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This paper proposes algorithms for sequential prediction under a lying oracle in stochastic and adversarial settings, proving logarithmic regret bounds for the cost function capturing prediction complexity.
This paper proposes learning dynamic classifier-free guidance schedules for diffusion models using adversarial learning to improve text-to-image generation quality by adapting guidance scales to different states.
This paper proposes a linguistic-invariant spoofing detection framework that uses teacher-student adversarial learning and a variational information bottleneck to mitigate linguistic bias, achieving up to a 36.2% relative reduction in equal error rate across nine datasets.
Introduces A3M, a framework combining adaptive deep reinforcement learning, adversarial reasoning, and multi-objective reward design for strategic bidding in repeated auctions, achieving 30-40% regret reduction.
Proposes a Wasserstein-GAN approach for unsupervised calibration of sensor-induced distribution drifts, validated on tracking detector toy models and simulated calorimeter data with aging effects.
This paper introduces Z-Image Turbo++, a two-step image generation model distilled from an eight-step teacher using distribution-aligned adversarial learning, step-decoupled parameterization, and end-to-end training with iterative regularization to narrow the quality gap with multi-step generation.
This paper proposed Distribution-Aligned Adversarial Distillation (DisAAD), a method that uses a lightweight proxy model to estimate uncertainty in black-box LLMs with only 1% of the original model size, achieving reliable quantification without requiring internal parameters or multiple sampling.