The workshop is a one-day event with invited speakers, oral presentations, and posters. The event brings together faculty, graduate students, research scientists, and engineers for an opportunity to connect and exchange ideas. There will be a panel discussion and a mentoring session to discuss current research trends and career choices in artificial intelligence and machine learning.

Website: https://www.latinxinai.org/neurips-2022

The 17th Workshop for Women in Machine Learning (WiML) brings together members of the academic and industry research landscape for an opportunity to connect and exchange ideas, and learn from each other.

Amazon organizer: Sergül Aydöre

Website: https://sites.google.com/view/wiml2022

At AWS, we aim at lowering the bar in productizing graph machine learning (GML). Neptune ML facilitates this goal and helps customers obtain real time GNN predictions with graph databases using graph query languages. Amazon develops frameworks based on DGL to solve internal and external GML problems and realize the impact of GNNs.

Amazon organizers: Vassilis Ioannidis, Zak Jost, Minjie Wang, David Paul Wipf

Website: https://sites.google.com/view/dgl-workshop-neurips-2022

This workshop brings 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.

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.

Amazon organizer: Olivia Choudhury

Amazon program committee member: Berivan Isik

Website: https://federated-learning.org/fl-neurips-2022

This workshop brings together research communities in generative models, privacy, and fairness as well as industry leaders in a joint effort to develop the theory, methodology, and algorithms to generate synthetic benchmark datasets with the goal of enabling ethical and reproducible ML research. The call for papers submission deadline is September 22, 11:59pm.

Amazon organizer: Sergül Aydöre

Amazon panelist: Shuai Tang

Website: https://www.syntheticdata4ml.vanderschaar-lab.com

The second version of the Efficient Natural Language and Speech Processing (ENLSP) 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.

Amazon panelist: Rahul Gupta

Amazon technical committee: Can Liu, Amina Shabbeer, M. Skylar Versage, Tanya Roosta

Website: https://neurips2022-enlsp.github.io

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.

Amazon speaker and panelist: Bernhard Schölkopf

Website: https://www.cml-4-impact.vanderschaar-lab.com

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.

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.

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. We are reserving a few complimentary workshop registrations for accepted paper authors who would otherwise have difficulty attending. Please reach out if this applies to you.

Amazon program committee members: Tong He, Mohammad Kachuee, Harshavardhan Sundar

Website: https://sites.google.com/view/interpolation-workshop

A workshop to bring together researchers who use score-based methods in machine learning and statistics.

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.).

Website: https://neurips-hill.github.io

The Table Representation Learning workshop is the first workshop in this emerging research area and has the following main goals: 1) motivating tabular data as a first-class modality for representation learning and further shaping this area, 2) show-casing impactful applications of pretrained table models and discussing future opportunities thereof, and 3) facilitating discussion and collaboration across the machine learning, natural language processing, and data management communities.

Website: https://table-representation-learning.github.io/

The main goals of the workshop are to: identify key research problems that are critical for the success of real-world applications; report progress on addressing these critical issues; and have practitioners share their success stories of applying RL to real-world problems, and the insights gained from such applications.

Amazon co-chair: Lihong Li, Yao Liu

Website: https://sites.google.com/view/RL4RealLife

This workshop focuses on how future researchers and practitioners should prepare themselves to achieve different trustworthiness requirements, such as security and privacy in machine learning through decentralization and blockchain techniques, as well as how to leverage machine learning techniques to automate some processes in current decentralized systems and ownership economies in Web3.

Amazon organizer: Bo Li

Website: https://ai-secure.github.io/DMLW2022/

In this workshop, we bring together researchers in dynamical systems, time-series methods, causality, infinite-depth neural networks, and machine learning. We believe a side-by-side discussion of dynamical systems and causal inference (discovery and estimation) will allow one to develop novel approaches, transfer expertise across communities, and enable us to overcome current limitations of each individual perspective. Connections to other scientific disciplines as well as practitioners’ view will be highlighted to showcase successful applications of causal inference in dynamical settings.

Amazon organizer: Yuyang (Bernie) Wang

Website: https://sites.google.com/view/caudyn2022

Transfer learning has become ubiquitous in natural language processing due in part to the ease of access to large pre-trained language models (PLM). Current transfer learning methods, combined with PLMs, have seen outstanding successes in transferring knowledge to new tasks, domains, and even languages. However, existing methods still suffer from some common weaknesses that restrict their potential applications.

One particular hope for this workshop is to help to answer the question: Can we characterize the transfer behaviors between source and target tasks/domains/languages in terms of their fundamental properties?

Amazon program committee member: Alham Fikri Aji

Website: https://tl4nlp.github.io

This workshop brings together domain experts and ML researchers working on mitigating distribution shifts in real-world applications.

Website: https://sites.google.com/view/distshift2022

In the 3rd iteration of this workshop, we continue to bridge this gap between theory and practice. We bring together SSL-interested researchers from various domains to discuss the theoretical foundations of empirically well-performing SSL approaches and how the theoretical insights can further improve SSL’s empirical performance.

In this competition, we challenge you to detect and analyze Trojan attacks on deep neural networks that are designed to be difficult to detect. Neural Trojans are a growing concern for the security of ML systems, but little is known about the fundamental offense-defense balance of Trojan detection. Early work suggests that standard Trojan attacks may be easy to detect [1], but recently it has been shown that in simple cases one can design practically undetectable Trojans.

Amazon organizer: Bo Li

Website: https://trojandetection.ai

This workshop will aim to bring together researchers with backgrounds in computational science and deep learning to encourage intellectual exchanges, cultivate relationships and accelerate research in this area. The scope of the workshop spans topics at the intersection of DL and DEs, including theory of DL and DEs, neural differential equations, solving DEs with neural networks, and more.

Amazon organizer: Archis Joglekar

Website: https://dlde-2022.github.io

A workshop for algorithmic efficiency in practical neural network training.

This workshop aims to bring together researchers interested in the emerging and interdisciplinary field of trustworthy and socially responsible machine learning from a broad range of disciplines with different perspectives to this problem. We attempt to highlight recent related work from different communities, clarify the foundations of trustworthy machine learning, and chart out important directions for future work and cross-community collaborations.

Website: https://sites.google.com/berkeley.edu/trustworthy-embodied-ai/home?authuser=0

In this exciting time for the field, our workshop, “Machine Learning in Structural Biology” (MLSB), seeks to bring together relevant experts, practitioners, and students across a broad community to focus on these challenges and opportunities. We believe the union of these communities, including the geometric and graph learning communities, NLP researchers, and structural biologists with domain expertise at our workshop can help spur new ideas, spark collaborations, and advance the impact of machine learning in structural biology. Progress at this intersection promises to unlock new scientific discoveries and the ability to design novel medicines.

The All Things Attention workshop aims to foster connections across disparate academic communities that conceptualize “attention” such as Neuroscience, Psychology, Machine Learning, and Human Computer Interaction.