Search Results for author:
Found 19 papers, 10 papers with code
Flash Boys 2.0: Frontrunning, Transaction Reordering, and Consensus Instability in Decentralized Exchanges
We study the breadth of DEX arbitrage bots in a subset of transactions that yield quantifiable revenue to these bots.
Cryptography and Security Computer Science and Game Theory
Neural Collaborative Reasoning
Existing Collaborative Filtering (CF) methods are mostly designed based on the idea of matching, i. e., by learning user and item embeddings from data using shallow or deep models, they try to capture the associative relevance patterns in data, so that a user embedding can be matched with relevant item embeddings using designed or learned similarity functions.
Learning Post-Hoc Causal Explanations for Recommendation
State-of-the-art recommender systems have the ability to generate high-quality recommendations, but usually cannot provide intuitive explanations to humans due to the usage of black-box prediction models.
Towards Long-term Fairness in Recommendation
We focus on the fairness of exposure of items in different groups, while the division of the groups is based on item popularity, which dynamically changes over time in the recommendation process.
Discrete Knowledge Graph Embedding based on Discrete Optimization
This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method, which projects KG entities and relations into the Hamming space based on a computationally tractable discrete optimization algorithm, to solve the formidable storage and computation cost challenges in traditional continuous graph embedding methods.
Causal Collaborative Filtering
However, pure correlative learning may lead to Simpson's paradox in predictions, and thus results in sacrificed recommendation performance.
User-oriented Fairness in Recommendation
To solve this problem, we provide a re-ranking approach to mitigate this unfairness problem by adding constraints over evaluation metrics.
Personalized Counterfactual Fairness in Recommendation
Therefore, it is important to provide personalized fair recommendations for users to satisfy their personalized fairness demands.
Counterfactual Explainable Recommendation
Technically, for each item recommended to each user, CountER formulates a joint optimization problem to generate minimal changes on the item aspects so as to create a counterfactual item, such that the recommendation decision on the counterfactual item is reversed.
Counterfactual Evaluation for Explainable AI
While recent years have witnessed the emergence of various explainable methods in machine learning, to what degree the explanations really represent the reasoning process behind the model prediction -- namely, the faithfulness of explanation -- is still an open problem.
Graph Collaborative Reasoning
Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering.
Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning
For quantitatively evaluating the generated explanations without the requirement of ground-truth, we design metrics based on Counterfactual and Factual reasoning to evaluate the necessity and sufficiency of the explanations.
Fairness in Recommendation: Foundations, Methods and Applications
It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking in order to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems.
A Survey on Trustworthy Recommender Systems
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process.
Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS)
Experiments on different datasets show that the adaptive architecture assembled by MANAS outperforms static global architectures.
Elements of effective machine learning datasets in astronomy
We define effective machine learning datasets in astronomy to be formed with well-defined data points, structure, and metadata.
Causal Inference for Recommendation: Foundations, Methods and Applications
We discuss the fundamental concepts of both recommender systems and causal inference as well as their relationship, and review the existing work on causal methods for different problems in recommender systems.
Transferable Fairness for Cold-Start Recommendation
The proposed method is able to transfer the knowledge of a fair model learned from the source users to the target users with the hope of improving the recommendation performance and keeping the fairness property on the target users.
Fairness of ChatGPT
Understanding and addressing unfairness in LLMs are crucial for responsible AI deployment.
