We invite anyone interested in reinforcement learning to join us in a GatherTown-format social. The goal is to connect both new and experienced RL researchers, to share ideas and discuss their recent work. Folks from anywhere in the world are welcome to participate, provided the timing is compatible with their schedule.

Until recently, many industrial Machine Learning applications have been the remit of consulting academics, data scientists within larger companies, and a number of dedicated Machine Learning research labs within a few of the world’s most innovative tech companies. Over the last few years we have seen the dramatic rise of companies dedicated to providing Machine Learning software-as-a-service tools, with the aim of democratizing access to the benefits of Machine Learning. All these efforts have revealed major hurdles to ensuring the continual delivery of good performance from deployed Machine Learning systems. These hurdles range from challenges in MLOps, to fundamental problems with deploying certain algorithms, to solving the legal issues surrounding the ethics involved in letting algorithms make decisions for your business.

This workshop will invite papers related to the challenges in deploying and monitoring ML systems. It will encourage submission on subjects related to: MLOps for deployed ML systems; the ethics around deploying ML systems; useful tools and programming languages for deploying ML systems; specific challenges relating to deploying reinforcement learning in ML systems and performing continual learning and providing continual delivery in ML systems;
and finally data challenges for deployed ML systems.

We will also invite the submission of open problems and encourage the discussion (through two live panels) on topics related to the areas of: "Deploying machine learning applications in the legal system" and "Deploying machine learning on devices or constrained hardware".

These subjects represent a wealth of topical and high-impact issues for the community to work on.

Normalizing flows are explicit likelihood models (ELM) characterized by a flexible invertible reparameterization of high-dimensional probability distributions. Unlike other ELMs, they offer both exact and efficient likelihood computation and data generation. Since their recent introduction, flow-based models have seen a significant resurgence of interest in the machine learning community. As a result, powerful flow-based models have been developed, with successes in density estimation, variational inference, and generative modeling of images, audio and video.

As the field is moving forward, the main goal of the workshop is to consolidate recent progress and connect ideas from related fields. Over the past few years, we’ve seen that normalizing flows are deeply connected to latent variable models, autoregressive models, and more recently, diffusion-based generative models. This year, we would like to further push the forefront of these explicit likelihood models through the lens of invertible reparameterization. We encourage researchers to use these models in conjunction to exploit the their benefits at once, and to work together to resolve some common issues of likelihood-based methods, such as mis-calibration of out-of-distribution uncertainty.

The focus of this workshop is on the use of machine learning to help in addressing climate change, encompassing mitigation efforts (reducing the severity of climate change), adaptation measures (preparing for unavoidable consequences), and climate science (our understanding of the climate and future climate predictions). Topics within the scope of this workshop include climate-relevant applications of machine learning to the power sector, buildings and transportation infrastructure, agriculture and land use, extreme event prediction, disaster response, climate policy, and climate finance. The goals of the workshop are: (1) to showcase high-impact applications of ML to climate change mitigation, adaptation, and climate science, (2) to demonstrate that the associated ML methods are interesting in their own right, (3) to encourage fruitful collaboration between the ML community and a diverse set of researchers and practitioners from climate change-related fields, and (4) to promote dialogue with decision-makers in the private and public sectors, ensuring that the works presented in this workshop have impact on the thoughtful deployment of ML in climate solutions. Building on our previous workshops in this series, this workshop will have a particular focus on ML for the assessment and implementation of objectives set under the Paris Agreement, though submitted works may be on any topic at the intersection of ML and climate change.

The proposed workshop pays a special interests in theoretic foundations, limitations, and new application trends in the scope of XAI. These issues reflect new bottlenecks in the future development of XAI, for example: (1) no theoretic definition of XAI and no solid and widely-used formulation for even a specific explanation task. (2) No sophisticated formulation of the essence of ``semantics'' encoded in a DNN. (3) How to bridge the gap between connectionism and symbolism in AI research has not been sophisticatedly explored. (4) How to evaluate the correctness and trustworthiness of an explanation result is still an open problem. (5) How to bridge the intuitive explanation (e.g., the attribution/importance-based explanation) and a DNN's representation capacity (e.g., the generalization power) is still a significant challenge. (6) Using the explanation to guide the architecture design or substantially boost the performance of a DNN is a bottleneck. Therefore, this workshop aims to bring together researchers, engineers as well as industrial practitioners, who concern about the interpretability, safety, and reliability of artificial intelligence. In this workshop, we hope to use a broad discussion on the above bottleneck issues to explore new critical and constructive views of the future development of XAI. Research outcomes are also expected to profoundly influences critical industrial applications such as medical diagnosis, finance, and autonomous driving.

Accepted papers: https://arxiv.org/html/2107.08821

Machine learning systems are commonly applied to isolated tasks (such as image recognition or playing chess) or narrow domains (such as control over similar robotic bodies). It is further assumed that the learning system has simultaneous access to all annotated data points of the tasks at hand. In contrast, Continual Learning (CL), also referred to as Lifelong or Incremental Learning, studies the problem of learning from a stream of data from changing domains, each connected to a different learning task. The objective of CL is to quickly adapt to new situations or tasks by exploiting previously acquired knowledge, while protecting previous learning from being erased.

Significant advances have been made in CL over the past few years, mostly through empirical investigations and benchmarking. However, theoretical understanding is still lagging behind. For instance, while Catastrophic Forgetting (CF) is a recurring ineffectiveness that most works try to tackle, little understanding is provided in the literature from a theoretical point of view. Many real life applications share common assumptions and settings with CL, what are the convergence guarantees when deploying a certain method? If memory capacity is an important constraint for replay methods, how can we select the minimal examples such that CF is minimized? While answers to the questions above are key ingredients to design better heuristics, very little theoretical guidance is provided in the literature.

The aim of this workshop is to achieve an understanding of different components of continual learning to bridge the gap with empirical results. Furthermore, we are also interested in submissions that draw connections between Continual Learning and other areas, such as Neuroscience and Meta-learning.

For more info visit our workshop website

Unsupervised learning has begun to deliver on its promise in the recent past with tremendous progress made in the fields of natural language processing and computer vision whereby large scale unsupervised pre-training has enabled fine-tuning to downstream supervised learning tasks with limited labeled data. This is particularly encouraging and appealing in the context of reinforcement learning considering that it is expensive to perform rollouts in the real world with annotations either in the form of reward signals or human demonstrations. We therefore believe that a workshop in the intersection of unsupervised and reinforcement learning is timely and we hope to bring together researchers with diverse views on how to make further progress in this exciting and open-ended subfield.

A key challenge for the successful deployment of many real world human-facing automated sequential decision-making systems is the need for human-AI collaboration. Effective collaboration ensures that the complementary abilities and skills of the human-users and the AI system are leveraged to maximize utility. This is for instance important in applications such as autonomous driving, in which a human user’s skill might be required in safety critical situations, or virtual personal assistants, in which a human user can perform real-world physical interactions which the AI system cannot. Facilitating such collaboration requires cooperation, coordination, and communication, e.g., in the form of accountability, teaching interactions, provision of feedback, etc. Without effective human-AI collaboration, the utility of automated sequential decision-making systems can be severely limited. Thus there is a surge of interest in better facilitating human-AI collaboration in academia and industry. Most existing research has focussed only on basic approaches for human-AI collaboration with little focus on long-term interactions and the breadth needed for next-generation applications. In this workshop we bring together researchers to advance this important topic, focussing on the following three directions: (a) Accountability and trust; (b) Adaptive behavior for long-term collaboration; (c) Robust collaboration under mismatch.

One of the fundamental promises of deep learning is its ability to build increasingly meaningful representations of data from complex but raw inputs. These techniques demonstrate remarkable efficacy on high dimensional data with unique proximity structures (image, natural language, graphs).

Not only are these types of data prevalent in financial services and e-commerce, but also they often capture extremely interesting aspects of social and economic behavior. For example, financial transactions and online purchases can be viewed as edges on graphs of economic activity. To date, these graphs are far less studied than social networks, though they provide a unique look at behavior, social structures, and risk. ​Meanwhile, activity or transaction sequences, usually determined by user sessions, can reflect the users’ long term and short term interests, which can be modeled by sequential models, and used to predict the user’s future activities. Although language models have been explored in session data modeling, how to re-use the representations learned from one job to another job effectively is still an open question.

Our goal is to bring together researchers from different domains to discuss the application of representation learning to financial services and e-commerce. For the first time, four major e-commerce companies (Amazon, Walmart, Alibaba and eBay) and two banks (JP Morgan and Capital One) have come together to organize this workshop along with researchers from academia. A shared goal across these industries and application areas is to transform large-scale representational data into tangible revenue for businesses. Towards this goal, our confirmed invited speakers will share diverse perspectives on ways that representation learning can be used to solve problems in financial services and e-commerce. This will also be a forum to share how research on financial services and e-commerce data provides unique insights into socio-economic behavior.

Reinforcement learning (RL) is a general learning, predicting, and decision making paradigm and applies broadly in many disciplines, including science, engineering and humanities. RL has seen prominent successes in many problems, such as games, robotics, recommender systems. However, applying RL in the real world remains challenging, and a natural question is:

Why isn’t RL used even more often and how can we improve this?

The main goals of the workshop are to: (1) identify key research problems that are critical for the success of real-world applications; (2) report progress on addressing these critical issues; and (3) have practitioners share their success stories of applying RL to real-world problems, and the insights gained from such applications.

We invite paper submissions successfully applying RL algorithms to real-life problems and/or addressing practically relevant RL issues. Our topics of interest are general, including (but not limited to): 1) practical RL algorithms, which covers all algorithmic challenges of RL, especially those that directly address challenges faced by real-world applications; 2) practical issues: generalization, sample efficiency, exploration, reward, scalability, model-based learning, prior knowledge, safety, accountability, interpretability, reproducibility, hyper-parameter tuning; and 3) applications.

We have 6 premier panel discussions and 70+ great papers/posters. Welcome!

Machine learning (ML) has achieved considerable successes in recent years, but this success often relies on human experts, who construct appropriate features, design learning architectures, set their hyperparameters, and develop new learning algorithms. Driven by the demand for robust, off-the-shelf ML methods from an ever-growing community, the research area of AutoML targets the progressive automation of machine learning aiming to make effective methods available to everyone. Hence, the workshop targets a broad audience ranging from core ML researchers in different fields of ML connected to AutoML, such as neural architecture search (NAS), hyperparameter optimization, meta-learning, and learning-to-learn, to domain experts aiming to apply ML to new types of problems.

There has been growing interest in ensuring that deep learning systems are robust and reliable. Challenges arise when models receive samples drawn from outside the training distribution. For example, a neural network tasked with classifying handwritten digits may assign high confidence predictions to cat images. Anomalies are frequently encountered when deploying ML models in the real world. Well-calibrated predictive uncertainty estimates are indispensable for many machine learning applications, such as self-driving vehicles and medical diagnosis systems. Generalization to unseen and worst-case inputs is also essential for robustness to distributional shift. In order to have ML models safely deployed in open environments, we must deepen technical understanding in the following areas:

(1) Learning algorithms that can detect changes in data distribution (e.g. out-of-distribution examples) and improve out-of-distribution generalization (e.g. temporal, geographical, hardware, adversarial shifts);
(2) Mechanisms to estimate and calibrate confidence produced by neural networks in typical and unforeseen scenarios;
(3) Guide learning towards an understanding of the underlying causal mechanisms that can guarantee robustness with respect to distribution shift.

In order to achieve these goals, it is critical to dedicate substantial effort on
(4) Creating benchmark datasets and protocols for evaluating model performance under distribution shift
(5) Studying key applications of robust and uncertainty-aware deep learning (e.g., computer vision, robotics, self-driving vehicles, medical imaging), as well as broader machine learning tasks.

This workshop will bring together researchers and practitioners from the machine learning communities to foster future collaborations. Our agenda will feature invited speakers, contributed talks, poster sessions in multiple time-zones and a panel discussion on fundamentally important directions for robust and reliable deep learning.

Applying machine learning (ML) in healthcare is gaining momentum rapidly. However, the black-box characteristics of existing ML approaches inevitably lead to less interpretability and verifiability in making clinical predictions. To enhance the interpretability of medical intelligence, it becomes critical to develop methodologies to explain predictions as these systems are pervasively being introduced to the healthcare domain, which requires a higher level of safety and security. Such methodologies would make medical decisions more trustworthy and reliable for physicians, which could facilitate the deployment ultimately. On the other hand, it is also essential to develop more interpretable and transparent ML systems. For instance, by exploiting structured knowledge or prior clinical information, one can design models to learn aspects more coherent with clinical reasoning. Also, it may help mitigate biases in the learning process, or identify more relevant variables for making medical decisions.

In this workshop, we aim to bring together researchers in ML, computer vision, healthcare, medicine, NLP, and clinical fields to facilitate discussions including related challenges, definition, formalisms, evaluation protocols regarding interpretable medical machine intelligence. Additionally, we will also introduce possible solutions such as logic and symbolic reasoning over medical knowledge graphs, uncertainty quantification, composition models, etc. We hope that the proposed workshop is fruitful in offering a step toward building autonomous clinical decision systems with a higher-level understanding of interpretability.

Differential privacy is a promising approach to privacy-preserving data analysis. It has been the subject of a decade of intense scientific study, and has now been deployed in products at government agencies such as the U.S. Census Bureau and companies like Microsoft, Apple, and Google. MIT Technology Review named differential privacy one of 10 breakthrough technologies of 2020.
Since data privacy is a pervasive concern, differential privacy has been studied by researchers from many distinct communities, including machine learning, statistics, algorithms, computer security, cryptography, databases, data mining, programming languages, social sciences, and law. We believe that this combined effort across a broad spectrum of computer science is essential for differential privacy to realize its full potential. To this end, our workshop will stimulate discussion among participants about both the state-of-the-art in differential privacy and the future challenges that must be addressed to make differential privacy more practical.

Using Process Mining and Machine Learning to Optimize Business Processes in HealthCare, in this case, data will be used to show the various process mining approaches to optimize the healthcare processes using various algorithms and machine learning use integration with Process mining.

As causality enjoys increasing attention in various areas of machine learning, this workshop turns the spotlight on the assumptions behind the successful application of causal inference techniques. It is well known that answering causal queries from observational data requires strong and sometimes untestable assumptions. On the theoretical side, a whole host of settings as been established in which causal effects are identifiable and consistently estimable under a set of by now considered "standard" assumptions. While these can be reasonable in specific scenarios, they were often at least partially motivated by rendering estimation theoretically feasible. Such assumptions tell us what we would need to assert about the data generating process in order to be able to answer causal queries. Unfortunately, in applications we often find them taken for granted as properties that can safely be assumed to hold without further scrutiny. This starts with fundamentally untestable assumptions such as the stable unit treatment value assumption or ignorability and continues to no interference, faithfulness, positivity or overlap, no unobserved confounding and even reaches blanket one-size-fits all assumptions on the linearity of structural equations or the additivity of noise. This situation may lead practitioners to either believe that well founded causal inference is unattainable altogether, or that established off-the-shelf methods can be trusted to deliver reliable causal estimates in virtually any situation. Similarly, as ideas from causality are increasingly picked up by researchers in deep-, reinforcement-, or meta-learning, there is a risk that the role of assumptions for causal inference gets lost in translation. One of the main goals of this workshop is to help the research community and practitioners understand the concrete challenges of trustworthy assumptions for effective causal inference.

As the use of machine learning (ML) becomes ubiquitous, there is a growing understanding and appreciation for the role that data plays for building successful ML solutions. Classical ML research has been primarily focused on learning algorithms and their guarantees. Recent progress has shown that data is playing an increasingly central role in creating ML solutions, such as the massive text data used for training powerful language models, (semi-)automatic engineering of weak supervision data that enables applications in few-labels settings, and various data augmentation and manipulation techniques that lead to performance boosts on many real world tasks. On the other hand, data is one of the main sources of security, privacy, and bias issues in deploying ML solutions in the real world. This workshop will focus on the new perspective of machine learning for data --- specifically how ML techniques can be used to facilitate and automate a range of data operations (e.g. ML-assisted labeling, synthesis, selection, augmentation), and the associated challenges of quality, security, privacy and fairness for which ML techniques can also enable solutions.

Join us for a virtual trivia night and an opportunity to learn more about the work we are doing at Amii! Teams will be assembled during the event and the winning team will walk away with a $25 gift card towards a celebratory meal from UberEATS.