Futures are contracts obligating the exchange of an asset at a predetermined date and price, notable for their high leverage (e.g., 5-fold) and liquidity (e.g., trillions of dollars) and, therefore, thrive in the Crypto market. Reinforcement learning (RL) has been widely applied in various quantitative tasks. However, most methods focus on the spot (e.g., stock) and could not be directly applied to the futures market with high leverage because of 2 key challenges. First, high leverage amplifies reward fluctuations, making RL training highly stochastic and difficult to converge. Second, prior works lacked self-awareness of capability boundaries, exposing them to the risk of significant capital loss when encountering previously unseen market state representations (e.g., during a black swan event like COVID-19). To tackle these challenges, we propose the eFficient and rIsk-aware eNsemble rEinforcement learning for Futures Trading (FineFT), a novel three-stage ensemble RL framework with stable training and proper risk management. In stage I, ensemble Q learners are selectively updated by ensemble temporal difference (TD) errors, i.e., TD errors across different learners, to improve convergence and performance. In stage II, we filter the Q-learners based on their profitabilities under different market dynamics and train variational autoencoders (VAEs) on market representations of each dynamic to identify the capability boundaries of the filtered learners. In stage III, we dynamically choose from the filtered ensemble and a conservative policy, guided by trained VAEs, to maintain profitability and mitigate risk with new market states. Through extensive experiments on crypto futures in a high-frequency trading environment with high fidelity and 5x leverage, we demonstrate that FineFT significantly outperforms 12 state-of-the-art baselines in 6 widely-used financial metrics, reducing risk by more than 40% while achieving superior profitability compared to the runner-up. Visualization of the selective update mechanism shows that different agents specialize in distinct market dynamics, and ablation studies certify routing with VAEs reduces maximum drawdown effectively, and selective update improves convergence and performance.

Visual-textual cross-modal hashing is an efficient method for large-scale multi-source heterogeneous data retrieval. However, existing unsupervised methods predominantly learn coarse-grained representations and establish global matching relationships, neglecting fine-grained alignment. Furthermore, image-text pairs exhibit partial semantic gaps and noise, leading to inaccurate similarity measurements. To tackle these issues, this paper proposes a Global-Regional Dual Hashing (GRDH) for unsupervised visual-textual retrieval, aiming to bridge the semantic ambiguity between image-text pairs and align multi-granularity representations within a unified transformer-based framework through concept similarity guidance. Specifically, we design the visual-textual transformers to extract global-region tokens from multimodal data. The region token refinement module is designed to eliminate semantically ambiguous region tokens, effectively reducing computational overhead for global-region interactions while promoting attention-based region information coordination. Secondly, under the guidance of concept-enhanced similarity, we constructed the global-regional semantic alignment loss that enables token-level and hash-level alignment in both continuous Euclidean space and discrete Hamming space. Finally, the global-regional dual hashing module generates hash codes of global-regional branches. Through cross-modal consistent hashing learning, it maintains semantic distribution consistency of cross-modal hash codes. Extensive experiments conducted on two baseline datasets (i.e., MIRFLICKR-25K and NUS-WIDE) demonstrate that our GRDH model outperforms recent state-of-the-art unsupervised methods, validating the effectiveness of our approach.

The growing scale of ad auctions on online advertising platforms has intensified competition, making manual bidding impractical and necessitating auto-bidding to help advertisers achieve their economic goals. Current auto-bidding methods have evolved to use offline reinforcement learning or generative methods to optimize bidding strategies, but they can sometimes behave counterintuitively due to the black-box training manner and limited mode coverage of datasets, leading to challenges in understanding task status and generalization in dynamic ad environments. Large language models (LLMs) offer a promising solution by leveraging prior human knowledge and reasoning abilities to improve auto-bidding performance. However, directly applying LLMs to auto-bidding faces difficulties due to the need for precise actions in competitive auctions and the lack of specialized auto-bidding knowledge, which can lead to hallucinations and suboptimal decisions. To address these challenges, we propose a hierarchical Large auto-Bidding Model (LBM) to leverage the reasoning capabilities of LLMs for developing a superior auto-bidding strategy. This includes a high-level LBM-Think model for reasoning and a low-level LBM-Act model for action generation. Specifically, we propose a dual embedding mechanism to efficiently fuse two modalities, including language and numerical inputs, for language-guided training of the LBM-Act; then, we propose an offline reinforcement fine-tuning technique termed GQPO for mitigating the LLM-Think's hallucinations and enhancing decision-making performance without simulation or real-world rollout like previous multi-turn LLM-based methods. Experiments demonstrate the superiority of a generative backbone based on our LBM, especially in an efficient training manner and generalization ability.

Auto-bidding, with its strong capability to optimize bidding decisions within dynamic and competitive online environments, has become a pivotal strategy for advertising platforms. Existing approaches typically employ rule-based strategies or Reinforcement Learning (RL) techniques. However, rule-based strategies lack the flexibility to adapt to time-varying market conditions, and RL-based methods struggle to capture essential historical dependencies and observations within Markov Decision Process (MDP) frameworks. Furthermore, these approaches often face challenges in ensuring strategy adaptability across diverse advertising objectives. Additionally, as offline training methods are increasingly adopted to facilitate the deployment and maintenance of stable online strategies, the issues of documented behavioral patterns and behavioral collapse resulting from training on fixed offline datasets become increasingly significant. To address these limitations, this paper introduces a novel offline Generative Auto-bidding framework with Value-Guided Explorations (GAVE). GAVE accommodates various advertising objectives through a score-based Return-To-Go (RTG) module. Moreover, GAVE integrates an action exploration mechanism with an RTG-based evaluation method to explore novel actions while ensuring stability-preserving updates. A learnable value function is also designed to guide the direction of action exploration and mitigate Out-of-Distribution (OOD) problems. Experimental results on two offline datasets and real-world deployments demonstrate that GAVE outperforms state-of-the-art baselines in both offline evaluations and online A/B tests. By applying the core methods of this framework, we proudly secured first place in the NeurIPS 2024 competition, 'AIGB Track: Learning Auto-Bidding Agents with Generative Models'.

Despite the great success of existing cross-modal retrieval methods, existing unsupervised cross-modal hashing methods still suffer from common problems. First, the features extracted from the text are too sparse. Second, the similarity matrices of each different modality cannot be fused adaptively. In this paper, we propose Deep Enhanced-Similarity Attention Hashing (DESAH) to alleviate the above problems. Firstly, we construct a text encoder expanding graph convolutional neural network to simultaneously extract features of samples and their semantic neighbors to enrich text features. Secondly, we propose an enhanced attention fusion mechanism. The mechanism is used to adaptively fuse the similarity matrices within different modalities to form a unified inter-modal similarity matrix to guide the learning of hash functions. Extensive experiments have demonstrated that DESAH provides significant improvements in cross-modal retrieval tasks compared to baseline methods.

Unsupervised cross-modal hashing (UCMH) has attracted increasing research due to its efficient retrieval performance and label irrelevance. However, existing methods have some bottlenecks: Firstly, the existing unsupervised methods suffer from inaccurate similarity measures due to the lack of correlation between features of different modalities and simple features cannot fully describe the fine-grained relationships of multi-modal data. Secondly, existing methods have rarely explored vision-language knowledge distillation schemes to distil multi-modal knowledge of these vision-language models to guide the learning of student networks. To address these bottlenecks, this paper proposes an effective unsupervised cross-modal hashing retrieval method, called Vision-Language Knowledge Distillation for Unsupervised Cross-Modal Hashing Retrieval (VLKD). VLKD uses the vision-language pre-training (VLP) model to encode features on multi-modal data, and then constructs a similarity matrix to provide soft similarity supervision for the student model. It distils the knowledge of the VLP model to the student model to gain an understanding of multi-modal knowledge. In addition, we designed an end-to-end unsupervised hashing learning model that incorporates a graph convolutional auxiliary network. The auxiliary network aggregates information from similar data nodes based on the similarity matrix distilled by the teacher model to generate more consistent hash codes. Finally, the teacher network does not require additional training, it only needs to guide the student network to learn high-quality hash representation, and VLKD is quite efficient in training and retrieval. Sufficient experiments on three multimedia retrieval benchmark datasets show that the proposed method achieves better retrieval performance compared to existing unsupervised cross-modal hashing methods, demonstrating the effectiveness of the proposed method.

Due to the advantages of low storage cost and fast retrieval efficiency, deep hashing methods are widely used in cross‐modal retrieval. Images are usually accompanied by corresponding text descriptions rather than labels. Therefore, unsupervised methods have been widely concerned. However, due to the modal divide and semantic differences, existing unsupervised methods cannot adequately bridge the modal differences, leading to suboptimal retrieval results. In this paper, we propose CLIP‐based cycle alignment hashing for unsupervised vision‐text retrieval (CCAH), which aims to exploit the semantic link between the original features of modalities and the reconstructed features. Firstly, we design a modal cyclic interaction method that aligns semantically within intramodality, where one modal feature reconstructs another modal feature, thus taking full account of the semantic similarity between intramodal and intermodal relationships. Secondly, introducing GAT into cross‐modal retrieval tasks. We consider the influence of text neighbour nodes and add attention mechanisms to capture the global features of text modalities. Thirdly, Fine‐grained extraction of image features using the CLIP visual coder. Finally, hash encoding is learned through hash functions. The experiments demonstrate on three widely used datasets that our proposed CCAH achieves satisfactory results in total retrieval accuracy. Our code can be found at: https://github.com/CQYIO/CCAH.git .