Yujia Jin I am a fourth-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. I am fortunate to be advised by Aaron Sidford. I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. I enjoy understanding the theoretical ground of many algorithms that are of practical importance. Prior to coming to Stanford, I received my Bachelor's degree in Applied Math at Fudan University, where I was fortunate to work with Prof. Zhongzhi Zhang. From 2016 to 2018, I also worked in Research Institute for Interdisciplinary Sciences (RIIS) at SHUFE, where I was fortunate to be advised by Prof. Dongdong Ge.
 Conference Publications
 Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space   [pdf] with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian to appear in ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022 "Streaming matching (and optimal transport) in $$\tilde{O}(1/\epsilon)$$ passes and $$O(n)$$ space."
 Stochastic Bias-Reduced Gradient Methods   [pdf] with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford to appear in Neural Information Processing Systems (NeurIPS), 2021 "A low-bias low-cost estimator of subproblem solution suffices for acceleration! "
 Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss   [pdf] [talk] [poster] with Yair Carmon, Arun Jambulapati and Aaron Sidford Conference of Learning Theory (COLT), 2021 "Improved upper and lower bounds on first-order queries for solving $$\min_{x}\max_{i\in[n]}\ell_i(x)$$."
 Towards Tight Bounds on the Sample Complexity of Average-reward MDPs   [pdf] [talk] [poster] with Aaron Sidford International Conference on Machine Learning (ICML), 2021 "Sample complexity for average-reward MDPs? A nearly matching upper and lower bound for constant error here!"
 Acceleration with a Ball Optimization Oracle   [pdf] [talk] [poster] with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian Neural Information Processing Systems (NeurIPS, Oral), 2020 "How many $$\epsilon$$-length segments do you need to look at for finding an $$\epsilon$$-optimal minimizer of convex function on a line?"
 Coordinate Methods for Matrix Games   [pdf] [talk] with Yair Carmon, Kevin Tian and Aaron Sidford Symposium on Foundations of Computer Science (FOCS), 2020 "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data."
 Efficiently Solving MDPs with Stochastic Mirror Descent   [pdf] [talk] with Aaron Sidford International Conference on Machine Learning (ICML), 2020 "Team-convex-optimization for solving discounted and average-reward MDPs!"
 Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG   [pdf] [slides] with Aaron Sidford Neural Information Processing Systems (NeurIPS, Spotlight), 2019 "A special case where variance reduction can be used to nonconvex optimization (monotone operators)."
 Variance Reduction for Matrix Games   [pdf] [poster] with Yair Carmon, Aaron Sidford and Kevin Tian Neural Information Processing Systems (NeurIPS, Oral), 2019 "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games."