Deep learning for anomaly detection: A review

G Pang, C Shen, L Cao, AVD Hengel - ACM computing surveys (CSUR), 2021‏ - dl.acm.org
Anomaly detection, aka outlier detection or novelty detection, has been a lasting yet active
research area in various research communities for several decades. There are still some …

Fair ranking: a critical review, challenges, and future directions

GK Patro, L Porcaro, L Mitchell, Q Zhang… - Proceedings of the …, 2022‏ - dl.acm.org
Ranking, recommendation, and retrieval systems are widely used in online platforms and
other societal systems, including e-commerce, media-streaming, admissions, gig platforms …

Judging llm-as-a-judge with mt-bench and chatbot arena

L Zheng, WL Chiang, Y Sheng… - Advances in …, 2023‏ - proceedings.neurips.cc
Evaluating large language model (LLM) based chat assistants is challenging due to their
broad capabilities and the inadequacy of existing benchmarks in measuring human …

Bias and debias in recommender system: A survey and future directions

J Chen, H Dong, X Wang, F Feng, M Wang… - ACM Transactions on …, 2023‏ - dl.acm.org
While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …

Mllm-as-a-judge: Assessing multimodal llm-as-a-judge with vision-language benchmark

D Chen, R Chen, S Zhang, Y Wang, Y Liu… - … on Machine Learning, 2024‏ - openreview.net
Multimodal Large Language Models (MLLMs) have gained significant attention recently,
showing remarkable potential in artificial general intelligence. However, assessing the utility …

Judgelm: Fine-tuned large language models are scalable judges

L Zhu, X Wang, X Wang - arxiv preprint arxiv:2310.17631, 2023‏ - arxiv.org
Evaluating Large Language Models (LLMs) in open-ended scenarios is challenging
because existing benchmarks and metrics can not measure them comprehensively. To …

Socially responsible ai algorithms: Issues, purposes, and challenges

L Cheng, KR Varshney, H Liu - Journal of Artificial Intelligence Research, 2021‏ - jair.org
In the current era, people and society have grown increasingly reliant on artificial
intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of …

Causerec: Counterfactual user sequence synthesis for sequential recommendation

S Zhang, D Yao, Z Zhao, TS Chua, F Wu - Proceedings of the 44th …, 2021‏ - dl.acm.org
Learning user representations based on historical behaviors lies at the core of modern
recommender systems. Recent advances in sequential recommenders have convincingly …

Measuring misinformation in video search platforms: An audit study on YouTube

E Hussein, P Juneja, T Mitra - Proceedings of the ACM on human …, 2020‏ - dl.acm.org
Search engines are the primary gateways of information. Yet, they do not take into account
the credibility of search results. There is a growing concern that YouTube, the second largest …

Controlling fairness and bias in dynamic learning-to-rank

M Morik, A Singh, J Hong, T Joachims - Proceedings of the 43rd …, 2020‏ - dl.acm.org
Rankings are the primary interface through which many online platforms match users to
items (eg news, products, music, video). In these two-sided markets, not only the users draw …