Microsoft @ NIPS 2017

NIPS 2017 Workshops

From “What if?” To “What Next?”: Causal Inference and Machine Learning for Intelligent Decision Making

Friday, December 8 @ 9:00 AM–6:30 PM | Hall C | Adith Swaminathan, Microsoft Research

This workshop is aimed at facilitating more interactions between researchers in machine learning and causal inference. In particular, it is an opportunity to bring together highly technical individuals who are strongly motivated by the practical importance and real-world impact of their work. Cultivating such interactions will lead to the development of theory, methodology, and – most importantly – practical tools, that better target causal questions across different domains.

In particular, we will highlight theory, algorithms and applications on automatic decision making systems, such as recommendation engines, medical decision systems and self-driving cars, as both producers and users of data. The challenge here is the feedback between learning from data and then taking actions that may affect what data will be made available for future learning. Learning algorithms have to reason about how changes to the system will affect future data, giving rise to challenging counterfactual and causal reasoning issues that the learning algorithm has to account for. Modern and scalable policy learning algorithms also require operating with non-experimental data, such as logged user interaction data where users click ads suggested by recommender systems trained on historical user clicks.

To further bring the community together around the use of such interaction data, this workshop will host a Kaggle challenge problem based on the first real-world dataset of logged contextual bandit feedback with non-uniform action-selection propensities. The dataset consists of several gigabytes of data from an ad placement system, which we have processed into multiple well-defined learning problems of increasing complexity, feedback signal, and context. Participants in the challenge problem will be able to discuss their results at the workshop.

Machine Learning and Computer Security

Friday, December 8 @ 9:00 AM–5:00 PM | Hyatt Hotel, Shoreline | Donald Brinkman, Microsoft Research

While traditional computer security relies on well-defined attack models and proofs of security, a science of security for machine learning systems has proven more elusive. This is due to a number of obstacles, including (1) the highly varied angles of attack against ML systems, (2) the lack of a clearly defined attack surface (because the source of the data analyzed by ML systems is not easily traced), and (3) the lack of clear formal definitions of security that are appropriate for ML systems. At the same time, security of ML systems is of great import due the recent trend of using ML systems as a line of defense against malicious behavior (e.g., network intrusion, malware, and ransomware), as well as the prevalence of ML systems as parts of sensitive and valuable software systems (e.g., sentiment analyzers for predicting stock prices). This workshop will bring together experts from the computer security and machine learning communities in an attempt to highlight recent work in this area, as well as to clarify the foundations of secure ML and chart out important directions for future work and cross-community collaborations.

Conversational AI – today’s practice and tomorrow’s potential

Friday, December 8 @ 8:00 AM–7:00 PM | Grand Ballroom B | Jason Williams, Microsoft Research

This workshop will include invited talks from academia and industry, contributed work, and open discussion. In these talks, senior technical leaders from many of the most popular conversational services will give insights into real usage and challenges at scale. An open call for papers will be issued, and we will prioritize forward-looking papers that propose interesting and impactful contributions. We will end the day with an open discussion, including a panel consisting of academic and industrial researchers.

Interpreting, Explaining and Visualizing Deep Learning…now what?

Saturday, December 9 @ 8:15 AM–6:30 PM | Hyatt Regency Ballroom | Hamid Palangi, Qiuyuan Huang, Paul Smolensky, and Xiaodong He, Microsoft Research

Our NIPS 2017 Workshop “Interpreting, Explaining and Visualizing Deep Learning – Now what?” aims to review recent techniques and establish new theoretical foundations for interpreting and understanding deep learning models. However, it will not stop at the methodological level, but also address the “now what?” question. This strong focus on the applications of interpretable methods in deep learning distinguishes this workshop from previous events as we aim to take the next step by exploring and extending the practical usefulness of Interpreting, Explaining and Visualizing in Deep Learning. Also with this workshop we aim to identify new fields of applications for interpretable deep learning. Since the workshop will host invited speakers from various application domains (computer vision, NLP, neuroscience, medicine), it will provide an opportunity for participants to learn from each other and initiate new interdisciplinary collaborations. The workshop will contain invited research talks, short methods and applications talks, a poster and demonstration session and a panel discussion. A selection of accepted papers together with the invited contributions will be published in an edited book by Springer LNCS in order to provide a representative overview of recent activities in this emerging research field.

Co-located workshops

Women in Machine Learning

Monday, December 4 & Thursday, December 7 @ 2:00 PM–2:30 PM | Room 104 | 12th Women in Machine Learning Workshop (WiML 2017), by Hanna Wallach, Microsoft Research

The annual Women in Machine Learning Workshop is the flagship event of Women in Machine Learning. This technical workshop gives female faculty, research scientists, and graduate students in the machine learning community an opportunity to meet, network and exchange ideas, participate in career-focused panel discussions with senior women in industry and academia and learn from each other. Underrepresented minorities and undergraduates interested in machine learning research are encouraged to attend. We welcome all genders; however, any formal presentations, i.e. talks and posters, are given by women. We strive to create an atmosphere in which participants feel comfortable to engage in technical and career-related conversations.

Black in AI

Friday, December 8 @ 1:30 PM–5:30 PM | Black in AI Workshop @ NIPS 2017, by Timnit Gebru, Microsoft Research

The first Black in AI event will be co-located with NIPS 2017. The goal is to gather people in the field to share ideas and discuss initiatives to increase the presence of Black people in the field of artificial intelligence, for both diversity and data bias prevention purposes. At this workshop, Black researchers in AI will also have the opportunity to present their work during our oral and poster sessions.