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Knowledge and Logical Reasoning in the Era of Data-driven Learning
Nezihe Merve Gürel · Bo Li · Theodoros Rekatsinas · Beliz Gunel · Alberto Sngiovanni Vincentelli · Paroma Varma

Fri Jul 28 12:00 PM -- 08:00 PM (PDT) @ Meeting Room 301
Event URL: https://klr-icml2023.github.io/ »

Thinking fast and automatic vs. slow and deliberate (respectively System I and II) is a popular analogy when comparing data-driven learning to the good old-fashion symbolic reasoning approaches. Underlying this analogy lies the different capabilities of both systems, or lack thereof. While data-driven learning (System I) has striking performance advantages over symbolic reasoning (System II), it lacks abilities such as abstraction, comprehensibility and contextual awareness. Symbolic reasoning, on the other hand, tackles those issues but tends to lag behind data-driven learning when it comes to speedy, efficient and automated decision-making. In the current state of matters to combat issues on both sides, there is an increasing consensus among the machine learning and artificial intelligence communities to draw out the best of both worlds and unify data-driven approaches with rule-based, symbolic, logical and commonsense reasoning. This workshop aims to discuss emerging advances and challenges on this topic, in particular at the intersection of data-driven paradigms and knowledge and logical reasoning. We focus on both directions of this intersection:
Knowledge and Logical Reasoning for Data-driven Learning: In this direction, we will investigate the role of rule-based, knowledge and logical reasoning to enable more deliberate and trustworthy data-driven learning.
Data-driven Learning for Knowledge and Logical Reasoning: In this reverse direction, we will explore the capabilities of data-driven approaches to derive knowledge, logical and commonsense reasoning from data.

Author Information

Nezihe Merve Gürel (TU Delft)
Bo Li (UIUC)
Bo Li

Dr. Bo Li is an assistant professor in the Department of Computer Science at the University of Illinois at Urbana–Champaign. She is the recipient of the IJCAI Computers and Thought Award, Alfred P. Sloan Research Fellowship, AI’s 10 to Watch, NSF CAREER Award, MIT Technology Review TR-35 Award, Dean's Award for Excellence in Research, C.W. Gear Outstanding Junior Faculty Award, Intel Rising Star award, Symantec Research Labs Fellowship, Rising Star Award, Research Awards from Tech companies such as Amazon, Facebook, Intel, IBM, and eBay, and best paper awards at several top machine learning and security conferences. Her research focuses on both theoretical and practical aspects of trustworthy machine learning, which is at the intersection of machine learning, security, privacy, and game theory. She has designed several scalable frameworks for trustworthy machine learning and privacy-preserving data publishing. Her work has been featured by major publications and media outlets such as Nature, Wired, Fortune, and New York Times.

Theodoros Rekatsinas (Apple)
Beliz Gunel (Stanford University)
Alberto Sngiovanni Vincentelli (University of California, Berkeley)
Paroma Varma (Stanford University)

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