题目一：Algorithmic Robustness in Classification
内容简介：Learning linear classifiers (i.e. halfspaces) is one of the fundamental problems in machine learning dating back to 1950s. In the presence of benign label noise such as random classification noise, the problem is well understood. However, when the data are corrupted by more realistic noise, even establishing polynomial-time learnability can be nontrivial. In this talk, I will introduce our recent work on learning with Massart noise and with malicious noise that significantly advances the state of the art. In particular, for the Massart noise where each label is flipped with an unknown probability across the domain, we present the first polynomial-time algorithm that is robust to any noise rate < 1/2. For the malicious noise where an adversary may inspect the learning algorithm and inject malicious data, we present the first sample-optimal learning algorithm that achieves information-theoretic noise tolerance. In both works, the developed algorithms are active in nature, and are nearly label-optimal. Finally, I will discuss some important directions such as list-decodable classification, where the majority of the data are contaminated.
报告人简介：Dr. Jie Shen is an Assistant Professor in the Computer Science Department at Stevens Institute of Technology, and is also a faculty member of the Stevens AI Institute. The goal of his research is to understand fundamental limits of learning under real-world constraints such as limited availability of labeled data and the presence of high level noise, and to design efficient algorithms with provable guarantees. His recent works investigate interactive learning from untrusted data, where learning algorithms are involved in data acquisition for optimal data efficiency and robustness. Over the past few years, he has published around 15 papers in top machine learning conferences such as ICML, NeurIPS, and ALT, and has served as senior program committee member for IJCAI, program committee member/journal reviewer for ICML, NeurIPS, COLT, ICLR, AISTATS, AAAI, JMLR, ML, TIT, TPAMI, TSP, PR etc. He obtained his BS degree in Mathematics at Shanghai Jiao Tong University, and completed his Ph.D. in Computer Science at Rutgers University in 2018. He was a visiting scholar at National University of Singapore and Duke University. He received the NSF CRII award in 2020.
题目二：Deep Learning and Wireless Communications: Encounter of Two Disciplines
内容简介：Recently, deep learning (DL), a branch of artificial intelligence (AI) techniques, has shown great promise in bewildering variety of applications such as speech recognition, object detection, and language translation. This great success of DL has stimulated the use of DL techniques to wireless systems. In this talk, we discuss the current status of 5G and why DL paradigm is essential to solve future wireless problems in 6G era.
报告人简介：Byonghyo Shim received the B.S. and M.S. degrees in EE Department from Seoul National University, Korea, in 1995 and 1997, respectively and the M.S. degree in Mathematics and the Ph.D. degree in electrical and computer engineering from the University of Illinois at Urbana-Champaign (UIUC), in 2004 and 2005, respectively. His industry experiences include Texas Instruments, Qualcomm, and Samsung Electronics. Since 2014, he has been with Seoul National University (SNU), where he is currently a Professor and the Vice Chair of the Department of Electrical and Computer Engineering. He was the recipient of the M. E. Van Valkenburg Research Award from University of Illinois, in 2005, the Hadong Young Engineer Award from IEIE in 2010, the Irwin Jacobs Award from Qualcomm in 2016, the Shinyang Research Award from SNU in 2017, the Okawa Foundation Research Award in 2020, and IEEE COMSOC AP Outstanding Paper in 2021. He has served as an Associate Editor for the IEEE Transactions on Signal Processing, IEEE Transactions on Communications, IEEE Transactions on Vehicular Technology, IEEE Wireless Communications Letters, and the Guest Editor of the IEEE Journal on Selected Areas in Communication.
时 间：2021年11月30日（周二） 上午9：30始
地 点：Zoom会议ID：945 9527 2453