{"type":"ScholarlyArticle","title":"FairReFuse: Referee-Guided Fusion for Multi-Modal Causal Fairness in Depression Detection","authors":[{"name":"Jiaee Cheong","slug":"jiaee-cheong","given":"Jiaee","family":"Cheong","affiliation":"University of Cambridge; Middle East Technical University; The Alan Turing Institute; East Technical University (METU) ."},{"name":"Sinan Kalkan","slug":"sinan-kalkan","given":"Sinan","family":"Kalkan","affiliation":"Middle East Technical University"},{"name":"Hatice Gunes","slug":"hatice-gunes","given":"Hatice","family":"Gunes","affiliation":"University of Cambridge"}],"abstract":"Machine learning (ML) bias in mental health detection and analysis is becoming an increasingly pertinent challenge. Despite promising efforts indicating that  multimodal methods work better than unimodal methods, there is minimal work on multimodal fairness for depression detection. We  propose a causal multimodal framework which consists of two modules. Module 1 performs causal interventional debiasing via backdoor adjustment for each modality to achieve group fairness. Module 2 adaptively fuses the different modalities using a referee-based individual fairness guided fusion mechanism to address individual fairness. We conduct experiments and ablation studies on three depression datasets, D-Vlog, DAIC-WOZ and E-DAIC, and show that our framework improves classification performance as well as group and individual fairness compared to existing approaches.","doi":"10.24963/ijcai.2024/799","url":"https://ijcai.org/proceedings/2024/799","pdf":"https://ijcai.org/proceedings/2024/0799.pdf","markdown":"https://ijcai.org/proceedings/2024/799.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/799","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI for Good","track":"AI for Good","number":799,"pages":{"first":7224,"last":7232},"keywords":["AI Ethics, Trust, Fairness: General","Multidisciplinary Topics and Applications: General","Machine Learning: General"],"videos":[],"references":[{"position":1,"raw":"[Alasadi et al., 2020] Jamal Alasadi, Ramanathan Arunachalam, Pradeep K. Atrey, and Vivek K. Singh. A fairness-aware fusion framework for multimodal cyberbullying detection. In (BigMM), pages 166-173, 2020.","title":"A fairness-aware fusion framework for multimodal cyberbullying detection","authors":"Jamal Alasadi, Ramanathan Arunachalam, Pradeep K Atrey, Vivek K Singh","year":"2020","venue":"BigMM)","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bailey and Plumbley, 2021] Andrew Bailey and Mark D Plumbley. Gender bias in depression detection using audio features. In 2021 29th European Signal Processing Conference (EUSIPCO), pages 596-600. IEEE, 2021.","title":"Gender bias in depression detection using audio features","authors":"Andrew Bailey, Mark D Plumbley","year":"2021","venue":"29th European Signal Processing Conference (EUSIPCO)","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Baltrušaitis et al., 2018] Tadas Baltrušaitis, Chaitanya Ahuja, and Louis-Philippe Morency. Multimodal machine learning: A sur- vey and taxonomy. IEEE transactions on pattern analysis and machine intelligence, 41(2):423-443, 2018.","title":"Multimodal machine learning: A survey and taxonomy","authors":"Tadas Baltrušaitis, Chaitanya Ahuja, Louis-Philippe Morency","year":"2018","venue":"IEEE transactions on pattern analysis and machine intelligence","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Barsky et al., 2001] Arthur J Barsky, Heli M Peekna, and Jonathan F Borus. Somatic symptom reporting in women and men. Journal of general internal medicine, 16(4):266-275, 2001.","title":"Somatic symptom reporting in women and men","authors":"J Arthur, Heli M Barsky, Jonathan F Peekna, Borus","year":"2001","venue":"Journal of general internal medicine","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Bolukbasi et al., 2016] Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. Man is to com- puter programmer as woman is to homemaker? debiasing word embeddings. NeurIPS, 29, 2016.","title":"Man is to computer programmer as woman is to homemaker? debiasing word embeddings","authors":"Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, Adam T Kalai","year":"2016","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Booth et al., 2021] Brandon M Booth, Louis Hickman, Shree Kr- ishna Subburaj, Louis Tay, Sang Eun Woo, and Sidney K D'Mello. Bias and fairness in multimodal machine learning: A case study of automated video interviews. In ICMI, 2021.","title":"Bias and fairness in multimodal machine learning: A case study of automated video interviews","authors":"M Brandon, Louis Booth, Hickman, Krishna Shree, Louis Subburaj, Sang Eun Tay, Sidney K D' Woo, Mello","year":"2021","venue":"ICMI","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen et al., 2022] Yingjie Chen, Diqi Chen, Tao Wang, Yizhou Wang, and Yun Liang. Causal intervention for subject- deconfounded facial action unit recognition. In AAAI, pages 374- 382, 2022.","title":"Causal intervention for subjectdeconfounded facial action unit recognition","authors":"Yingjie Chen, Diqi Chen, Tao Wang, Yizhou Wang, Yun Liang","year":"2022","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chen et al., 2023] Weixin Chen, Li Chen, Yongxin Ni, Yuhan Zhao, Fajie Yuan, and Yongfeng Zhang. Fmmrec: Fairness-aware multimodal recommendation. arXiv preprint, 2023.","title":"Fmmrec: Fairness-aware multimodal recommendation","authors":"Weixin Chen, Li Chen, Yongxin Ni, Yuhan Zhao, Fajie Yuan, Yongfeng Zhang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Cheong et al., 2021] Jiaee Cheong, Sinan Kalkan, and Hatice Gunes. The hitchhiker's guide to bias and fairness in facial af- fective signal processing: Overview and techniques. IEEE Signal Processing Magazine, 38(6):39-49, 2021.","title":"The hitchhiker's guide to bias and fairness in facial affective signal processing: Overview and techniques","authors":"Cheong","year":"2021","venue":"IEEE Signal Processing Magazine","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Cheong et al., 2022] Jiaee Cheong, Sinan Kalkan, and Hatice Gunes. Causal fairness for affect recognition. NeurIPS AFCP Workshop, 2022.","title":"Causal fairness for affect recognition","authors":"Cheong","year":"2022","venue":"NeurIPS AFCP Workshop","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Cheong et al., 2023a] Jiaee Cheong, Sinan Kalkan, and Hatice Gunes. Causal structure learning of bias for fair affect recog- nition. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023.","title":"Causal structure learning of bias for fair affect recognition","authors":"Cheong","year":"2023","venue":"Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Cheong et al., 2023b] Jiaee Cheong, Sinan Kalkan, and Hatice Gunes. Counterfactual fairness for facial expression recognition. In Computer Vision-ECCV 2022 Workshops: Tel Aviv, Israel, Proceedings, Part V, pages 245-261. Springer, 2023.","title":"Counterfactual fairness for facial expression recognition","authors":"Cheong","year":"2023","venue":"Computer Vision-ECCV 2022 Workshops: Tel Aviv, Israel, Proceedings, Part V","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Cheong et al., 2023c] Jiaee Cheong, Selim Kuzucu, Sinan Kalkan, and Hatice Gunes. Towards gender fairness for mental health prediction. In IJCAI 2023, 2023.","title":"Towards gender fairness for mental health prediction","authors":"Cheong","year":"2023","venue":"IJCAI 2023","doi":null,"url":null,"internal_url":"/proceedings/2023/658"},{"position":14,"raw":"[Cheong et al., 2023d] Jiaee Cheong, Micol Spitale, and Hatice Gunes. \"it's not fair!\" -fairness for a small dataset of multi- modal dyadic mental well-being coaching. In ACII 2023, 2023.","title":"it's not fair!\" -fairness for a small dataset of multimodal dyadic mental well-being coaching","authors":"Cheong","year":"2023","venue":"ACII 2023","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Churamani et al., 2023] Nikhil Churamani, Jiaee Cheong, Sinan Kalkan, and Hatice Gunes. Towards causal replay for knowledge rehearsal in continual learning. In PMLR, pages 63-70, 2023.","title":"Towards causal replay for knowledge rehearsal in continual learning","authors":"Nikhil Churamani, Jiaee Cheong, Sinan Kalkan, Hatice Gunes","year":"2023","venue":"PMLR","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"Elvan C ¸iftc ¸i, Heysem Kaya, Hüseyin Gülec ¸, and Albert Ali Salah. The turkish audio-visual bipolar disorder corpus. In 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia), pages 1-6. IEEE, 2018.","title":"The turkish audio-visual bipolar disorder corpus","authors":"Heysem Elvan C ¸iftc ¸i, Hüseyin Kaya, ¸ Gülec, Albert Ali Salah","year":"2018","venue":"First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia)","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Dalal et al., 2023] Sumit Dalal, Sarika Jain, and Mayank Dave. Early depression detection using textual cues from social data: A research agenda. In IHIC, pages 393-406. Springer, 2023.","title":"Early depression detection using textual cues from social data: A research agenda","authors":"Sumit Dalal, Sarika Jain, Mayank Dave","year":"2023","venue":"IHIC","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Dosovitskiy et al., 2020] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2020.","title":"An image is worth 16x16 words: Transformers for image recognition at scale","authors":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Georg Heigold, Sylvain Gelly","year":"2020","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Dwork et al., 2012] Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference, pages 214-226, 2012.","title":"Fairness through awareness","authors":"Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Richard Zemel","year":"2012","venue":"Proceedings of the 3rd innovations in theoretical computer science conference","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Feng and Shah, 2022] Yunhe Feng and Chirag Shah. Has ceo gen- der bias really been fixed? adversarial attacking and improving gender fairness in image search. In AAAI, volume 36, pages 11882-11890, 2022.","title":"Has ceo gender bias really been fixed? adversarial attacking and improving gender fairness in image search","authors":"Shah Feng, Yunhe Feng, Chirag Shah","year":"2022","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Garg et al., 2018] Nikhil Garg, Londa Schiebinger, Dan Jurafsky, and James Zou. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences, 115(16):E3635-E3644, 2018.","title":"Word embeddings quantify 100 years of gender and ethnic stereotypes","authors":"Nikhil Garg, Londa Schiebinger, Dan Jurafsky, James Zou","year":"2018","venue":"Proceedings of the National Academy of Sciences","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Hall et al., 2000] Judith A Hall, Jason D Carter, and Terrence G Horgan. Gender differences in nonverbal communication of emo- tion. Gender and emotion: Social psychological perspectives, pages 97-117, 2000.","title":"Gender differences in nonverbal communication of emotion","authors":"Judith A Hall, Jason D Carter, Terrence G Horgan","year":"2000","venue":"Gender and emotion: Social psychological perspectives","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[He et al., 2022] Lang He, Mingyue Niu, Prayag Tiwari, Pekka Marttinen, Rui Su, Jiewei Jiang, Chenguang Guo, Hongyu Wang, Songtao Ding, Zhongmin Wang, et al. Deep learning for depres- sion recognition with audiovisual cues: A review. Information Fusion, 80:56-86, 2022.","title":"Deep learning for depression recognition with audiovisual cues: A review","authors":"Lang He, Mingyue Niu, Prayag Tiwari, Pekka Marttinen, Rui Su, Jiewei Jiang, Chenguang Guo, Hongyu Wang, Songtao Ding, Zhongmin Wang","year":"2022","venue":"Information Fusion","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Hort et al., 2022] Max Hort, Zhenpeng Chen, Jie M Zhang, Fed- erica Sarro, and Mark Harman. Bias mitigation for machine learning classifiers: A comprehensive survey. arXiv preprint arXiv:2207.07068, 2022.","title":"Bias mitigation for machine learning classifiers: A comprehensive survey","authors":"Hort","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Janghorbani and De Melo, 2023] Sepehr Janghorbani and Gerard De Melo. Multi-modal bias: Introducing a framework for stereotypical bias assessment beyond gender and race in vision- language models. In EACL, pages 1717-1727, 2023.","title":"Multi-modal bias: Introducing a framework for stereotypical bias assessment beyond gender and race in visionlanguage models","authors":"De Janghorbani, ; Melo, Janghorbani Sepehr, Gerard De, Melo","year":"2023","venue":"EACL","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Jansz and others, 2000] Jeroen Jansz et al. Masculine identity and restrictive emotionality. Gender and emotion: Social psycholog- ical perspectives, pages 166-186, 2000.","title":"Masculine identity and restrictive emotionality","authors":"Jeroen Jansz, Jansz","year":"2000","venue":"Gender and emotion: Social psychological perspectives","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Kathan et al., 2022] Alexander Kathan, Shahin Amiriparian, Lukas Christ, Andreas Triantafyllopoulos, Niklas Müller, An- dreas König, and Björn W Schuller. A personalised approach to audiovisual humour recognition and its individual-level fairness. In MuSe '22', pages 29-36, 2022.","title":"A personalised approach to audiovisual humour recognition and its individual-level fairness","authors":"Alexander Kathan, Shahin Amiriparian, Lukas Christ, Andreas Triantafyllopoulos, Niklas Müller, Andreas König, Björn W Schuller","year":"2022","venue":"MuSe '22","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Kendler et al., 1994] Kenneth S Kendler, Ellen E Walters, Kim R Truett, Andrew C Heath, Michael C Neale, Nicholas G Martin, and Lindon J Eaves. Sources of individual differences in depres- sive symptoms: analysis of two samples of twins and their fami- lies. The American Journal of Psychiatry, 151(11), 1994.","title":"Sources of individual differences in depressive symptoms: analysis of two samples of twins and their families","authors":"Ellen E Kenneth S Kendler, Walters, Andrew C Kim R Truett, Heath, Nicholas G Michael C Neale, Lindon J Martin, Eaves","year":"1994","venue":"The American Journal of Psychiatry","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Kingma and Ba, 2014] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. International Conference for Learning Representations, 2014.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2014","venue":"International Conference for Learning Representations","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Ma et al., 2016] Xingchen Ma, Hongyu Yang, Qiang Chen, Di Huang, and Yunhong Wang. Depaudionet: An efficient deep model for audio based depression classification. In 6th Intl. Work- shop on audio/visual emotion challenge, 2016.","title":"Depaudionet: An efficient deep model for audio based depression classification","authors":"Ma","year":"2016","venue":"6th Intl. Workshop on audio/visual emotion challenge","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Maj et al., 2020] Mario Maj, Dan J Stein, Gordon Parker, Mark Zimmerman, Giovanni A Fava, Marc De Hert, Koen Demyt- tenaere, Roger S McIntyre, Thomas Widiger, and Hans-Ulrich Wittchen. The clinical characterization of the adult patient with depression aimed at personalization of management. World Psy- chiatry, 19(3):269-293, 2020.","title":"The clinical characterization of the adult patient with depression aimed at personalization of management","authors":"Mario Maj, Dan J Stein, Gordon Parker, Mark Zimmerman, Giovanni A Fava, Marc De Hert, Koen Demyttenaere, Thomas Roger S Mcintyre, Hans-Ulrich Widiger, Wittchen","year":"2020","venue":"World Psychiatry","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Mandhala et al., 2023] Venkata Naresh Mandhala, Debnath Bhat- tacharyya, and Divya Midhunchakkaravarthy. A novel study on tools and frameworks for mitigating bias in multimodal datasets. In ICCIC, pages 277-283. Springer, 2023.","title":"A novel study on tools and frameworks for mitigating bias in multimodal datasets","authors":"Debnath Venkata Naresh Mandhala, Divya Bhattacharyya, Midhunchakkaravarthy","year":"2023","venue":"ICCIC","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Mukherjee et al., 2020] Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, and Yuekai Sun. Two simple ways to learn individual fairness metrics from data. In ICML, pages 7097-7107. PMLR, 2020.","title":"Two simple ways to learn individual fairness metrics from data","authors":"Mukherjee","year":"2020","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Ogrodniczuk and Oliffe, 2011] John S Ogrodniczuk and John L Oliffe. Men and depression. Canadian Family Physician, 57(2):153-155, 2011.","title":"Men and depression","authors":"S John, John L Ogrodniczuk, Oliffe","year":"2011","venue":"Canadian Family Physician","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Pearl, 2009] Judea Pearl. Causality. Cambridge Uni. Press, 2009.","title":"Judea Pearl. Causality","authors":"Pearl","year":"2009","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Peña et al., 2023] Alejandro Peña, Ignacio Serna, Aythami Morales, Julian Fierrez, Alfonso Ortega, Ainhoa Herrarte, Manuel Alcantara, and Javier Ortega-Garcia. Human-centric multimodal machine learning: Recent advances and testbed on ai-based recruitment. SN Computer Science, 4(5):434, 2023.","title":"Human-centric multimodal machine learning: Recent advances and testbed on ai-based recruitment","authors":"Alejandro Peña, Ignacio Serna, Aythami Morales, Julian Fierrez, Alfonso Ortega, Ainhoa Herrarte, Manuel Alcantara, Javier Ortega-Garcia","year":"2023","venue":"SN Computer Science","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Pessach and Shmueli, 2022] Dana Pessach and Erez Shmueli. A review on fairness in machine learning. ACM Computing Surveys (CSUR), 55(3):1-44, 2022.","title":"A review on fairness in machine learning","authors":"Shmueli Pessach, Dana Pessach, Erez Shmueli","year":"2022","venue":"ACM Computing Surveys (CSUR)","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Ringeval et al., 2019] Fabien Ringeval, Björn Schuller, Michel Valstar, Nicholas Cummins, Roddy Cowie, Leili Tavabi, Maxi- milian Schmitt, Sina Alisamir, Shahin Amiriparian, Eva-Maria Messner, et al. Avec 2019 workshop and challenge. AVEC 2019, 2019.","title":"workshop and challenge","authors":"Fabien Ringeval, Björn Schuller, Michel Valstar, Nicholas Cummins, Roddy Cowie, Leili Tavabi, Maximilian Schmitt, Sina Alisamir, Shahin Amiriparian, Eva-Maria Messner","year":"2019","venue":"AVEC","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Schmitz et al., 2022] Matheus Schmitz, Rehan Ahmed, and Jimi Cao. Bias and fairness on multimodal emotion detection algo- rithms. arXiv preprint arXiv:2205.08383, 2022.","title":"Bias and fairness on multimodal emotion detection algorithms","authors":"Matheus Schmitz, Rehan Ahmed, Jimi Cao","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Sendak et al., 2020] Mark Sendak, Madeleine Clare Elish, Michael Gao, Joseph Futoma, William Ratliff, Marshall Nichols, Armando Bedoya, Suresh Balu, and Cara O'Brien. \"the human body is a black box\" supporting clinical decision-making with deep learning. In FAccT, 2020.","title":"the human body is a black box\" supporting clinical decision-making with deep learning","authors":"Mark Sendak, Madeleine, Clare Elish, Michael Gao, Joseph Futoma, William Ratliff, Marshall Nichols, Armando Bedoya, Suresh Balu, Cara O' Brien","year":"2020","venue":"FAccT","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Song et al., 2018] Siyang Song, Linlin Shen, and Michel Val- star. Human behaviour-based automatic depression analysis us- ing hand-crafted statistics and deep learned spectral features. In FG 2018, pages 158-165. IEEE, 2018.","title":"Human behaviour-based automatic depression analysis using hand-crafted statistics and deep learned spectral features","authors":"Siyang Song, Linlin Shen, Michel Valstar","year":"2018","venue":"FG 2018","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Touvron et al., 2021] Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. Training data-efficient image transformers & distillation through attention. In ICML. PMLR, 2021.","title":"Training data-efficient image transformers & distillation through attention","authors":"Touvron","year":"2021","venue":"ICML. PMLR","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Vabalas et al., 2019] Andrius Vabalas, Emma Gowen, Ellen Poli- akoff, and Alexander J Casson. Machine learning algorithm val- idation with a limited sample size. PloS one, 14(11), 2019.","title":"Machine learning algorithm validation with a limited sample size","authors":"Andrius Vabalas, Emma Gowen, Ellen Poliakoff, Alexander J Casson","year":"2019","venue":"PloS one","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Valstar et al., 2016] Michel Valstar, Jonathan Gratch, Björn Schuller, Fabien Ringeval, Denis Lalanne, Mercedes Torres Tor- res, Stefan Scherer, Giota Stratou, Roddy Cowie, and Maja Pan- tic. Depression, mood, and emotion recognition workshop and challenge. In Avec 2016, 2016.","title":"Depression, mood, and emotion recognition workshop and challenge","authors":"Michel Valstar, Jonathan Gratch, Björn Schuller, Fabien Ringeval, Denis Lalanne, Mercedes Torres Torres, Stefan Scherer, Giota Stratou, Roddy Cowie, Maja Pantic","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Par- mar, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polo- sukhin. Attention is all you need. NeurIPS, 30, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin","year":"2017","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Wang et al., 2007] Philip S Wang, Sergio Aguilar-Gaxiola, Jordi Alonso, Matthias C Angermeyer, Guilherme Borges, Evelyn J Bromet, Ronny Bruffaerts, Ron De Graaf, Oye Gureje, et al. Use of mental health services for anxiety, mood, and substance disor- ders in 17 countries in the who world mental health surveys. The Lancet, 370(9590):841-850, 2007.","title":"Use of mental health services for anxiety, mood, and substance disorders in 17 countries in the who world mental health surveys","authors":"Sergio Philip S Wang, Jordi Aguilar-Gaxiola, Matthias C Alonso, Guilherme Angermeyer, Evelyn J Borges, Ronny Bromet, Ron De Bruffaerts, Oye Graaf, Gureje","year":"2007","venue":"The Lancet","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Wang et al., 2019] Jingying Wang, Lei Zhang, Tianli Liu, Wei Pan, Bin Hu, and Tingshao Zhu. Acoustic differences between healthy and depressed people: a cross-situation study. BMC psychiatry, 19:1-12, 2019.","title":"Acoustic differences between healthy and depressed people: a cross-situation study","authors":"Jingying Wang, Lei Zhang, Tianli Liu, Wei Pan, Bin Hu, Tingshao Zhu","year":"2019","venue":"BMC psychiatry","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Wang et al., 2022] Yikai Wang, Xinghao Chen, Lele Cao, Wen- bing Huang, Fuchun Sun, and Yunhe Wang. Multimodal token fusion for vision transformers. In CVPR, 2022.","title":"Multimodal token fusion for vision transformers","authors":"Yikai Wang, Xinghao Chen, Lele Cao, Wenbing Huang, Fuchun Sun, Yunhe Wang","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Williamson et al., 2016] James R Williamson, Elizabeth Godoy, Miriam Cha, Pooya Khorrami, Youngjune Gwon, Charlie Dagli, and Thomas F Quatieri. Detecting depression using vocal, fa- cial and semantic communication cues. In 6th Intl. Workshop on Audio/Visual Emotion Challenge, 2016.","title":"Detecting depression using vocal, facial and semantic communication cues","authors":"Elizabeth James R Williamson, Miriam Godoy, Pooya Cha, Youngjune Khorrami, Charlie Gwon, Thomas F Dagli, Quatieri","year":"2016","venue":"6th Intl. Workshop on Audio/Visual Emotion Challenge","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Wu et al., 2022] Kan Wu, Jinnian Zhang, Houwen Peng, Mengchen Liu, Bin Xiao, Jianlong Fu, and Lu Yuan. Tinyvit: Fast pretraining distillation for small vision transformers. In ECCV. Springer, 2022.","title":"Tinyvit: Fast pretraining distillation for small vision transformers","authors":"Kan Wu, Jinnian Zhang, Houwen Peng, Mengchen Liu, Bin Xiao, Jianlong Fu, Lu Yuan","year":"2022","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Yan et al., 2020] Shen Yan, Di Huang, and Mohammad Soley- mani. Mitigating biases in multimodal personality assessment. In ICMI, pages 361-369, 2020.","title":"Mitigating biases in multimodal personality assessment","authors":"Shen Yan, Di Huang, Mohammad Soleymani","year":"2020","venue":"ICMI","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Yang et al., 2012] Ying Yang, Catherine Fairbairn, and Jeffrey F Cohn. Detecting depression severity from vocal prosody. IEEE transactions on affective computing, 4(2):142-150, 2012.","title":"Detecting depression severity from vocal prosody","authors":"Ying Yang, Catherine Fairbairn, Jeffrey F Cohn","year":"2012","venue":"IEEE transactions on affective computing","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Yin et al., 2022] Hongxu Yin, Arash Vahdat, Jose M Alvarez, Arun Mallya, Jan Kautz, and Pavlo Molchanov. A-vit: Adaptive tokens for efficient vision transformer. In CVPR, 2022.","title":"A-vit: Adaptive tokens for efficient vision transformer","authors":"Arash Hongxu Yin, Jose M Vahdat, Arun Alvarez, Jan Mallya, Pavlo Kautz, Molchanov","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Yoon et al., 2022] Jeewoo Yoon, Chaewon Kang, Seungbae Kim, and Jinyoung Han. D-vlog: Multimodal vlog dataset for depres- sion detection. AAAI, 36(11), Jun. 2022.","title":"D-vlog: Multimodal vlog dataset for depression detection","authors":"Yoon","year":"2022","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":55,"raw":"[Yurochkin and Sun, 2021] Mikhail Yurochkin and Yuekai Sun. Sensei: Sensitive set invariance for enforcing individual fairness. In International Conference on Learning Representations, 2021.","title":"Sensei: Sensitive set invariance for enforcing individual fairness","authors":"Sun ; Yurochkin, Mikhail Yurochkin, Yuekai Sun","year":"2021","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":56,"raw":"[Zanna et al., 2022] Khadija Zanna, Kusha Sridhar, Han Yu, and Akane Sano. Bias reducing multitask learning on mental health prediction. In ACII, pages 1-8. IEEE, 2022.","title":"Bias reducing multitask learning on mental health prediction","authors":"Khadija Zanna, Kusha Sridhar, Han Yu, Akane Sano","year":"2022","venue":"ACII","doi":null,"url":null,"internal_url":null},{"position":57,"raw":"[Zemel et al., 2013] Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. Learning fair representations. In ICML, pages 325-333. PMLR, 2013.","title":"Learning fair representations","authors":"Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, Cynthia Dwork","year":"2013","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":58,"raw":"[Zheng et al., 2023] Wenbo Zheng, Lan Yan, and Fei-Yue Wang. Two birds with one stone: Knowledge-embedded temporal con- volutional transformer for depression detection and emotion recognition. IEEE Transactions on Affective Computing, 2023.","title":"Two birds with one stone: Knowledge-embedded temporal convolutional transformer for depression detection and emotion recognition","authors":"Wenbo Zheng, Lan Yan, Fei-Yue Wang","year":"2023","venue":"IEEE Transactions on Affective Computing","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}