July 13 - July 15: New Models in Online Decision Making for Real-World Applications

Organizers:

• Yonathan Efroni (Microsoft Research, NYC)
• Akshay Krishnamurthy (Microsoft Research, NYC)
• Aldo Pacchiano (Microsoft Research, NYC)
• Aadirupa Saha (Microsoft Research, NYC)

Real-world environments pose many challenges for online decision-making algorithms. In such settings, the feedback gathered through interaction with the environment is often high-dimensional, partially observed, implicit, and often corrupted. Nevertheless, in standard Bandits and Reinforcement Learning (RL) such challenges are often overlooked. This workshop aims to present a broad overview of the contemporary decision-making models that are being actively researched, and provide a networking forum for researchers and practitioners. Our current theme, New Models in Online Decision Making for Real-World Applications, pervades machine learning at large, and with our aim of bridging practice and theory this workshop will find general appeal. The workshop forum will allow us to foster dissemination, cross-fertilization and discussion at scale.

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August 4 - August 5: Representation Learning Theory

Organizers:

• Yasaman Bahri (Google Research)
• Quanquan Gu (UCLA)
• Yuanzhi Li (CMU)
• Aarti Singh (CMU)

One of the major advantages of modern deep networks is their ability to automatically extract powerful representations of the input data - many of the representations are even considered to be much better than humanly constructed ones. However, from a theoretical point of view, what representations can be efficiently learned by those models? This workshop will serve as a platform to foster discussion and collaboration for understanding the theoretical foundations of representation learning in modern machine learning.

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