5 papers
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…
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…