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Events for April 22, 2024
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PhD Thesis Proposal - Qinyuan Ye
Mon, Apr 22, 2024 @ 10:00 AM - 11:30 AM
Thomas Lord Department of Computer Science
University Calendar
Title: Cross-Task Generalization Abilities of Large Language Models
Committee Members: Xiang Ren (Chair), Robin Jia, Swabha Swayamdipta, Jesse Thomason, Morteza Dehghani
Date & Time: Monday, April 22, 10am-11:30am\
Location: SAL 213
Abstract: Humans can learn a new language task efficiently with only a few examples, by leveraging their knowledge and experience obtained when learning prior tasks. Enabling similar cross-task generalization abilities in NLP systems is fundamental for achieving the goal of general intelligence and enabling broader and more scalable adoption of language technology in future applications. In this thesis proposal, I will present my work on (1) benchmarking cross-task generalization abilities with diverse NLP tasks; (2) developing new model architecture for improving cross-task generalization abilities; (3) analyzing and predicting the generalization landscape of current state-of-the-art large language models. Additionally, I will outline future research directions, along with preliminary thoughts on addressing them.
Zoom Link: https://usc.zoom.us/j/93269270403?pwd=NVNmN085bm5SWXNnNGErcXczeVkxdz09Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Qinyuan Ye
Event Link: https://usc.zoom.us/j/93269270403?pwd=NVNmN085bm5SWXNnNGErcXczeVkxdz09
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PhD Defense- Tiancheng Jin
Mon, Apr 22, 2024 @ 04:00 PM - 05:30 PM
Thomas Lord Department of Computer Science
Student Activity
PhD Defense- Tiancheng Jin
Title: Robust and Adaptive Online Reinforcement Learning
Committee: Haipeng Luo (Chair), Rahul Jain, Vatsal Sharron
Abstract: Reinforcement learning (RL) is a machine learning (ML) technique on learning to make optimal sequential decisions via interactions with an environment. In recent years, RL achieved great success in many artificial intelligence tasks, and has been widely regarded as one of the keys towards Artificial General Intelligence (AGI). However, most RL models are trained on simulators, and suffer from the reality gap: a mismatch between simulated and real-world performance. Moreover, recent work has shown that RL models are especially vulnerable to adversarial attacks. This motivates the research on improving the robustness of RL, that is, the ability of ensuring worst-case guarantees.
On the other hand, it is not favorable to be too conservative/pessimistic and sacrifice too much performance while the environment is not difficult to deal with.In other words, adaptivity --- the capability of automatically adapting to the maliciousness of the environment, is especially desirable to RL algorithms: they should not only target worst-case guarantee, but also pursue instance optimality and achieve better performance against benign environments.
In this thesis, we focus on designing practical, robust and adaptive reinforcement algorithms.
Specifically, we take inspiration from the online learning literature, and consider interacting with a sequence of Markov Decision Processes (MDPs), which captures the nature of changing environment. We hope that the techniques and insight developed in this thesis could shed light on improving existing deep RL algorithms for future applications.Location: Kaprielian Hall (KAP) - 141
Audiences: Everyone Is Invited
Contact: Tiancheng Jin
