me

Yu-Guan Hsieh

Postdoctoral Researcher

Apple MLR

About me

Welcome to my website! As a postdoctoral researcher at Apple MLR, Paris, I am deeply interested in how we can improve AI systems from various perspectives. In particular, my current research focuses on vision-language pre-training and generative modeling, where I take a data-centric approach and explore different ways to enhance data quality.
In the mean time, I have a theoretical background, as evidenced by my Ph.D. in optimization, online learning, and learning in games completed under the supervision of Jérôme Malick, Franck Iutzeler, and Panayotis Mertikopoulos at Université Grenoble Alpes. Finally, I also contributed to the LyCORIS project that implements a number of algorithms for efficient fine-tuning. This represents an important topic in making AI more personalized and accessible.

Contact: cyberhsieh212 AT gmail DOT com

News

  • (July 2024) We have released the GBC datasets which recaption 10M images from CC12M in a new annotation format! Check the paper.

  • (June 2024) I received the Academic Thesis Prize from Université Grenble Alpes!

  • (May 2024) The BLOOP paper for improved gradient surgery is accepted at ICML.

  • (Jan 2024) The LyCORIS paper for Stable Diffusion fine-tuning is accepted at ICLR.

Publications & Preprints

Graph-Based Captioning: Enhancing Visual Descriptions by Interconnecting Region Captions
Yu-Guan Hsieh, Cheng-Yu Hsieh, Shih-Ying Yeh, Louis Béthune, Hadi Pour Ansari, Pavan Kumar Anasosalu Vasu, Chun-Liang Li, Ranjay Krishna, Oncel Tuzel, and Marco Cuturi
arxiv 2407.06723, 2024
Paper Arxiv
Careful with that Scalpel: Improving Gradient Surgery with an EMA
Yu-Guan Hsieh, James Thornton, Eugene Ndiaye, Michal Klein, Marco Cuturi, and Pierre Ablin
International Conference on Machine Learning (ICML), 2024
Paper Arxiv Poster
Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluation
Shin-Ying Yeh, Yu-Guan Hsieh, Zhidong Gao, Bernard B W Yang, Giyeong Oh, and Yanmin Gong
International Conference on Learning Representations (ICLR), 2024
Paper Arxiv Poster Code
Decision-Making in Multi-Agent Systems: Delays, Adaptivity, and Learning in Games
Yu-Guan Hsieh
Ph.D. Dissertation, 2023
Manuscript Slides
Thompson Sampling with Diffusion Generative Prior
Yu-Guan Hsieh, Shiva Kasiviswanathan, Branislav Kveton, and Patrick Blöbaum
International Conference on Machine Learning (ICML), 2023
Paper Arxiv Poster Slides
Push--Pull with Device Sampling
Yu-Guan Hsieh, Yassine Laguel, Franck Iutzeler, and Jérôme Malick
IEEE Transactions on Automatic Control (TACON), 2023
Paper Arxiv
No-Regret Learning in Games with Noisy Feedback: Faster Rates and Adaptivity via Learning Rate Separation
Yu-Guan Hsieh, Kimon Antonakopoulos, Volkan Cevher, and Panayotis Mertikopoulos
Neural Information Processing Systems (NeurIPS), 2022
Paper Arxiv Poster Slides
Uplifting Bandits
Yu-Guan Hsieh, Shiva Prasad Kasiviswanathan, and Branislav Kveton
Neural Information Processing Systems (NeurIPS), 2022
Paper Arxiv Poster Short slides Long slides
Multi-agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism
Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, and Panayotis Mertikopoulos
Journal of Machine Learning Research (JMLR), 2022
Paper Arxiv Poster
Optimization in Open Networks via Dual Averaging
Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, and Panayotis Mertikopoulos
IEEE Conference on Decision and Control (CDC), 2021
Paper Arxiv Slides
Adaptive Learning in Continuous Games: Optimal Regret Bounds and Convergence to Nash Equilibrium
Yu-Guan Hsieh, Kimon Antonakopoulos, and Panayotis Mertikopoulos
Conference on Learning Theory (COLT), 2021
Paper Arxiv Poster Slides Video
Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling
Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, and Panayotis Mertikopoulos
Neural Information Processing Systems (NeurIPS), 2020
Paper Arxiv Poster Slides Video Code
On the Convergence of Single-Call Stochastic Extra-Gradient Methods
Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, and Panayotis Mertikopoulos
Neural Information Processing Systems (NeurIPS), 2019
Paper Arxiv Poster Slides
Classification from Positive, Unlabeled and Biased Negative Data
Yu-Guan Hsieh, Gang Niu, and Masashi Sugiyama
International Conference on Machine Learning (ICML), 2019
Paper Arxiv Poster Slides Code

Experiences

  • 2024 January 8th

    Postdoc in Apple Paris

    I am returning to Paris after four years of my Ph.D. pursuit, and this comes with a refocusing of my research interests. Having witnessed how large foundation models are transforming the world that we live in, I have made up my mind to work on problems that are more directly relevant to these models. Meanwhile, I also want to dedicate myself to the development of open-source models and datasets so that more people can benefit from this exciting progress of science and technology.

  • 2023 November 7th

    Ph.D. Defense

    After four years of hard work, the Ph.D. defense marked an import milestone of my academic journey. My Ph.D. Thesis deals with mathematical frameworks and algorithms for decision-making in multi-agent systems, making use of tools from online learning, game theory, and stochastic optimization. The four years also came with many great experiences, including workshops in Luminy, Les Houches, Singapore, and a lot more. I am deeply grateful for my Ph.D. advisors for their support and guidance along these years.

  • 2022 August ~ November

    Internship at Amazon in Santa Clara, USA

    I was fortunate to get a return internship at Amazon in the same team but at a different location. Impressed by the performance and flexibility of score-based diffusion models, I decided to investigate how it can be incorporated as prior in multi-armed bandit problems. This internship was thus an occasion for me to conduct a quite different type of research, in which algorithms and experiments prevail theory. I also enjoyed the four months in the Bay Area, where I got the opportunity to meet many old friends and gained new insignts into my future career.

  • 2021 Oct. ~ 2022 Jan.

    Internship at Amazon in Tübingen, Germany

    To acquire industrial experience, I did a four-month applied science internship at Amazon in the third year of my Ph.D. During this internship, I had the great honor to work with my internship manager Shiva Kasiviswanathan and Principal Scientist Branislav Kveton on a stochastic bandit model (see this paper). Additionally, being part of the Causality team, led by Dominik Janzing and Yasser Jadidi, allowed me to gain an understanding of the basics of causality. Finally, the internship provided me with valuable experience in remote collaboration, as well as an enjoyable experience living in the beautiful medieval city of Tübingen, surrounded by nature.

  • 2019

    • Master Mathematics, Vision, Learning
    • Move to Grenoble and start Ph.D.

    For my second year of master, I studied Mathematics, Vision, and Learning in ENS Paris-Saclay. I then came to Grenoble for the internship, before starting my Ph.D. with my current advisors in this “Capital of the Alps”.

  • 2018 March ~ August

    Internship at Riken AIP, Tokyo, Japan

    As a fan of Japanese Culture, I decided to go to Japan for my first-year master’s internship. Thanks to my kind internship advisors (Masashi Sugiyama and Gang Niu) and co-workers, this internship marked the beginning of my research career. My work centered around weakly-supervised learning and my first paper was written. I also enjoyed very much the time off from work. I benefited from these five months so much that I dare say that it was probably my greatest turning point after my arrival in France.

  • ENS Paris

    2016 September

    Enter ENS Paris

    After two years of hard work. I was admitted to ENS Paris, being ranked 1bis of the computer science group in the entrance exam (bis as I am not French). Only during the first and half years we actually followed courses on the campus of ENS. Besides math and computer science, I also attended several cognitive science courses (which granted me a minor in cognitive science).

  • Moving to France

    2014 July

    Move to Lyon, France

    The randomness of life brought me to France after I graduated from high school. I got this opportunity thanks to the CPGE Taiwan program which recruit Taiwanese students to study in French classe prepa through a maths exam. After 6 months of intensive French course in Taiwan, in July 2014, I embarked on the journey and started my study abroad life in France.

  • IMO

    2013

    IMO silver medal in Colombia

    My passion for mathematics was already developed in my childhood. From 12 to 18 years old, I actively participated in math competitions and spent a lot of time on Olympiad type questions. Especially, during my second year of high school, I was so fortunate to become one of the six contestants representing Taiwan in IMO 2013 after passing multiple stages of selection.

  • 1996

    Born in Taipei, Taiwan

    I was born in Taiwan, this beautiful island on which both western democracy and eastern lifestyle can be found. Yes, that is surprisingly rare. Moreover, Taiwan is also considered as one of the best places for expats living abroad and is the first in Asia that legalizes same-sex marriage. I am proud of being Taiwanese.

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    My CV