6 papers
You Only Edit Once: Incentivizing In-Context Capability of LLMs via Local Demonstration Refinement
Jiarong Wen, Qi Wang, Yun Qu +8
In-context learning (ICL) is crucial for boosting the inference performance of large language models (LLMs). However, the effectiveness of ICL in LLMs is greatly influenced by the…
VAO: Validation-Aligned Optimization for Cross-Task Generative Auto-Bidding
Yiqin Lv, Zhiyu Mou, Miao Xu +9
Generative auto-bidding has demonstrated strong performance in online advertising, yet it often suffers from data scarcity in small-scale settings with limited advertiser participa…
Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search
Zhiyu Mou, Yiqin Lv, Miao Xu +9
Auto-bidding is a critical tool for advertisers to improve advertising performance. Recent progress has demonstrated that AI-Generated Bidding (AIGB), which learns a conditional ge…
Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environments
Yun Qu, Qi Cheems Wang, Yixiu Mao +2
Task robust adaptation is a long-standing pursuit in sequential decision-making. Some risk-averse strategies, e.g., the conditional value-at-risk principle, are incorporated in dom…
Model Predictive Task Sampling for Efficient and Robust Adaptation
Qi Wang, Zehao Xiao, Yixiu Mao +4
Foundation models have revolutionized general-purpose problem-solving, offering rapid task adaptation through pretraining, meta-training, and finetuning. Recent crucial advances in…
Robust Fast Adaptation from Adversarially Explicit Task Distribution Generation
Cheems Wang, Yiqin Lv, Yixiu Mao +3
Meta-learning is a practical learning paradigm to transfer skills across tasks from a few examples. Nevertheless, the existence of task distribution shifts tends to weaken meta-lea…