understand data over long temporal durations – for example, SOTA models are limited to processing several seconds of video, and powerful transformer models are still fundamentally limited by their attention spans. On the other hand, humans and other biological systems are able to flexibly store and update information in memory to comprehend and manipulate multimodal streams of input. Cognitive neuroscientists propose that they do so via the interaction of multiple memory systems with different neural mechanisms. What types of memory systems and mechanisms already exist in our current AI models? First, there are extensions of the classic proposal that memories are formed via synaptic plasticity mechanisms – information can be stored in the static weights of a pre-trained network, or in fast weights that more closely resemble short-term plasticity mechanisms. Then there are persistent memory states, such as those in LSTMs or in external differentiable memory banks, which store information as neural activations that can change over time. Finally, there are models augmented with static databases of knowledge, akin to a high-precision long-term memory or semantic memory in humans. When is it useful to store information in each one of these mechanisms, and how should models retrieve from them or modify the information therein? How should we design models that may combine multiple memory mechanisms to address a problem? Furthermore, do the shortcomings of current models require some novel memory systems that retain information over different timescales, or with different capacity or precision? Finally, what can we learn from memory processes in biological systems that may advance our models in AI? We aim to explore how a deeper understanding of memory mechanisms can improve task performance in many different application domains, such as lifelong / continual learning, reinforcement learning, computer vision, and natural language processing.

Background. In recent years, graph learning has quickly grown into an established sub-field of machine learning. Researchers have been focusing on developing novel model architectures, theoretical understandings, scalable algorithms and systems, and successful applications across industry and science regarding graph learning. In fact, more than 5000 research papers related to graph learning have been published over the past year alone.

Challenges. Despite the success, existing graph learning paradigms have not captured the full spectrum of relationships in the physical and the virtual worlds. For example, in terms of applicability of graph learning algorithms, current graph learning paradigms are often restricted to datasets with explicit graph representations, whereas recent works have shown promise of graph learning methods for applications without explicit graph representations. In terms of usability, while popular graph learning libraries greatly facilitate the implementation of graph learning techniques, finding the right graph representation and model architecture for a given use case still requires heavy expert knowledge. Furthermore, in terms of generalizability, unlike domains such as computer vision and natural language processing where large-scale pre-trained models generalize across downstream applications with little to no fine-tuning and demonstrate impressive performance, such a paradigm has yet to succeed in the graph learning domain.

Goal. The primary goal of this workshop is to expand the impact of graph learning beyond the current boundaries. We believe that graph, or relation data, is a universal language that can be used to describe the complex world. Ultimately, we hope graph learning will become a generic tool for learning and understanding any type of (structured) data. We aim to present and discuss the new frontiers in graph learning with researchers and practitioners within and outside the graph learning community. New understandings of the current challenges, new perspectives regarding the future directions, and new solutions and applications as proof of concepts are highly welcomed.

Scope and Topics. We welcome submissions regarding the new frontiers of graph learning, including but not limited to:
- Graphs in the wild: Graph learning for datasets and applications without explicit relational structure (e.g., images, text, audios, code). Novel ways of modeling structured/unstructured data as graphs are highly welcomed.
- Graphs in ML: Graph representations in general machine learning problems (e.g., neural architectures as graphs, relations among input data and learning tasks, graphs in large language models, etc.)
- New oasis: Graph learning methods that are significantly different from the current paradigms (e.g., large-scale pre-trained models, multi-task models, super scalable algorithms, etc.)
- New capabilities: Graph representation for knowledge discovery, optimization, causal inference, explainable ML, ML fairness, etc.
- Novel applications: Novel applications of graph learning in real-world industry and scientific domains. (e.g., graph learning for missing data imputation, program synthesis, etc.)

Call for papers

Submission deadline: Thursday, Sept 22, 2022 (16:59 PDT)

Submission site (OpenReview): NeurIPS 2022 GLFrontiers Workshop

Author notification: Thursday, Oct 6, 2022

Camera ready deadline: Thursday, Oct 27, 2022 (16:59 PDT)

Workshop (in person): Friday, Dec 2, 2022

The workshop will be held fully in person at the New Orleans Convention Center, as part of the NeurIPS 2022 conference. We also plan to offer livestream for the event, and more details will come soon.

We welcome both short research papers of up to 4 pages (excluding references and supplementary materials), and full-length research papers of up to 8 pages (excluding references and supplementary materials). All accepted papers will be presented as posters. We plan to select around 6 papers for oral presentations and 2 papers for the outstanding paper awards with potential cash incentives.

All submissions must use the NeurIPS template. We do not require the authors to include the checklist in the template. Submissions should be in .pdf format, and the review process is double-blind—therefore the papers should be appropriately anonymized. Previously published work (or under-review) is acceptable.

Should you have any questions, please reach out to us via email:
glfrontiers@googlegroups.com

The score function, which is the gradient of the log-density, provides a unique way to represent probability distributions. By working with distributions through score functions, researchers have been able to develop efficient tools for machine learning and statistics, collectively known as score-based methods.

Score-based methods have had a significant impact on vastly disjointed subfields of machine learning and statistics, such as generative modeling, Bayesian inference, hypothesis testing, control variates and Stein’s methods. For example, score-based generative models, or denoising diffusion models, have emerged as the state-of-the-art technique for generating high quality and diverse images. In addition, recent developments in Stein’s method and score-based approaches for stochastic differential equations (SDEs) have contributed to the developement of fast and robust Bayesian posterior inference in high dimensions. These have potential applications in engineering fields, where they could help improve simulation models.

At our workshop, we will bring together researchers from these various subfields to discuss the success of score-based methods, and identify common challenges across different research areas. We will also explore the potential for applying score-based methods to even more real-world applications, including in computer vision, signal processing, and computational chemistry. By doing so, we hope to folster collaboration among researchers and build a more cohesive research community focused on score-based methods.

'Medical Imaging meets NeurIPS' is a satellite workshop established in 2017. The workshop aims to bring researchers together from the medical image computing and machine learning communities. The objective is to discuss the major challenges in the field and opportunities for joining forces. This year the workshop will feature online oral and poster sessions with an emphasis on audience interactions. In addition, there will be a series of high-profile invited speakers from industry, academia, engineering and medical sciences giving an overview of recent advances, challenges, latest technology and efforts for sharing clinical data.

Attention is a widely popular topic studied in many fields such as neuroscience, psychology, and machine learning. A better understanding and conceptualization of attention in both humans and machines has led to significant progress across fields. At the same time, attention is far from a clear or unified concept, with many definitions within and across multiple fields.

Cognitive scientists study how the brain flexibly controls its limited computational resources to accomplish its objectives. Inspired by cognitive attention, machine learning researchers introduce attention as an inductive bias in their models to improve performance or interpretability. Human-computer interaction designers monitor people’s attention during interactions to implicitly detect aspects of their mental states.

While the aforementioned research areas all consider attention, each formalizes and operationalizes it in different ways. Bridging this gap will facilitate:
- (Cogsci for AI) More principled forms of attention in AI agents towards more human-like abilities such as robust generalization, quicker learning and faster planning.
- (AI for cogsci) Developing better computational models for modeling human behaviors that involve attention.
- (HCI) Modeling attention during interactions from implicit signals for fluent and efficient coordination
- (HCI/ML) Artificial models of algorithmic attention to enable intuitive interpretations of deep models?

Recent years have seen rapid progress in meta-learning methods, which transfer knowledge across tasks and domains to efficiently learn new tasks, optimize the learning process itself, and even generate new learning methods from scratch. Meta-learning can be seen as the logical conclusion of the arc that machine learning has undergone in the last decade, from learning classifiers, to learning representations, and finally to learning algorithms that themselves acquire representations, classifiers, and policies for acting in environments. In practice, meta-learning has been shown to yield new state-of-the-art automated machine learning methods, novel deep learning architectures, and substantially improved one-shot learning systems. Moreover, improving one’s own learning capabilities through experience can also be viewed as a hallmark of intelligent beings, and neuroscience shows a strong connection between human and reward learning and the growing sub-field of meta-reinforcement learning.

Some of the fundamental questions that this workshop aims to address are:
- What are the meta-learning processes in nature (e.g., in humans), and how can we take inspiration from them?
- What is the relationship between meta-learning, continual learning, and transfer learning?
- What interactions exist between meta-learning and large pretrained / foundation models?
- What principles can we learn from meta-learning to help us design the next generation of learning systems?
- What kind of theoretical principles can we develop for meta-learning?
- How can we exploit our domain knowledge to effectively guide the meta-learning process and make it more efficient?
- How can we design better benchmarks for different meta-learning scenarios?

As prospective participants, we primarily target machine learning researchers interested in the questions and foci outlined above. Specific target communities within machine learning include, but are not limited to: meta-learning, AutoML, reinforcement learning, deep learning, optimization, evolutionary computation, and Bayesian optimization. We also invite submissions from researchers who study human learning and neuroscience, to provide a broad and interdisciplinary perspective to the attendees.

In recent years, the growth of decision-making applications, where principled handling of uncertainty is of key concern, has led to increased interest in Bayesian techniques. By offering the capacity to assess and propagate uncertainty in a principled manner, Gaussian processes have become a key technique in areas such as Bayesian optimization, active learning, and probabilistic modeling of dynamical systems. In parallel, the need for uncertainty-aware modeling of quantities that vary over space and time has led to large-scale deployment of Gaussian processes, particularly in application areas such as epidemiology. In this workshop, we bring together researchers from different communities to share ideas and success stories. By showcasing key applied challenges, along with recent theoretical advances, we hope to foster connections and prompt fruitful discussion. We invite researchers to submit extended abstracts for contributed talks and posters.

As machine learning models find increasing use in the real world, ensuring their safe and reliable deployment depends on ensuring their robustness to distribution shift. This is especially true for sequential data, which occurs naturally in various data domains such as natural language processing, healthcare, computational biology, and finance. However, building models for sequence data which are robust to distribution shifts presents a unique challenge. Sequential data are often discrete rather than continuous, exhibit difficult to characterize distributions, and can display a much greater range of types of distributional shifts. Although many methods for improving model robustness exist for imaging or tabular data, extending these methods to sequential data is a challenging research direction that often requires fundamentally different techniques.

This workshop aims to facilitate progress towards improving the distributional robustness of models trained on sequential data by bringing together researchers to tackle a wide variety of research questions including, but not limited to:
(1) How well do existing robustness methods work on sequential data, and why do they succeed or fail?
(2) How can we leverage the sequential nature of the data to develop novel and distributionally robust methods?
(3) How do we construct and utilize formalisms for distribution shifts in sequential data?

We hope that this workshop provides a first step towards improving the robustness, and ultimately safety and reliability, of models in sequential data domains.

Time series data are ubiquitous in healthcare, from medical time series to wearable data, and present an exciting opportunity for machine learning methods to extract actionable insights about human health. However, huge gap remain between the existing time series literature and what is needed to make machine learning systems practical and deployable for healthcare. This is because learning from time series for health is notoriously challenging: labels are often noisy or missing, data can be multimodal and extremely high dimensional, missing values are pervasive, measurements are irregular, data distributions shift rapidly over time, explaining model outcomes is challenging, and deployed models require careful maintenance over time. These challenges introduce interesting research problems that the community has been actively working on for the last few years, with significant room for contribution still remaining. Learning from time series for health is a uniquely challenging and important area with increasing application. Significant advancements are required to realize the societal benefits of these systems for healthcare. This workshop will bring together machine learning researchers dedicated to advancing the field of time series modeling in healthcare to bring these models closer to deployment.