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Found 12 papers, 2 papers with code
Careful with that Scalpel: Improving Gradient Surgery with an EMA
Beyond minimizing a single training loss, many deep learning estimation pipelines rely on an auxiliary objective to quantify and encourage desirable properties of the model (e. g. performance on another dataset, robustness, agreement with a prior).
Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluation
Text-to-image generative models have garnered immense attention for their ability to produce high-fidelity images from text prompts.
Thompson Sampling with Diffusion Generative Prior
In this work, we initiate the idea of using denoising diffusion models to learn priors for online decision making problems.
No-Regret Learning in Games with Noisy Feedback: Faster Rates and Adaptivity via Learning Rate Separation
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 algorithm, it is possible to achieve significantly lower regret relative to fully adversarial environments.
Push--Pull with Device Sampling
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 communication graph.
Uplifting Bandits
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.
Optimization in Open Networks via Dual Averaging
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.
Adaptive Learning in Continuous Games: Optimal Regret Bounds and Convergence to Nash Equilibrium
In game-theoretic learning, several agents are simultaneously following their individual interests, so the environment is non-stationary from each player's perspective.
Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism
In this paper, we provide a general framework for studying multi-agent online learning problems in the presence of delays and asynchronicities.
Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling
Owing to their stability and convergence speed, extragradient methods have become a staple for solving large-scale saddle-point problems in machine learning.
On the convergence of single-call stochastic extra-gradient methods
Variational inequalities have recently attracted considerable interest in machine learning as a flexible paradigm for models that go beyond ordinary loss function minimization (such as generative adversarial networks and related deep learning systems).
Classification from Positive, Unlabeled and Biased Negative Data
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 scenarios.
