In this workshop, we introduce AutoGluon, a state-of-the-art and easy-to-use toolkit that empowers multimodal AutoML. Different from most AutoML systems that focus on solving tabular tasks containing categorical and numerical features, we consider supervised learning tasks on various types of data including tabular features, text, image, time series, as well as their combinations. We will introduce the real-world problems that AutoGluon can help you solve within three lines of code and the fundamental techniques adopted in the toolkit. Rather than diving deep into the mechanisms underlining each individual ML models, we emphasize on how you can take advantage of a diverse collection of models to build an automated ML pipeline. Our workshop will also emphasize on the techniques behind automatically building and training deep learning models, which are powerful yet cumbersome to manage manually.

Organizers: Nick Erickson, Tony Hu, Zhiqiang Tang

Website: https://autogluon.github.io/neurips-autogluon-workshop

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. While all presenters will identify primarily as LatinX, all are invited to attend.

Amazon organizer: Brayan Valdés Ortiz

Website: https://www.latinxinai.org/neurips-2023

Our goal is to democratize access to language models and address three major issues: (1) Lack of transparency around model training methods leads to a majority of models being not reproducible. (2) The absence of a standard benchmark to evaluate these models side-by-side. (3) Insufficient access to dedicated hardware prevents widespread availability and usage of these models.

Here we present a LLM efficiency challenge, to tackle these three challenges and democratize access to state of the art LLMs.

Website: https://llm-efficiency-challenge.github.io

The Table Representation Learning (TRL) workshop is the first in this emerging research area and concentrates on three main goals: motivate tables as a primary modality for representation and generative learning and advance the area further, Showcase impactful applications of pretrained table models and discussing future opportunities, and foster discussion and collaboration across the ML, NLP and DB communities.

Website: https://table-representation-learning.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/distshift2023

Humanitarian crises from disease outbreak to war to oppression against disadvantaged groups have threatened people and their communities throughout history. Natural disasters are a single, extreme example of such crises. In the wake of hurricanes, earthquakes, and other such crises, people have ceaselessly sought ways--often harnessing innovation--to provide assistance to victims after disasters have struck.

Through this workshop, we intend to establish meaningful dialogue between the Artificial Intelligence (AI) and Humanitarian Assistance and Disaster Response (HADR) communities. By the end of the workshop, the ICCV research community can learn the practical challenges of aiding those in crisis, while the HADR community can get to know the state of art and practice in AI. We seek to establish a pipeline of transitioning the research created by the AI and Computer Vision community to real-world humanitarian issues. We believe such an endeavor is possible due to recent successes in applying techniques from various AI and Machine Learning (ML) disciplines to HADR.

Website: https://www.hadr.ai

The goal of this workshop is to bring together machine learning researchers from academia and industry to encourage knowledge transfer and collaboration on these topics to discover ideas that can expand our understanding of robustness of few-shot learning approaches based on large foundational models. The ideal outcome of the workshop is to identify a set of concrete research directions to enable the next generation of robust models that are safe and responsible.

Website: https://sites.google.com/view/r0-fomo

In this workshop, we provide a unique venue for the most recent trends in research on deep generative models, focusing on exploring their potential for health applications. We also provide the optimal setting to discuss the open problems that prevent these methods from having a profound positive impact in clinical settings. This workshop will be the ideal venue to attract a diverse pool of researchers aiming to integrate generative models in health scenarios.

Website: https://neurips.cc/virtual/2023/workshop/66495

The third version of the Efficient Natural Language and Speech Processing (ENLSP-III) workshop will focus on the future of large language models and their emerging applications on different domains such as natural language, speech processing, and biological sequences; and the target is on how to make them more efficient in terms of Data, Model, Training, and Inference for real-world applications as well as academic research. 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 discuss and share challenging problems, 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.

Website: https://neurips2023-enlsp.github.io

In the 6th iteration of the Robot Learning workshop at NeurIPS, we will create a space for researchers from diverse backgrounds to gather and discuss the opportunities, challenges, and risks associated with large models in robotics research. Robotics is one of the most exciting and diverse applications for machine learning. It is both a hard challenge and a fruitful source of problems for machine learning approaches and our workshop is a space for members of both communities to meet.

Website: https://www.robot-learning.ml/2023

This workshop brings together research communities in generative models, privacy, and fairness as well as industry leaders to provide a platform for vigorous discussion between all these different perspectives in the hope of progressing the ideal of using SD for better and trustworthy ML training.

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

This workshop aims to bring together researchers from academia and industry to discuss major challenges, outline recent advances, and highlight future directions pertaining to novel and existing real-world experimental design and active learning problems. In addition, we aim to highlight new and emerging research opportunities for the machine learning community that arise from the evolving needs to make experimental design and active learning procedures that are theoretically and practically relevant for practical applications. Examples include protein design, causal discovery, drug design, and materials design, to name a few.

Website: https://realworldml.github.io/neurips2023/

The third version of the Efficient Natural Language and Speech Processing (ENLSP-III) workshop will focus on the future of large language models and their emerging applications on different domains such as natural language, speech processing, and biological sequences; and the target is on how to make them more efficient in terms of Data, Model, Training, and Inference for real-world applications as well as academic research.

https://neurips2023-enlsp.github.io/

Many in the ML community wish to take action on climate change, but are unsure of the pathways through which they can have the most impact. This workshop highlights work that demonstrates that, while no silver bullet, ML can be an invaluable tool in reducing greenhouse gas emissions and in helping society adapt to the effects of climate change. Climate change is a complex problem, for which action takes many forms - from theoretical advances to deployment of new technology. Many of these actions represent high-impact opportunities for real-world change, and are simultaneously interesting academic research problems.

Website: https://www.climatechange.ai/events/neurips2023