Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising

Real-time advertising allows advertisers to bid for each impression for a visiting user. To optimize specific goals such as maximizing revenue and return on investment (ROI) led by ad placements, advertisers not only need to estimate the relevance between the ads and user's interests, but most importantly require a strategic response with respect to other advertisers bidding in the market. In this paper, we formulate bidding optimization with multi-agent reinforcement learning. To deal with a large number of advertisers, we propose a clustering method and assign each cluster with a strategic bidding agent. A practical Distributed Coordinated Multi-Agent Bidding (DCMAB) has been proposed and implemented to balance the tradeoff between the competition and cooperation among advertisers. The empirical study on our industry-scaled real-world data has demonstrated the effectiveness of our methods. Our results show cluster-based bidding would largely outperform single-agent and bandit approaches, and the coordinated bidding achieves better overall objectives than purely self-interested bidding agents

ASP: Automatic Selection of Proxy dataset for efficient AutoML

Deep neural networks have gained great success due to the increasing amounts of data, and diverse effective neural network designs. However, it also brings a heavy computing burden as the amount of training data is proportional to the training time. In addition, a well-behaved model requires repeated trials of different structure designs and hyper-parameters, which may take a large amount of time even with state-of-the-art (SOTA) hyper-parameter optimization (HPO) algorithms and neural architecture search (NAS) algorithms. In this paper, we propose an Automatic Selection of Proxy dataset framework (ASP) aimed to dynamically find the informative proxy subsets of training data at each epoch, reducing the training data size as well as saving the AutoML processing time. We verify the effectiveness and generalization of ASP on CIFAR10, CIFAR100, ImageNet16-120, and ImageNet-1k, across various public model benchmarks. The experiment results show that ASP can obtain better results than other data selection methods at all selection ratios. ASP can also enable much more efficient AutoML processing with a speedup of 2x-20x while obtaining better architectures and better hyper-parameters compared to utilizing the entire dataset.Comment: This paper was actually finished in 202

Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization

Recently, the remarkable advance of the Large Language Model (LLM) has inspired researchers to transfer its extraordinary reasoning capability to both vision and language data. However, the prevailing approaches primarily regard the visual input as a prompt and focus exclusively on optimizing the text generation process conditioned upon vision content by a frozen LLM. Such an inequitable treatment of vision and language heavily constrains the model's potential. In this paper, we break through this limitation by representing both vision and language in a unified form. Specifically, we introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language that LLM can read. The resulting visual tokens encompass high-level semantics worthy of a word and also support dynamic sequence length varying from the image. Coped with this tokenizer, the presented foundation model called LaVIT can handle both image and text indiscriminately under the same generative learning paradigm. This unification empowers LaVIT to serve as an impressive generalist interface to understand and generate multi-modal content simultaneously. Extensive experiments further showcase that it outperforms the existing models by a large margin on massive vision-language tasks. Our code and models will be available at https://github.com/jy0205/LaVIT

KwaiYiiMath: Technical Report

Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning. In this report, we introduce the KwaiYiiMath which enhances the mathematical reasoning abilities of KwaiYiiBase1, by applying Supervised Fine-Tuning (SFT) and Reinforced Learning from Human Feedback (RLHF), including on both English and Chinese mathematical tasks. Meanwhile, we also constructed a small-scale Chinese primary school mathematics test set (named KMath), consisting of 188 examples to evaluate the correctness of the problem-solving process generated by the models. Empirical studies demonstrate that KwaiYiiMath can achieve state-of-the-art (SOTA) performance on GSM8k, CMath, and KMath compared with the similar size models, respectively.Comment: technical report. arXiv admin note: text overlap with arXiv:2306.16636 by other author

FogChain: a blockchain-based Peer-to-Peer solar power trading system powered by fog AI

Microgrids, gaining traction from rising distributed generation for carbon reduction, demand novel solutions to regulate on-and off-grid operations, as well as both energy and monetary transfers between the microgrid and the central grid and among different microgrid participants. This research aims to develop and validate an intelligent microgrid management system to secure the competitiveness of Singapore's energy market, by leveraging the inherent synergy between two emerging technologies, i.e., blockchain for Peer-to-Peer (P2P) solar power trading and fog computing for grid infrastructure management. For this vision, we have developed FogChain, an integrative, cost-effective, and scalable microgrid operating system (MGOS), consisting of three technical service layers: 1) a novel microgrid information infrastructure based on the fog-computing paradigm (i.e., intelligence on edge); 2) a blockchain-based microgrid service layer, providing smart contract and decentralized control capabilities for grid application development; and 3) a microgrid application layer (i.e., P2P energy trading) over the blockchain-based grid service. This MGOS would fundamentally transform how solar power is traded among participating electricity prosumers, leading to potentially new operational and business models. We have implemented the FogChain system and conducted extensive experiments to verify its performance advantages. Our results demonstrate that FogChain can efficiently process energy auction among 1000 participants with 1.1 s delay on average, reduce transmission cost up to 20% under the loss-aware trading mechanism, and reduce the solar yield prediction error to 0.11. Our system prototype suggests that FogChain provides a promising solution for efficient decentralized energy trading and intelligent distributed control for microgrids.Energy Market Authority (EMA)Ministry of Education (MOE)National Research Foundation (NRF)This work was supported in part by the National Research Foundation, Singapore, and the Energy Market Authority through Energy Programme under EP Award NRF2017EWT-EP003-023, and in part by the MOE through Tier-1 Grant Call under Award RG96/20