joint-optimization

This paper proposes a particle-swarm-assisted gradient meta-learning algorithm for joint optimization of transmit precoding and STAR-RIS coefficients in multi-user wireless systems, achieving improved weighted sum rates over conventional methods.

The paper introduces Joint Pixel-Prompt Optimization (JPPO), a novel adversarial framework that jointly optimizes pixel perturbations and visible prompts to exhaust resources in autoregressive vision-language models, achieving significant latency and energy amplification compared to existing methods.

The paper proposes WHALE, an alternating optimization method for jointly training model weights and harness code in AI agents, achieving significant performance improvements across search QA, math reasoning, and chess puzzles.

Proposes a two-timescale multi-layer deep reinforcement learning framework with latent action space for joint service placement, computational delegation, and power control in hierarchical edge-cloud computing, achieving up to 20.8% latency reduction and 13% resource utilization improvement.

Proposes a joint optimization framework for multi-slot guaranteed display advertising, addressing slot-level redundancy and contract imbalance via bipartite matching and contract roulette. Online A/B tests on Meituan show significant improvements in revenue and contract fulfillment.