[10/2019] I will be presenting a paper titled “Are Transformers universal approximators of sequence-to-sequence functions?” at the NeurIPS 2019 Workshop on Machine Learning with Guarantees.
[09/2019] Two papers were accepted to NeurIPS 2019: “Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity” (Spotlight) and “Are deep ResNets provably better than linear predictors?” (Poster).
[07/2019] Spending a wonderful summer as a research intern at Google Research New York!
[02/2019] Two talks given in Feb: one at The 24th LIDS Student conference and the other at an invited seminar held by KAIST ISysE.
[12/2018] Two theoretical papers were accepted at The Seventh International Conference on Learning Representations (ICLR 2019)!
[09/2018] I’ll be giving a presentation based on “Small nonlinearities in activation functions create bad local minima in neural networks” at The 2018 INFORMS Annual Meeting.
[05/2018] A new paper “Minimax Bounds on Stochastic Batched Convex Optimization” was accepted for presentation at The 31st Annual Conference on Learning Theory (COLT 2018). The project during my master’s is finally to be published!
[01/2018] Our work “Global optimality conditions for deep neural networks” was accepted as a poster presentation at The Sixth International Conference on Learning Representations (ICLR 2018). Very excited!
[01/2018] I will be giving a presentation based on our work “Global optimality conditions for deep neural networks” at The 23rd Annual LIDS Student Conference on Feb 1, 2018.
[12/2017] I will be presenting a short paper “Global optimality conditions for deep neural networks” at Deep Learning: Bridging Theory and Practice Workshop at NIPS 2017.