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Registration Desk Fri 15 Dec 07:30 a.m.  


Workshop: Generative AI for Education (GAIED): Advances, Opportunities, and Challenges Fri 15 Dec 08:15 a.m.  

Paul Denny · Sumit Gulwani · Neil Heffernan · Tanja Käser · Steven Moore · Anna Rafferty · Adish Singla

GAIED (pronounced "guide") aims to bring together researchers, educators, and practitioners to explore the potential of generative AI for enhancing education.


Workshop: AI for Accelerated Materials Design (AI4Mat-2023) Fri 15 Dec 08:15 a.m.  

Santiago Miret · Benjamin Sanchez-Lengeling · Jennifer Wei · Vineeth Venugopal · Marta Skreta · N M Anoop Krishnan

The AI for Accelerated Materials Discovery (AI4Mat) Workshop 2023 provides an inclusive and collaborative platform where AI researchers and material scientists converge to tackle the cutting-edge challenges in AI-driven materials discovery and development. Our goal is to foster a vibrant exchange of ideas, breaking down barriers between disciplines and encouraging insightful discussions among experts from diverse disciplines and curious newcomers to the field. The workshop embraces a broad definition of materials design encompassing matter in various forms, such as crystalline and amorphous solid-state materials, glasses, molecules, nanomaterials, and devices. By taking a comprehensive look at automated materials discovery spanning AI-guided design, synthesis and automated material characterization, we hope to create an opportunity for deep, thoughtful discussion among researchers working on these interdisciplinary topics, and highlight ongoing challenges in the field.


Workshop: New Frontiers of AI for Drug Discovery and Development Fri 15 Dec 08:15 a.m.  

Animashree Anandkumar · Ilija Bogunovic · Ti-chiun Chang · Quanquan Gu · Jure Leskovec · Michelle Li · Chong Liu · Nataša Tagasovska · Mengdi Wang · Wei Wang

We will facilitate interdisciplinary discussions to identify gaps and opportunities for AI in the drug discovery and development pipeline.


Workshop: UniReps: Unifying Representations in Neural Models Fri 15 Dec 08:15 a.m.  

Marco Fumero · Emanuele Rodolà · Francesco Locatello · Gintare Karolina Dziugaite · Mathilde Caron · Clémentine Dominé

Neural models tend to learn similar representations when subject to similar stimuli; this behavior has been observed both in biological and artificial settings.The emergence of these similar representations is igniting a growing interest in the fields of neuroscience and artificial intelligence. To gain a theoretical understanding of this phenomenon, promising directions include: analyzing the learning dynamics and studying the problem of identifiability in the functional and parameter space. This has strong consequences in unlocking a plethora of applications in ML from model fusion, model stitching, to model reuse and in improving the understanding of biological and artificial neural models. The objective of the workshop is to discuss theoretical findings, empirical evidence and practical applications of this phenomenon, benefiting from the cross-pollination of different fields (ML, Neuroscience, Cognitive Science) to foster the exchange of ideas and encourage collaborations.Overall the questions we aim to investigate are when, why and how internal representations of distinct neural models can be unified into a common representation.


Workshop: Associative Memory & Hopfield Networks in 2023 Fri 15 Dec 08:15 a.m.  

Parikshit Ram · Hilde Kuehne · Daniel Lee · Cengiz Pehlevan · Mohammed Zaki · Lenka Zdeborová

This workshop will discuss the latest multidisciplinary developments in Associative Memory and Hopfield Networks. A number of leading researchers in this research area from around the world have already agreed to attend and present their latest results. We anticipate sharing their presentations and outlining future research directions in this emerging field with the rest of the NeurIPS community.

Tagline: We will discuss recent multidisciplinary developments in Hopfield Networks and outline future research directions in this emerging field.


Workshop: Touch Processing: a new Sensing Modality for AI Fri 15 Dec 08:15 a.m.  

Roberto Calandra · Haozhi Qi · Mike Lambeta · Perla Maiolino · Yasemin Bekiroglu · Jitendra Malik

This workshop aims to seed foundations of using AI/ML dedicated to studying touch and enable future applications such as robotics and AR/VR.


NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences Fri 15 Dec 08:15 a.m.  

Brian Nord · Atilim Gunes Baydin · Adji Bousso Dieng · Emine Kucukbenli · Siddharth Mishra-Sharma · Benjamin Nachman · Kyle Cranmer · Gilles Louppe · Savannah Thais

Physical sciences and machine learning: more than the sum of their parts. Join us to discuss research at the convergence of these fields!


Workshop: Deep Generative Models for Health Fri 15 Dec 08:15 a.m.  

Emanuele Palumbo · Laura Manduchi · Sonia Laguna · Melanie F. Pradier · Vincent Fortuin · Stephan Mandt · Julia Vogt

Deep generative models have recently gained increasing attention in machine learning research with recent breakthroughs, such as Stable Diffusion, DALL-E, and Chat-GPT, among others. Despite significant advancements, the potential of generative AI in the health sector is yet not fully exploited. To address this gap, our workshop serves as a forum for presenting the latest research trends in generative models tailored for health applications. By bringing together a diversified pool of experts, we aim to investigate the methodological requirements and clinical implications of generative AI for health applications, thus shedding light on the challenges that lie ahead. Through this collaborative effort, we aspire to unlock the potential of generative models for groundbreaking advancements in the health sector.


Workshop: Foundation Models for Decision Making Fri 15 Dec 08:15 a.m.  

Sherry Yang · Ofir Nachum · Yilun Du · Stephen McAleer · Igor Mordatch · Linxi Fan · Jeannette Bohg · Dale Schuurmans

Foundation models pretrained on diverse vision and language datasets have demonstrated exceptional capabilities in performing a wide range of downstream vision and language tasks. As foundation models are deployed in real-world applications such as dialogue, autonomous driving, healthcare, and robotics, they inevitably face new challenges such as learning from external feedback, adapting to different task modalities, and performing long-term reasoning and planning. Such challenges have traditionally been at the core of sequential decision making, encompassing areas such as reinforcement learning, imitation learning, planning, search, and optimal control. These research fields have traditionally focused on task-specific settings with limited prior knowledge, and yet there has been significant research progress in surpassing human performance in tasks like playing board games and Atari video games, as well as operating robots to complete navigation and manipulation tasks. However, since these methods generally learn to solve a specific task from scratch without broad knowledge from vision and language, they can struggle with generalization and sample efficiency. The goal of this workshop is to bring together the sequential decision making community including planning, search, RL, and optimal control, together with the foundation models community in vision and language to confront the challenges in decision making at scale. The workshop will span high-level discussions on how foundation models and decision making can benefit each other when jointly considered and low-level algorithmic details of various decision making algorithms and vision-language architectures, which might lead to both opportunities or challenges. Specific topics, for example, will include foundation model agents interacting with humans, computers, tools, simulators, physical world, and each other.


Workshop: Causal Representation Learning Fri 15 Dec 08:15 a.m.  

Sara Magliacane · Atalanti Mastakouri · Yuki Asano · Claudia Shi · Cian Eastwood · Sébastien Lachapelle · Bernhard Schölkopf · Caroline Uhler

Can we learn causal representations from raw data, e.g. images? This workshop connects research in causality and representation learning.


Workshop: Information-Theoretic Principles in Cognitive Systems (InfoCog) Fri 15 Dec 08:15 a.m.  

Noga Zaslavsky · Rava Azeredo da Silveira · Ronit Bustin · Ron M. Hecht

Information theory provides a mathematical framework allowing to formulate and quantify the basic limitations of data compression and communication. The notions of data compression and communication, based in analog and digital communication, are also relevant toother domains; as such, information theory spans a number of research fields. Aiming to formulate, understand, and quantify the storage and processing of information is a thread that ties together these disparate fields, and especially the study of cognition in humans and machines. Specifically, the desire to reach an integrative computational theory of human and artificial cognition, is attempted by leveraging information-theoretic principles as bridges between various cognitive functions and neural representations. Insights from information theoretic formalization have also led to tangible outcomes which have influenced the operation of artificial intelligent systems. One example is the information bottleneck (IB) approach, yielding insights on learning in neural networks (NN), as well as tools for slow feature analysis and speech recognition. A central application of the IB approach on NN, is through the view of data transfer between layers as an autoencoder. The approach then uses a variational approximation of the IB to produce an objective for minimization that is feasible and results in efficient training (a.k.a. variational IB(VIB)). In the other direction, the variational autoencoder (VAE) framework has also been used to explain cognitive functions, as done for example in. The IB approach has also been applied to emergent communication (EC) in both humans and machines, using a vector quantization VIB(VQ-VIB) method, that extends the aforementioned VIB method. Another example is the trade-off between information and value in the context of sequential decision making. This corresponding formalism has led to tangible methods in the solution of sequential decision making problems and was even used in an experimental study of mouse navigation and study of drivers' eye gaze patterns and study of drivers' language models. In aiming at understanding machine learning (ML), specifically in the context of NNs, or cognition, we need theoretical principles (hypotheses) that can be tested. To quote Shannon: I personally believe that many of the concepts of information theory will prove useful in these other fields-and, indeed, some results are already quite promising-but the establishing of such applications is not a trivial matter of translating words to a new domain, but rather the slow tedious process of hypothesis and experimental verification. If, for example, the human being acts in some situations like an ideal decoder, this is an experimental and not a mathematical fact, and as such must be tested under a wide variety of experimental situations. Today, both ML and cognition can entertain huge amounts of data. Establishing quantitative theories and corresponding methods for computation can have a massive impact on progress in these fields. Broadly, this workshop aims to further the understanding of information flow in cognitive processes and neural networks models of cognition. More concretely, this year’s workshop goals are twofold. On the one hand we wish to provide a fruitful platform for discussions relating to formulations of storage and processing of information either in human or artificial cognition systems, via information-theoretic measures, as those formalisms mentioned above. Specifically, the workshop comes to allow information theory researchers to take part in such discussions, allowing first-hand sharing of knowledge and ideas. On the other hand, we hope this workshop can advance, sharpen and enhance the research done around the computation of information theoretic quantities, specifically for the needs and benefits of cognition research. The two aims of the workshop are not independent of one another - any information theoretic formalism that we wish to experimentally verify has to be, in some sense, computationally feasible. Moreover, we wish that computation and estimation methods are developed in a way that is tailored to the open questions in human and artificial cognition. The proposed workshop focuses on bringing together researchers interested in integrating information-theoretic approaches with researchers focused on the computation/estimation of information-theoretic quantities, with the aim of tightening the collaboration between the two communities. Researchers interested in integrating information-theoretic approaches come from cognitive science, neuroscience, linguistics, economics, and beyond. Efforts in the computation/estimation of information-theoretic quantities are pursued for many reasons, and is a line of research gaining increasing attention due to advances in ML. Furthermore, these researchers have created in recent years new methods to measure information-related quantities.


Competition: Causal Structure Learning from Event Sequences and Prior Knowledge Fri 15 Dec 08:30 a.m.  

Zhang Keli · Ruichu Cai · Kun Kuang · Lujia Pan · Ye Jian · Jiale Zheng · Mengyue Yang · Marcus Kalander · Dai Quanyu · Liu Yuequn

In this competition, we are focusing on a fundamental causal challenge: participantsare asked to learn the causal alarm graphs in which every node is an alarm typefrom observable historical alarm data together with limited prior knowledge. Thechallenge originates from a real-world root cause analysis (RCA) scenario intelecommunication networks. By addressing this challenge, participants will notonly help operators trouble-shooting efficiently, but also advance the field of causaldiscovery, and contribute to our understanding of complex systems.


Workshop: Instruction Tuning and Instruction Following Fri 15 Dec 08:30 a.m.  

Qinyuan Ye · Yizhong Wang · Shayne Longpre · Yao Fu · Daniel Khashabi

Recent advancements in training large language models (LLMs) to follow “instructions” have significantly increased their ability to comprehend open-ended language commands, encompassing a wide range of needs, preferences, and values.

This remarkable transformation has led to the creation of remarkable industrial models such as GPT-4 and Bard, as well as an increased focus within the open-source and research communities: creating new benchmark and resources, developing new training methods, and understanding the limitations of these methods. Furthermore, instruction following powered by LLMs has proven to be effective in multi-modal settings, with applications in image editing and robotic command execution.

We organize this workshop to facilitate discussions on advancing instruction tuning methodologies and constructing general-purpose instruction-following models. We believe it is crucial to organize this workshop due to the prevalence of proprietary models with restricted access, thereby creating the need for an open platform to encourage discussions. Moreover, we aim to foster interdisciplinary collaboration by bringing together researchers from diverse fields such as natural language processing, computer vision, robotics, human-computer interaction, AI safety, among others, to share their latest findings and explore potential avenues for future research.


Table Representation Learning Workshop Fri 15 Dec 08:30 a.m.  

Madelon Hulsebos · Bojan Karlaš · Haoyu Dong · Gael Varoquaux · Laurel Orr · Pengcheng Yin

Tables are a promising modality for representation learning with too much application potential to ignore. However, tables have long been overlooked despite their dominant presence in the data landscape, e.g. data management and analysis pipelines. The majority of datasets in Google Dataset Search, for example, resembles typical tabular file formats like CSVs. Similarly, the top-3 most-used database management systems are all relational (RDBMS). Representation learning over tables (TRL), possibly combined with other modalities such as text or SQL, has shown impressive performance for tasks like table-based question answering, table understanding, and data preparation. More recently, TRL was shown to be effective for tabular ML as well, while researchers also started exploring the impressive capabilities of LLMs for table encoding and data manipulation. Follow our Twitter feed for updates: https://twitter.com/TrlWorkshop.

The first edition of the Table Representation Learning (TRL) workshop at NeurIPS 2022 gathered an enthusiastic community and stimulated new research and collaborations, which we aim to continue in 2023. The TRL workshop has three main goals:

(1) Motivate tables as a primary modality for representation and generative learning and advance the area further.
(2) Showcase impactful applications of pretrained table models and discussing future opportunities.
(3) Foster discussion and collaboration across the ML, NLP and DB communities.


Machine Learning in Structural Biology Workshop Fri 15 Dec 08:30 a.m.  

Hannah Wayment-Steele · Roshan Rao · Ellen Zhong · Sergey Ovchinnikov · Gabriele Corso · Gina El Nesr

Structural biology, the study of the 3D structure or shape of proteins and other biomolecules, has been transformed by breakthroughs from machine learning algorithms. While methods such as AlphaFold2 have made exponential progress in certain areas, many active and open challenges for the field remain, including modeling protein dynamics, predicting the structure of other classes of biomolecules such as RNA, and ultimately relating the structure of isolated proteins to the in vivo and contextual nature of their underlying function. These challenges are diverse and require interdisciplinary collaboration between ML and structural biology researchers. The 4th edition of the Machine Learning in Structural Biology (MLSB) workshop focuses on these challenges and opportunities. In a unique commitment of support, PRX Life journal has committed to waiving publication fees for accepted papers in a special collection for interested authors. We anticipate this workshop will be of significant interest to both ML researchers as well as computational / experimental biologists and will stimulate continued problem-solving and new directions in the field.


Competition: Melting Pot Contest Fri 15 Dec 08:35 a.m.  

Rakshit Trivedi · Akbir Khan · Jesse Clifton · Lewis Hammond · John Agapiou · Edgar Dueñez-Guzman · Jayd Matyas · Dylan Hadfield-Menell · Joel Leibo

Multi-agent AI research promises a path to develop human-like and human-compatible intelligent technologies that complement the solipsistic view of other approaches, which mostly do not consider interactions between agents. We propose a Cooperative AI contest based on the Melting Pot framework. At its core, Melting Pot provides an evaluation protocol that measures generalization to novel social partners in a set of canonical test scenarios. There exist several benchmarks, challenges, and contests aimed at spurring research on cooperation in multi-agent learning. Melting Pot expands and generalizes these previous efforts in several ways: (1) it focuses on mixed-motive games, (as opposed to purely cooperative or competitive games); (2) it enables testing generalizability of agent cooperation to previously unseen coplayers; (3) it consists of a suite of multiple environments rather than a single one; and (4) it includes games with larger numbers of players (> 7). These properties make it an accessible while also challenging framework for multi-agent AI research. For this contest, we invite multi-agent reinforcement learning solutions that focus on driving cooperation between interacting agents in the Melting Pot environments and generalize to new situations beyond training. A scoring mechanism based on metrics representative of cooperative intelligence will be used to measure success of the solutions. We believe that Melting Pot can serve as a clear benchmark to drive progress on Cooperative AI, as it focuses specifically on evaluating social intelligence of both groups and individuals. As an overarching goal, we are excited in assessing the implications of current definitions of cooperative intelligence on resulting solution approaches and studying the emerging behaviors of proposed solutions to inform future research directions in Cooperative AI.


Workshop: Computational Sustainability: Promises and Pitfalls from Theory to Deployment Fri 15 Dec 08:45 a.m.  

Suzanne Stathatos · Christopher Yeh · Laura Greenstreet · Tarun Sharma · Katelyn Morrison · Yuanqi Du · Chenlin Meng · Sherrie Wang · Fei Fang · Pietro Perona · Yoshua Bengio

Computational sustainability (CompSust) is an interdisciplinary research area that uses compu- tational methods to help address the 17 United Nations Sustainable Development Goals (UN SDGs), including but not limited to hunger and poverty reduction, infrastructure development, and environmental conservation. Computational sustainability is a two-way street: sustain- ability domains benefit from computational tools and methods and computational research areas benefit from the unique challenges that arise in attempting to address sustainability problems, including noisy and biased data, complex multi-agent systems, and multi-objective problems. Previous computational sustainability problems have led to new approaches in computer vision, reinforcement learning, multi-agent systems, and decision-focused learning. While computational sustainability problems span many domains, they share common challenges. This workshop will bring the community together to focus on two topics:1. The path from theory to deployment: Many challenges arise on the path from theory to deployment. This workshop will help researchers navigate this path by bringing together participants and speakers from academia, industry, and non-profits, highlighting successes going from theory to deployment, and facilitating collaboration.2. Promises and pitfalls: Advances on ML benchmarks do not always translate to improvements in computational sustainability problems, with contributing factors including low- signal-to-noise ratios, ever changing conditions, and biased or imbalanced data. However, due to the difficulties of publishing negative results, these findings rarely reach the community leading to duplicated effort and obscuring important gaps in existing methods.The goals of this workshop are to (i) identify pathways from theory to deployment, including best-practices and measures to quantify success, (ii) facilitate discussion and collaboration between participants from academia, industry, and the non-profit sector, and (iii) identify common failure modes and high-impact research directions, including “moonshot” challenges.


Workshop: Attributing Model Behavior at Scale (ATTRIB) Fri 15 Dec 08:45 a.m.  

Tolga Bolukbasi · Logan Engstrom · Kelvin Guu · Andrew Ilyas · Sam Park · Ellie Pavlick · Anders Søgaard

Recently-developed algorithmic innovations (e.g., transformers, diffusion models ) and large-scale datasets (e.g., Common Crawl, LAION) have given rise to machine learning models with impressive capabilities. However, there is much left to understand in how these different factors combine to give rise to observed behaviors. For example, we still do not fully understand how the composition of training datasets influence downstream model capabilities (e.g., which data sources within LAION-5B are important for training high-quality CLIP embeddings?), how to attribute model capabilities to subcomponents inside the model(e.g., can we identify which subnetwork of a LLM implements addition ?), and which algorithmic choices really drive performance (e.g., is RL necessary to align language models?).A common theme underlying all these challenges is model behavior attribution. That is, the need to tie model behavior back to factors in the machine learning pipeline---such as the choice of training dataset or particular training algorithm---that we can control or reason about. This workshop aims to bring together researchers and practitioners that advance our understanding of model behavior attribution in the contexts that span: data, models, and learning algorithms.


Workshop: AI meets Moral Philosophy and Moral Psychology: An Interdisciplinary Dialogue about Computational Ethics Fri 15 Dec 08:45 a.m.  

Sydney Levine · Liwei Jiang · Jared Moore · Zhijing Jin · Yejin Choi

Be it in advice from a chatbot, suggestions on how to administer resources, or which content to highlight, AI systems increasingly make value-laden decisions. However, researchers are becoming increasingly concerned about whether AI systems are making the right decisions. These emerging issues in the AI community have been long-standing topics of study in the fields of moral philosophy and moral psychology. Philosophers and psychologists have for decades (if not centuries) been interested in the systematic description and evaluation of human morality and the sub-problems that come up when attempting to describe and prescribe answers to moral questions. For instance, philosophers and psychologists have long debated the merits of utility-based versus rule-based theories of morality, their various merits and pitfalls, and the practical challenges of implementing them in resource-limited systems. They have pondered what to do in cases of moral uncertainty, attempted to enumerate all morally relevant concepts, and argued about what counts as a moral issue at all.In some isolated cases, AI researchers have slowly started to adopt the theories, concepts, and tools developed by moral philosophers and moral psychologists. For instance, we use the "trolley problem" as a tool, adopt philosophical moral frameworks to tackle contemporary AI problems, and have begun developing benchmarks that draw on psychological experiments probing moral judgment and development.Despite this, interdisciplinary dialogue remains limited. Each field uses specialized language, making it difficult for AI researchers to adopt the theoretical and methodological frameworks developed by philosophers and psychologists. Moreover, many theories in philosophy and psychology are developed at a high level of abstraction and are not computationally precise. In order to overcome these barriers, we need interdisciplinary dialog and collaboration. This workshop will create a venue to facilitate these interactions by bringing together psychologists, philosophers, and AI researchers working on morality. We hope that the workshop will be a jumping-off point for long-lasting collaborations among the attendees and will break down barriers that currently divide the disciplines. The central theme of the workshop will be the application of moral philosophy and moral psychology theories to AI practices. Our invited speakers are some of the leaders in the emerging efforts to draw on theories in philosophy or psychology to develop ethical AI systems. Their talks will demonstrate cutting-edge efforts to do this cross-disciplinary work, while also highlighting their own shortcomings (and those of the field more broadly). Each talk will receive a 5-minute commentary from a junior scholar in a field that is different from that of the speaker. We hope these talks and commentaries will inspire conversations among the rest of the attendees.


NeurIPS 2023 Workshop on Diffusion Models Fri 15 Dec 08:50 a.m.  

Bahjat Kawar · Valentin De Bortoli · Charlotte Bunne · James Thornton · Jiaming Song · Jong Chul Ye · Chenlin Meng

Over the past three years, diffusion models have established themselves as a new generative modeling paradigm. Their empirical successes have broadened the applications of generative modeling to image, video, audio, 3D synthesis, science applications, and more. As diffusion models become more and more popular and are applied to extremely diverse problems, it also becomes harder to follow the key contributions in the field. This workshop aims to keep track of recent advances and identify guidelines for future research. By bringing together practice, methodology, and theory actors we aim to identify unexplored areas, foster collaboration, and push the frontier of diffusion model research.

Link to website: https://diffusionworkshop.github.io/

Ask questions to our panelists here: https://docs.google.com/forms/d/e/1FAIpQLSeTRsWFvKlsFg31K8Vq6hHGOydmvd7YNMuOLOCcKgqSqO8mXw/viewform


Workshop on robustness of zero/few-shot learning in foundation models (R0-FoMo) Fri 15 Dec 08:50 a.m.  

Ananth Balashankar · Saurabh Garg · Jindong Gu · Amrith Setlur · Yao Qin · Aditi Raghunathan · Ahmad Beirami

Recent advances in the capabilities of large foundation models have been catalyzed by repurposing pretrained models to domain specific use cases through few-shot learning methods like prompt-tuning, in-context-learning; and zero-shot learning based on task descriptions. Given a few labeled examples that outline a new task [T5, GPT2, T0, DALL-E, CLIP], these large foundation models have demonstrably improved upon previous few-shot learning benchmarks [T-few, LAION]. We are closer than ever to learn from very few examples; and recent works [Frozen, Flamingo] have proposed methods to use large language and vision transformer models directly on these few examples, instead of human annotation to create large datasets for fine-tuning. The lessons learned from past-work in counterfactual reasoning, domain adaptation, meta-learning, continual learning, and adversarial training have to be revisited with a new lens towards improving robustness of few-shot learning methods or learning from no supervision (i.e., unlabeled data) that scale to multiple tasks in a safe and responsible manner. In addition to leveraging few-shot learning methods with labeled examples, there is also significant potential in harnessing the power of unlabeled data. When labeled and unlabeled data are from the same distribution, semi-supervised learning methods can be modified to now utilize large foundation models that can further improve boost performance over purely few-shot algorithms. Furthermore, similar ideas need to be explored for unsupervised domain adaptation, to improve robustness of fine-tuned methods to distribution shifts when the unlabeled data distribution is much broader than the distribution from which the labeled examples are collected.


Competition: Foundation Model Prompting for Medical Image Classification Challenge 2023 Fri 15 Dec 09:00 a.m.  

Dequan Wang · Xiaosong Wang · Mengzhang Li · Qian Da · DOU QI · · Shaoting Zhang · Dimitris Metaxas

The lack of public availability and quality annotations in medical image data has been the bottleneck for training large-scale deep learning models for many clinical downstream applications. It remains a tedious and time-consuming job for medical professionals to hand-label volumetric data repeatedly while providing a few differentiable sample cases is more logically feasible and complies with the training process of medical residents. The proposed challenge aims to advance technique in prompting large-scale pre-trained foundation models via a few data samples as a new paradigm for medical image analysis, e.g., classification tasks proposed here as use cases. It aligns with the recent trend and success of building foundation models (e.g., Vision Transformers, GPT-X, and CLIP) for a variety of downstream applications. Three private datasets for different classification tasks, i.e., thoracic disease classification, pathological tumor tissue classification, and colonoscopy lesion classification, are composed as the training (few samples) and validation sets (the rest of each dataset). Participants are encouraged to advance cross-domain knowledge transfer techniques in such a setting and achieve higher performance scores in all three tasks. The final evaluation will be conducted in the same tasks on the reserved private datasets.


Workshop: New Frontiers in Graph Learning (GLFrontiers) Fri 15 Dec 09:00 a.m.  

Jiaxuan You · Rex Ying · Hanjun Dai · Ge Liu · Azalia Mirhoseini · Smita Krishnaswamy · Chaoran Cheng

Overview: Graph learning has grown into an established sub-field of machine learning in recent years. 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. With the success of the New Frontiers in Graph Learning (GLFrontiers) Workshop in NeurIPS 2022, we hope to continue to promote the exchange of discussions and ideas regarding the future of graph learning in NeurIPS 2023.Challenges: Despite the success of graph learning in various applications, the recent machine learning research trends, especially the research towards foundation models and large language models, have posed challenges for the graph learning field. For example, regarding the model architecture, Transformer-based models have been shown to be superior to graph neural networks in certain small graph learning benchmarks. In terms of usability, with language as a generic user interface, it is still a research frontier to explore whether natural language can also interact with ubiquitous graph-structured data and whether it is feasible to build generic foundation models for graphs. Lastly, while graph learning has achieved recent exciting results in molecule and protein design, exploring how graph learning can accelerate scientific discoveries in other disciplines remains an open question.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. In GLFrontiers 2023, we specifically aim to discuss the future of graph learning in the era of foundation models and envision how graph learning can contribute to scientific discoveries.


6th Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response Fri 15 Dec 09:00 a.m.  

Ritwik Gupta · Thomas Manzini · Robin Murphy · Eric Heim · Bertrand Saux · Katie Picchione

Natural disasters are one of the oldest threats to both individuals and the societies they co-exist in. As a result, humanity has ceaselessly sought way to provide assistance to people in need after disasters have struck. Further, natural disasters are but a single, extreme example of the many possible humanitarian crises. Disease outbreak, famine, and oppression against disadvantaged groups can pose even greater dangers to people that have less obvious solutions. In this proposed workshop, we seek to bring together the Artificial Intelligence (AI) and Humanitarian Assistance and Disaster Response (HADR) communities in order to bring AI to bear on real-world humanitarian crises. Through this workshop, we intend to establish meaningful dialogue between the communities.By the end of the workshop, the NeurIPS research community can come to understand the practical challenges of aiding those who are experiencing crises, while the HADR community can understand the landscape that is the state of art and practice in AI. Through this, we seek to begin establishing a pipeline of transitioning the research created by the NeurIPS community to real-world humanitarian issues.


Workshop on Distribution Shifts: New Frontiers with Foundation Models Fri 15 Dec 09:00 a.m.  

Rebecca Roelofs · Fanny Yang · Hongseok Namkoong · Masashi Sugiyama · Jacob Eisenstein · Pang Wei Koh · Shiori Sagawa · Tatsunori Hashimoto · Yoonho Lee

Tagline: This workshop focuses on distribution shifts in the context of foundation models.Distribution shifts---where a model is deployed on a data distribution different from what it was trained on---pose significant robustness challenges in real-world ML applications. Such shifts are often unavoidable in the wild and have been shown to substantially degrade model performance in a wide range of applications. For example, models can systematically fail when tested on patients from different hospitals or people from different demographics. Training models that are robust to such distribution shifts is a rapidly growing area of interest in the ML community, and the goal of our workshop is to foster discussions and further research on distribution shifts. In the context of distribution shifts, our workshop this year focuses on foundation models: large pretrained models that can be adapted for a wide range of tasks. Foundation models open up an exciting new frontier in the study of distribution shifts, raising open research questions such as how pre-training improves robustness, how to finetune foundation models for increased robustness, how to leverage foundation models’ generative capabilities for robustness, and how to handle discrepancies between standard pre-training distributions and downstream distributions of interest. We aim to facilitate discussions around these topics by bringing together researchers working on distribution shifts and foundation models.


Workshop: Goal-Conditioned Reinforcement Learning Fri 15 Dec 09:00 a.m.  

Benjamin Eysenbach · Ishan Durugkar · Jason Ma · Andi Peng · Tongzhou Wang · Amy Zhang

Learning goal-directed behavior is one of the classical problems in AI, one that has received renewed interest in recent years and currently sits at the crossroads of many seemingly-disparate research threads: self-supervised learning , representation learning, probabilistic inference, metric learning, and duality.

Our workshop focuses on these goal-conditioned RL (GCRL) algorithms and their connections to different areas of machine learning. Goal-conditioned RL is exciting not just because of these theoretical connections with different fields, but also because it promises to lift some of the practical challenges with applying RL algorithms: users can specify desired outcomes with a single observation, rather than a mathematical reward function. As such, GCRL algorithms may be applied to problems varying from robotics to language models tuning to molecular design to instruction following.

Our workshop aims to bring together researchers studying the theory, methods, and applications of GCRL, researchers who might be well posed to answer questions such as:

1. How does goal-directed behavior in animals inform better GCRL algorithmic design?
2. How can GCRL enable more precise and customizable molecular generation?
3. Do GCRL algorithms provide an effective mechanism for causal reasoning?
4. When and how should GCRL algorithms be applied to precision medicine?


Agent Learning in Open-Endedness Workshop Fri 15 Dec 09:00 a.m.  

Minqi Jiang · Mikayel Samvelyan · Jack Parker-Holder · Mayalen Etcheverry · Yingchen Xu · Michael Dennis · Roberta Raileanu

Open-ended learning (OEL) is receiving rapidly growing attention in recent years, as deep learning models become ever more adept at learning meaningful and useful behaviors from web-scale data. Improving the performance and generality of such models depends greatly on our ability to continue to collect new and useful training data. OEL systems co-evolve the learning agent (e.g. the model) with its environment or other sources of training data, resulting in the continued, active generation of new training data specifically useful for the current agent or model. Conceivably such OEL processes, if designed appropriately, can lead to models exhibiting increasingly general capabilities. However, it remains an open problem to produce a truly open-ended system in practice, one that endlessly generates meaningfully novel data. We hope our workshop provides a forum both for bridging knowledge across a diverse set of relevant fields as well as sparking new insights that can enable truly open-ended learning systems.


Workshop: Heavy Tails in ML: Structure, Stability, Dynamics Fri 15 Dec 09:00 a.m.  

Mert Gurbuzbalaban · Stefanie Jegelka · Michael Mahoney · Umut Simsekli

Heavy-tails and chaotic behavior naturally appear in many ways in ML. We aim to understand how they emerge and how they affect the properties of ML methods.


Workshop: OPT 2023: Optimization for Machine Learning Fri 15 Dec 09:00 a.m.  

Cristóbal Guzmán · Courtney Paquette · Katya Scheinberg · Aaron Sidford · Sebastian Stich

Optimization lies at the heart of many machine learning algorithms and enjoys great interest in our community. Indeed, this intimate relation of optimization with ML is the key motivation for the OPT series of workshops. We aim to foster discussion, discovery, and dissemination of state-of-the-art research in optimization relevant to ML.

To foster the spirit of innovation and collaboration, a goal of this workshop, OPT 2023 will focus the contributed talks on research in "Optimization in the Wild"; this title is meant to encompass the new challenges that traditional optimization theory and algorithms face with the growth and variety of novel ML applications.

Successful applications of both theory and algorithms from optimization to ML frequently require a profound redesign or even entirely new approaches. This becomes apparent in settings where the classical (empirical) risk minimization approach is no longer sufficient to address the challenges of learning. As motivating examples, we consider the case of learning under (group or individual) fairness in distributed scenarios, learning under differential privacy, robustness, multi-task and transfer learning, as well as sampling from log-concave distributions. On the other hand, novel neural network architectures (such as transformers) require exploiting its structures for efficient optimization in crucial ways. For these models and problems: What is the role of optimization? What synergies can be exploited with the insights coming from these particular areas towards more efficient and reliable solutions? We will foster discussions directed at developing understanding of these challenges, and raising awareness of the capabilities and risks of using optimization in each of these areas.


Workshop: Backdoors in Deep Learning: The Good, the Bad, and the Ugly Fri 15 Dec 09:00 a.m.  

Khoa D Doan · Aniruddha Saha · Anh Tran · Yingjie Lao · Kok-Seng Wong · Ang Li · HARIPRIYA HARIKUMAR · Eugene Bagdasarian · Micah Goldblum · Tom Goldstein

Deep neural networks (DNNs) are revolutionizing almost all AI domains and have become the core of many modern AI systems. While having superior performance compared to classical methods, DNNs are also facing new security problems, such as adversarial and backdoor attacks, that are hard to discover and resolve due to their black-box-like property. Backdoor attacks, particularly, are a brand-new threat that was only discovered in 2017 but has gained attention quickly in the research community. The number of backdoor-related papers grew from 21 to around 110 after only one year (2019-2020). In 2022 alone, there were more than 200 papers on backdoor learning, showing a high research interest in this domain.Backdoor attacks are possible because of insecure model pretraining and outsourcing practices. Due to the complexity and the tremendous cost of collecting data and training models, many individuals/companies just employ models or training data from third parties. Malicious third parties can add backdoors into their models or poison their released data before delivering it to the victims to gain illegal benefits. This threat seriously damages the safety and trustworthiness of AI development. Lately, many studies on backdoor attacks and defenses have been conducted to prevent this critical vulnerability.While most works consider backdoor ``evil'', some studies exploit it for good purposes. A popular approach is to use the backdoor as a watermark to detect illegal use of commercialized data/models. A few works employ the backdoor as a trapdoor for adversarial defense. Learning the working mechanism of backdoor also elevates a deeper understanding of how deep learning models work.This workshop is designed to provide a comprehensive understanding of the current state of backdoor research. We also want to raise awareness of the AI community on this important security problem, and motivate researchers to build safe and trustful AI systems.


Competition: Privacy Preserving Federated Learning Document VQA Fri 15 Dec 09:00 a.m.  

Dimosthenis Karatzas · Rubèn Tito · Lei Kang · Mohamed Ali Souibgui · Khanh Nguyen · Raouf Kerkouche · Kangsoo Jung · Marlon Tobaben · Joonas Jälkö · Vincent Poulain d'Andecy · Aurélie JOSEPH · Ernest Valveny · Josep Llados · Antti Honkela · Mario Fritz

In an era of increasing digitalization and data-driven decision-making, the intersection of document intelligence and privacy has become a critical concern. The Privacy-Preserving Federated Learning Document Visual Question Answering Workshop aims to bring together experts, researchers, and practitioners to explore innovative solutions and discuss the latest advancements in this crucial field.

Join us for insightful invited talks by leading figures in the field. These talks will provide valuable perspectives on the current state of privacy-preserving document intelligence and its future directions. Get an in-depth look at the Privacy-Preserving Document Visual Question Answering Competition that we are currently holding, with a detailed overview of the competition, the dataset, and the competition results. Moreover, the top winners of the competition will have the opportunity to give short talks about their winning methods and strategies. Gain firsthand insights into the innovative approaches that led to their success.

Workshop URL: https://sites.google.com/view/pfldocvqa-neurips-23/home
Associated Competition URL: https://benchmarks.elsa-ai.eu/?ch=2


NeurIPS 2023 Machine Unlearning Competition Fri 15 Dec 09:00 a.m.  

Eleni Triantafillou · Fabian Pedregosa · Meghdad Kurmanji · Kairan ZHAO · Gintare Karolina Dziugaite · Peter Triantafillou · Ioannis Mitliagkas · Vincent Dumoulin · Lisheng Sun · Peter Kairouz · Julio C Jacques Junior · Jun Wan · Sergio Escalera · Isabelle Guyon

We are proposing the first competition on machine unlearning, to our knowledge. Unlearning is a rapidly growing area of research that has emerged in response to one of the most significant challenges in deep learning: allowing users to exercise their right to be forgotten. This is particularly challenging in the context of deep models, which tend to memorize information from their training data, thus compromising privacy. The lack of a standardized evaluation protocol has hindered the development of unlearning, which is a relatively new area of research. Our challenge is designed to fill this need. By incentivizing the development of better unlearning algorithms, informing the community of their relative strengths and weaknesses, and unifying evaluation criteria, we expect our competition to have a significant impact. We propose a realistic scenario for unlearning face images.


Competition: The NeurIPS 2023 Neural MMO Challenge: Multi-Task Reinforcement Learning and Curriculum Generation Fri 15 Dec 09:00 a.m.  

Joseph Suarez · Phillip Isola · David Bloomin · Kyoung Whan Choe · Hao Li · Ryan Sullivan · Nishaanth Kanna · Daniel Scott · Rose Shuman · Herbie Bradley · Louis Castricato · Chenghui Yu · Yuhao Jiang · Qimai Li · Jiaxin Chen · Xiaolong Zhu · Dipam Chakrabroty · Sharada Mohanty · Nikhil Pinnaparaju

In this competition, participants train agents to complete a variety of tasks in Neural MMO 2.0 including foraging, combat, tool acquisition and usage, and item trading. Neural MMO is a simulated environment featuring 128 players, procedurally generated maps, and emergent complexity from interactions among agents. The competition features three tracks: two compute-limited academic tracks focused on multi-agent reinforcement learning and curriculum generation and one unrestricted track. This is the fourth challenge on Neural MMO, and the previous competitions have all yielded state-of-the-art performance on earlier versions of this environment as well as more general improvements to learning methods. The NeurIPS workshop will include presentations by the developers and by researchers in reinforcement learning and open-endedness.


Competition: The Robot Air Hockey Challenge: Robust, Reliable, and Safe Learning Techniques for Real-world Robotics Fri 15 Dec 09:00 a.m.  

Puze Liu · Jonas Günster · Niklas Funk · Dong Chen · Haitham Bou Ammar · Davide Tateo · Ziyuan Liu · Jan Peters

While machine learning methods demonstrated impressive success in many application domains, their impact on real robotic platforms is still far from their potential.To unleash the capabilities of machine learning in the field of robotics, researchers need to cope with specific challenges and issues of the real world. While many robotics benchmarks are available for machine learning, most simplify the complexity of classical robotics tasks, for example neglecting highly nonlinear dynamics of the actuators, such as stiction. We organize the robot air hockey challenge, which allows machine learning researchers to face the sim-to-real-gap in a complex and dynamic environment while competing with each other. In particular, the challenge focuses on robust, reliable, and safe learning techniques suitable for real-world robotics. Through this challenge, we wish to investigate how machine learning techniques can outperform standard robotics approaches in challenging robotic scenarios while dealing with safety, limited data usage, and real-time requirements.


Competition: Weather4cast 2023 – Data Fusion for Quantitative Hi-Res Rain Movie Prediction under Spatio-temporal Shifts Fri 15 Dec 09:00 a.m.  

Aleksandra Gruca · Pilar Rípodas · Xavier Calbet · Llorenç Lliso Valverde · Federico Serva · Bertrand Le Saux · Michael Kopp · David Kreil · Sepp Hochreiter

The competition will advance modern algorithms in AI and machine learning through a highly topical interdisciplinary competition challenge: The prediction of hi-res rain radar movies from multi-band satellite sensors requires data fusion of complementary signal sources, multi-channel video frame prediction, as well as super-resolution techniques. To reward models that extract relevant mechanistic patterns reflecting the underlying complex weather systems our evaluation incorporates spatio-temporal shifts: Specifically, algorithms need to forecast 8h of ground-based hi-res precipitation radar from lo-res satellite spectral images in a unique cross-sensor prediction challenge. Models are evaluated within and across regions on Earth with diverse climate and different distributions of heavy precipitation events. Conversely, robustness over time is achieved by testing predictions on data one year after the training period.Now, in its third edition, weather4cast 2023 moves to improve rain forecasts world-wide on an expansive data set and novel quantitative prediction challenges. Accurate rain predictions are becoming ever more critical for everyone, with climate change increasing the frequency of extreme precipitation events. Notably, the new models and insights will have a particular impact for the many regions on Earth where costly weather radar data are not available.


Workshop: Algorithmic Fairness through the Lens of Time Fri 15 Dec 09:00 a.m.  

Awa Dieng · Miriam Rateike · Golnoosh Farnadi · Ferdinando Fioretto · Jessica Schrouff
We are proposing the Algorithmic Fairness through the Lens of Time (AFLT) workshop, which isthe fourth edition of this workshop series on algorithmic fairness. Previous editions have looked atcausal approaches to fairness and the intersection of fairness with other fields of trustworthy machinelearning namely interpretability, robustness and privacy.The aim of this year’s workshop is to provide a venue to discuss foundational work on fairness,challenge existing static definitions of fairness (group, individual, causal) and explore the long-termeffects of fairness methods. More importantly, the workshop aims to foster an open discussion on howto reconcile existing fairness frameworks with the development and proliferation of large generativemodels.$$$$Topic $$$$Fairness has been predominantly studied under the static regime, assuming an unchangingdata generation process [Hardt et al., 2016a, Dwork et al., 2012, Agarwal et al., 2018, Zafar et al.,2017]. However, these approaches neglect the dynamic interplay between algorithmic decisions andthe individuals they impact, which have shown to be prevalent in practical settings [Chaney et al.,2018, Fuster et al., 2022]. Such observation has highlighted the need to study the long term effectof fairness mitigation strategies and incorporate dynamic systems within the development of fairalgorithms.Despite prior research identifying several impactful scenarios where such dynamics can occur,including bureaucratic processes [Liu et al., 2018], social learning [Heidari et al., 2019], recourse[Karimi et al., 2020], and strategic behavior [Hardt et al., 2016b, Perdomo et al., 2020], extensiveinvestigation of the long term effect of fairness methods remains limited. Initial studies have shownhow enforcing static fairness constraints in dynamical systems can lead to unfair data distributionsand may perpetuate or even amplify biases [Zhang et al., 2020, Creager et al., 2020, D’Amour et al.,2020].Additionally, the rise of powerful large generative models have brought at the forefront the need tounderstand fairness in evolving systems. The general capabilities and widespread use of these modelsraise the critical question of how to assess these models for fairness[Luccioni et al., 2023] and mitigateobserved biases [Ranaldi et al., 2023, Ma et al., 2023] within a long term perspective. Importantly,mainstream fairness frameworks have been developed around classification and prediction tasks. Howcan we reconcile these existing techniques (proprocessing, in-processing and post-processing) withthe development of large generative models?Given these interesting questions, this workshop aims to deeply investigate how to address fairness concerns in settings where learning occurs sequentially or in evolving environments. We are particularly interested in addressing open questions in the field, such as:• What are the long term effects of static fairness methods?• How to develop adaptable fairness approaches under known or unknown dynamic environments?• Are there trade-offs between short-term and long-term fairness?• How to incorporate existing fairness frameworks into the development of large generativemodels?• How to ensure long term fairness in large generative models via feedback loops?

MATH-AI: The 3rd Workshop on Mathematical Reasoning and AI Fri 15 Dec 09:00 a.m.  

Zhenwen Liang · Albert Q. Jiang · Katie Collins · Pan Lu · Kaiyu Yang · Sean Welleck · James McClelland

Mathematical reasoning is a fundamental aspect of human cognition that has been studied by scholars ranging from philosophers to cognitive scientists and neuroscientists. Mathematical reasoning involves analyzing complex information, identifying patterns and relationships, and drawing logical conclusions from evidence. It is central to many applications in science, engineering, finance, and everyday contexts. Recent advancements in large language models (LLMs) have unlocked new opportunities at the intersection of artificial intelligence and mathematical reasoning, ranging from new methods that solve complex problems or prove theorems, to new forms of human-machine collaboration in mathematics and beyond. Our proposed workshop is centered on the intersection of deep learning and mathematical reasoning, with an emphasis on, but not limited to, large language models. Our guiding theme is: "To what extent can machine learning models comprehend mathematics, and what applications could arise from this capability?'' To address this question, we aim to bring together a diverse group of scholars from different backgrounds, institutions, and disciplines in our workshop. By hosting this workshop, we hope to stimulate insightful discussions that will guide future research and applications in this rapidly expanding field.


Competition: The CityLearn Challenge 2023 Fri 15 Dec 10:00 a.m.  

Zoltan Nagy · Kingsley Nweye · Sharada Mohanty · Ruchi Choudhary · Max Langtry · Gregor Henze · Jan Drgona · Sourav Dey · Alfonso Capozzoli · Mohamed Ouf

Reinforcement learning has gained popularity as a model-free and adaptive controller for the built-environment in demand-response applications. However, a lack of standardization on previous research has made it difficult to compare different RL algorithms with each other. Also, it is unclear how much effort is required in solving each specific problem in the building domain and how well a trained RL agent will scale up to new environments. The CityLearn Challenge 2023 provides an avenue to address these problems by leveraging CityLearn, an OpenAI Gym Environment for the implementation of RL agents for demand response. The challenge utilizes a novel dataset based on the US end use load profile database. Participants are to develop energy management agents for battery charge and discharge control in each building with a goal of minimizing electricity demand from the grid, electricity bill and greenhouse gas emissions. We provide a baseline RBC agent for the evaluation of the RL agents performance and rank the participants' according to their solution's ability to outperform the baseline.


Competition: The HomeRobot Open Vocabulary Mobile Manipulation Challenge Fri 15 Dec 01:30 p.m.  

Sriram Yenamandra · Arun Ramachandran · Mukul Khanna · Karmesh Yadav · Devendra Singh Chaplot · Gunjan Chhablani · Alexander Clegg · Theophile Gervet · Vidhi Jain · Ruslan Partsey · Ram Ramrakhya · Andrew Szot · Austin Wang · Tsung-Yen Yang · Aaron Edsinger · Charles Kemp · Binit Shah · Zsolt Kira · Dhruv Batra · Roozbeh Mottaghi · Yonatan Bisk · Chris Paxton

Deploying robots in real human environments requires a full hardware and software stack, that includes everything from perception to manipulation, in simulation and on accessible physical hardware. The lack of a single unified resource providing these capabilities means that the academic literature often focuses on creating agents in simulation or on one-off hardware, preventing comprehensive benchmarking and reproducibility. We present the first Open-Vocabulary Mobile Manipulation challenge with diverse assets and environments in simulation and a different, held-out set of physical objects in a novel real-world environment. We provide an entire robotics software stack that is modular, fully open-source, and centered on a popular low-cost hardware platform for easy replication and extension by the research community. Machine learning has benefited greatly from the standardization of high-quality engineering; our work aims to lower the cost of entry to robotics.We have assembled a team that spans Georgia Tech, Carnegie Mellon, Meta and Hello Robot to enable physical robot evaluations at NeurIPS. A simulator is ready for deployment and physical kitchens have been constructed for use as a real-world test set. We present development environments here and a peek at the construction of our full held-out test apartment being constructed in Fremont, California. Crucially, we are releasing a full software control stack for the Hello Robot Stretch, and have university partners to beta-test.


Competition: ROAD-R 2023: the Road Event Detection with Requirements Challenge Fri 15 Dec 01:30 p.m.  

Eleonora Giunchiglia · Mihaela C. Stoian · Salman Khan · Reza Javanmard alitappeh · Izzeddin A M Teeti · Adrian Paschke · Fabio Cuzzolin · Thomas Lukasiewicz

In recent years, there has been an increasing interest in exploiting readily available background knowledge in order to obtain neural models (i) able to learn from less data, and/or (ii) guaranteed to be compliant with the background knowledge corresponding to requirements about the model. In this challenge, we focus on the autonomous driving domain, and we provide our participants with the recently proposed ROAD-R dataset, which consists of 22 long videos annotated with road events together with a set of requirements expressing well known facts about the world (e.g., “a traffic light cannot be red and green at the same time”). The participants will face two challenging tasks. In the first, they will have to develop the best performing model with only a subset of the annotated data, which in turn will encourage them to exploit the requirements to facilitate training on the unlabelled portion of the dataset. In the second, we ask them to create systems whose predictions are compliant with the requirements. This is the first competition addressing the open questions: (i) If limited annotated data is available, is background knowledge useful to obtain good performance? If so, how can it be injected in deep learning models? And, (ii) how can we design effective deep learning based systems that are compliant with a set of requirements? As a consequence, this challenge is expected to bring together people from different communities, especially those interested in the general topic of Safe-AI as well as in the autonomous driving application domain, and also researchers working in the neuro-symbolic AI, semi-supervised learning and action recognition.


The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos Fri 15 Dec 01:30 p.m.  

Polina Turishcheva · Paul Fahey · Rachel Froebe · Mohammad Bashiri · Konstantin Willeke · Fabian Sinz · Andreas Tolias · Alexander Ecker

Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high- dimensional visual input. Artificial neural networks have already improved our understanding of this system by allowing computational neuroscientists to create predictive models and bridge biological and machine vision. During the Sensorium 2022 competition, we introduced benchmarks for vision models with static input (i.e. images). However, animals operate and excel in dynamic environments, making it crucial to study and understand how the brain functions under these conditions. Moreover, many biological theories, such as predictive coding, suggest that previous input is crucial for current input processing. Currently, there is no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we propose the Sensorium 2023 Benchmark Competition with dynamic input. This competition includes the collection of a new large-scale dataset from the primary visual cortex of five mice, containing responses from over 38,000 neurons to over 2 hours of dynamic stimuli per neuron. Participants in the main benchmark track will compete to identify the best predictive models of neuronal responses for dynamic input (i.e. video). We will also host a bonus track in which submission performance will be evaluated on out-of-domain input, using withheld neuronal responses to dynamic input stimuli whose statistics differ from the training set. Both tracks will offer behavioral data along with video stimuli. As before, we will provide code, tutorials, and strong pre-trained baseline models to encourage participation. We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and beyond.


Competition: Lux AI Challenge Season 2 NeurIPS Edition Fri 15 Dec 01:30 p.m.  

Stone Tao · Qimai Li · Yuhao Jiang · JIAXIN CHEN · Xiaolong Zhu · Bovard Doerschuk-Tiberi · Isabelle Pan · Addison Howard

The proposed challenge is a large-scale multi-agent environment with novel complex dynamics, featuring long-horizon planning, perfect information, and more. The challenge uniquely presents an opportunity to investigate problems at a large-scale in two forms, large-scale RL training via GPU optimized environments powered by Jax, as well as large populations of controllable units in the environments. The Lux AI Challenge Season 2 NeurIPS Edition presents a benchmark to test the scaling capabilities of solutions such as RL on environement settings of increasing scale and complexity. Participants can easily get started using any number of strong rule-based, RL, and/or imitation learning (IL) baselines. They are also given access to more than a billion frames of "play" data from the previous iteration of the competition on the small scale version of the environment previously hosted on Kaggle. Participants can submit their agents to compete against other submitted agents on a online leaderboard ranked by a Trueskill ranking system.


Competition: NeurIPS Large Language Model Efficiency Challenge: 1 LLM + 1GPU + 1Day Fri 15 Dec 01:30 p.m.  

Mark Saroufim · Weiwei Yang · Christian Puhrsch · Luca Antiga · Greg Bowyer · Driss Guessous · Artidoro Pagnoni · Supriya Rao · Joseph Isaacson · Vicki Boykis · Geeta Chauhan · aaron gonzales · Davide Eynard

Large Language Models (LLMs) have been pivotal in the recent Cambrian explosion of generative AI applications. However, existing efforts to democratize access to fine-tune and query LLMs have been largely limited by growing hardware costs required to adapt and serve these models. Enabling low cost and efficient LLM fine-tuning and inference can have significant impact on industrial and scientific applications. Here, we present a single GPU fine-tuning and inference competition. Our goal is to accelerate the development of practical software methods to reduce the costs associated with utilizing LLMs. Furthermore, by advocating for goal-oriented and infrastructure-focused evaluation frameworks that stress reproducibility, our aim is to democratize access to these methods and enhance their accessibility to the wider public.


Social: AI+Music Fri 15 Dec 05:30 p.m.  

Tianyu Liu · Jingshu Li · Tianyu Liu

Social: Commercializing Research. From idea to IPO, VC Perspective Fri 15 Dec 05:30 p.m.  

Yashwanth Reddy Virupaksha