26 citations · 90 across the 10 of their papers we have counts for
11 papers
Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluation
Shih-Ying Yeh, Yu-Guan Hsieh, Zhidong Gao +3
Text-to-image generative models have garnered immense attention for their ability to produce high-fidelity images from text prompts. Among these, Stable Diffusion distinguishes its…
Thompson Sampling with Diffusion Generative Prior
Yu-Guan Hsieh, Shiva Prasad Kasiviswanathan, Branislav Kveton +1
In this work, we initiate the idea of using denoising diffusion models to learn priors for online decision making problems. Our special focus is on the meta-learning for bandit fra…
Uplifting Bandits
Yu-Guan Hsieh, Shiva Prasad Kasiviswanathan, Branislav Kveton
We introduce a multi-armed bandit model where the reward is a sum of multiple random variables, and each action only alters the distributions of some of them. After each action, th…
No-Regret Learning in Games with Noisy Feedback: Faster Rates and Adaptivity via Learning Rate Separation
Yu-Guan Hsieh, Kimon Antonakopoulos, Volkan Cevher +1
We examine the problem of regret minimization when the learner is involved in a continuous game with other optimizing agents: in this case, if all players follow a no-regret algori…
Push--Pull with Device Sampling
Yu-Guan Hsieh, Yassine Laguel, Franck Iutzeler +1
We consider decentralized optimization problems in which a number of agents collaborate to minimize the average of their local functions by exchanging over an underlying communicat…
Optimization in Open Networks via Dual Averaging
Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick +1
In networks of autonomous agents (e.g., fleets of vehicles, scattered sensors), the problem of minimizing the sum of the agents' local functions has received a lot of interest. We…
Adaptive Learning in Continuous Games: Optimal Regret Bounds and Convergence to Nash Equilibrium
Yu-Guan Hsieh, Kimon Antonakopoulos, Panayotis Mertikopoulos
In game-theoretic learning, several agents are simultaneously following their individual interests, so the environment is non-stationary from each player's perspective. In this con…
Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism
Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick +1
In this paper, we provide a general framework for studying multi-agent online learning problems in the presence of delays and asynchronicities. Specifically, we propose and analyze…
Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling
Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick +1
Owing to their stability and convergence speed, extragradient methods have become a staple for solving large-scale saddle-point problems in machine learning. The basic premise of t…
On the convergence of single-call stochastic extra-gradient methods
Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick +1
Variational inequalities have recently attracted considerable interest in machine learning as a flexible paradigm for models that go beyond ordinary loss function minimization (suc…
Classification from Positive, Unlabeled and Biased Negative Data
Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama
In binary classification, there are situations where negative (N) data are too diverse to be fully labeled and we often resort to positive-unlabeled (PU) learning in these scenario…