Mingyi Hong
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Mingyi HongAssociate Professor Email: mhong at umn.edu |
Teaching
EE 3015 Signal and Systems, Spring 2019, 2022, UMN, ECE Department
EE 5239 Nonlinear Optimization, Fall 2017, 2018, 2019, 2020, 2021 UMN, ECE Department
RA and Postdoctoral position available
We have research assistants and post doctoral fellow position available. If you are interested, please contact Dr. Hong via email
Group News
Dec 2023, Group at NeurIPS 2023
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Dec. 2023 new preprint (with Xinwei, Steven and Woody) entitled Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach has been submitted for publication; see the preprint here; This work shows that, by using error feedback, the bias of clipping gradient can be completely removed, while being able to use arbitrary clipping threshold (independent of the problem). These properties makes it practical to use the algorithm in practice (as compared with DP-SGD).
Dec 2023, Dr. Jiaxiang Li from Math Department of UC Davis has joined our group as a Post-Doctoral Fellow. Welcome Jiaxiang!
Dec 2023, NSF sponsored workshop on sensing and analytics will be help Dec 7-8 in DC NSF headquarters; See [Workshop Website]
Nov 2023, Xinwei has successfully defended his PhD thesis; Xinwei has made some exciting achievements during his PhD career, and he will be joining USC for a postdoctoral fellowship; see his [publications]; Congrats Dr. Zhang!
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Nov. 2023, paper accepted (TAC): our work On the local linear rate of consensus on the stiefel manifold (joint work with Sixiang, Alfredo and Shahin) has been accepted by IEEE TAC; see the paper [here]
Oct. 2022, tutorial proposal accepted. Our tutorial proposal “Zeroth-Order Machine Learning: Fundamental Principles and Emerging Applications in Foundation Models” has been accepted by ICASSP 2024 and AAAI 2024.
Sept 2023, Group Kayak Activity, with visiting student Zhiwei Tang
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Sept. 2022, paper accepted: Four papers have been accepted by NeurIPS 2023.
Understanding Expertise through Demonstrations: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning (joint work with Siliang, Chenliang and Alfredo) has been accepted as an Oral paper; see the paper here [here]
Vcc: Scaling Transformers to 128K Tokens or More by Prioritizing Important Tokens (joint work with Zhanpeng and AWS researchers); see the paper here [here]
Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning (joint work with Yihua et al)
A Unified Framework for Inference-Stage Backdoor Defense (joint work with Sun, Ganghua, Xuan, Jie and Cisco researchers).
July. 2023, new grant: We got a grant to organize an NSF workshop on “the Convergence of Smart Sensing Systems, Applications, Analytic and Decision Making”; the workshop website will be online soon.
July. 2023, new grant: We obtained a new 3-year grant “A Multi-Rate Feedback Control Framework for Modeling, Analyzing, and Designing Distributed Optimization Algorithms” from NSF; In this work, we advocate the a generic “model” of distributed algorithms (based on techniques from stochastic multi-rate feedback control), which can abstract their important features (e.g., privacy preserving mechanism, compressed communication, occasional communication) into tractable modules.
June 2023, We have bee presented the SPS Best Paper Award and the Pierre-Simon Laplace Early Career Technical Achievement Award at ICASSP 2023. Congratulations to everyone, especially formal members from our group, Dr. Haoran Sun and Dr. Xiangyi Chen!
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May. 2023, new grant:, M. Is the co-PI for the UMN-lead AI-Climate Institute; this is a 5-year project funded by NSF, NIFA and USDA focusing on climate-smart agriculture and forestry.
April. 2023, paper accepted (TSP): our work Towards Understanding Asynchronous Advantage Actor-critic: Convergence and Linear Speedup (joint work with Tianyi, Hen and Kaiqing) has been accepted by IEEE TSP; see the paper [here]
April. 2023, paper accepted (SIOPT): our work Minimax problems with coupled linear constraints: computational complexity, duality and solution methods (joint work with Ioannis and Shuzhong) has been accepted by SIAM Journal on Optimization. In this work, we analyzed a class of seemingly easy min-max problems, where there is a linear constraint coupling the min and max optimization variables. We show that this class of problem is NP-hard, and then derived a duality theory for it. Leveraging the resulting duality-based relaxations, we propose a family of efficient algorithms, and test them on the network interdiction problems. see the paper [here]
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April 2023, papers accepted (ICML 2023):
Linearly Constrained Bilevel Optimization: A Smoothed Implicit Gradient Approach with Ioannis and Prashant, Sijia, Yihua and Kevin
FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks, with Bingqing, Xinwei, and Prashant
Understanding Backdoor Attacks through the Adaptability Hypothesis, with Xun, Jie, and Xuan and Cisco team
Jan. 2023 new preprint (with Siliang, Chenliang and Alfredo) entitled Understanding Expertise through Demonstrations: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning has been submitted for publication; see the preprint here; This work develops one of the first offline inverse reinforcement learning (IRL) formulation and algorithm for inferring an agent's reward function, while finding its policy.
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Dec. 2022, SPS Early-Career Award: M. Receives the Pierre-Simon Laplace Early Career Technical Achievement Award from IEEE Signal Processing Society.
Dec. 2022, SPS Best Paper Award: our work Learning to optimize: Training deep neural networks for interference management (joint work with Haoran, Xiangyi, Qingjiang, Nikos and Xiao), published in IEEE TSP 2018, has been awarded the 2022 Signal Processing Society Best Paper Award.
Dec. 2022, papers accepted (TSP & TWC): our work Parallel Assisted Learning (joint work with Xinran, Jiawei, Yuhong and Jie Ding) has been accepted by TSP; see the paper [here]; Also, our work Learning to beamform in heterogeneous massive MIMO networks (joint work with Minghe and Tsung-Hui) has been accepted by TWC; see the paper [here]
Nov. 2022, paper conditionally accepted (SIOPT): our work Primal-Dual First-Order Methods for Affinely Constrained Multi-Block Saddle Point Problems (joint work with Junyu, Mengdi and Shuzhong) has been conditionally accepted by SIAM Journal on Optimization (with minor revision)
Nov. 2022, paper conditionally accepted (SIOPT): our work Minimax problems with coupled linear constraints: computational complexity, duality and solution methods (joint work with Ioannis and Shuzhong) has been conditionally accepted by SIAM Journal on Optimization (with minor revision); see the paper [here]
Nov. 2022, paper accepted (SIOPT): our work Understanding a class of decentralized and federated optimization algorithms: A multi-rate feedback control perspective (joint work with Xinwei and Nicola) has been accepted by SIAM Journal on Optimization; see the paper [here]
Oct. 2022, tutorial proposal accepted: with Sijia, Yihua and Bingqing, we will be presenting a tutorial on bilevel optimization in machine learning for AAAI 2023.
Sept. 2022, research award: Our group (together with Jie, Zhi-Li and Prashant) has received a new Meta Research Award, to support our work on developing large-scale distributed algorithms and systems for autoscaling.
Aug. 2022, paper accepted: Five papers have been accepted by NeurIPS 2022.
Advancing Model Pruning via Bi-level Optimization with Yihua, Sijia, Yanzhi, Yugang, et al
Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees, with Siliang, Chenliang, and Alfredo
Distributed Optimization for Overparameterized Problems: Achieving Optimal Dimension Independent Communication Complexity, with Bingqing, Ioannis, Hoi-To, and Chung-Yiu
Inducing Equilibria via Incentives: Simultaneous Design-and-Play Ensures Global Convergence, with Boyi, Jiayang, Zhaoran, Zhuoran, Hoi-To, et al
A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization, with Songtao, Siliang, et al
Aug. 2022, paper accepted (TSP): our work FedBCD: A Communication-Efficient Collaborative Learning Framework for Distributed Features (joint work with researchers at WeBank) has been accepted by TSP; see the paper [here]
Aug. 2022, paper accepted (SIOPT): our work A two-timescale framework for bilevel optimization: Complexity analysis and application to actor-critic (joint work with Hoi-To, Zhaoran and Zhuoran) has been accepted by SIAM Journal on Optimization; see the paper [here]
Aug. 2022, paper award (UAI): our work Distributed Adversarial Training to Robustify Deep Neural Networks at Scale (joint work with IBM researchers), published in The Conference on Uncertainty in Artificial Intelligence (UAI) 2022, has been selected as oral presentation, and selected as the Best Paper Runner-Up Award for the conference; the paper can be found [here]
June 2022, paper accepted (SIOPT): our work On the divergence of decentralized non-convex optimization (joint work with Siliang, Junyu and Haoran) has been accepted by SIAM Journal on Optimization; see the paper [here]
May 2022, We have been virtually presented the SPS Best Paper Award at ICASSP 2022.
May 2022, Prashant will be starting his position at CS Department of Wayne State University; congrats Dr. Khanduri!
May 2022, Xiangyi has successfully defended his PhD thesis; Xiangyi has made some exciting achievements during his PhD career; see his [publications] congrats Dr. Chen!
April 2022, paper accepted: Three papers have been accepted by ICML 2022
A Stochastic Multi-Rate Control Framework For Modeling Distributed Optimization Algorithms with Xinwei, Sairaj and Nicola
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy, with Xiangyi, Xinwei, and Steven
Revisiting and advancing fast adversarial training through the lens of bi-level optimization, with Yihua, Sijia, Prashant and Siyu
April 2022, Xinwei has received the University of Minnesota's Doctoral Dissertation Fellowship; congrats Xinwei!
Feb. 2022, paper published (TSP): Our work (with Wenqiang, Shahana and Xiao) entitled Stochastic mirror descent for low-rank tensor decomposition under non-Euclidean losses has been published in TSP.
Dec. 2021, SPS Best Paper Award: our work Multi-agent distributed optimization via inexact consensus ADMM (joint work with Tsung-Hui and Xiangfeng), published in IEEE TSP 2016, has been awarded the 2021 Signal Processing Society Best Paper Award.