Search Results - "Farnadi, Golnoosh"

A taxonomy of weight learning methods for statistical relational learning by Srinivasan, Sriram, Dickens, Charles, Augustine, Eriq, Farnadi, Golnoosh, Getoor, Lise

“…Statistical relational learning (SRL) frameworks are effective at defining probabilistic models over complex relational data. They often use weighted…”
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Computational personality recognition in social media by Farnadi, Golnoosh, Sitaraman, Geetha, Sushmita, Shanu, Celli, Fabio, Kosinski, Michal, Stillwell, David, Davalos, Sergio, Moens, Marie-Francine, De Cock, Martine

“…A variety of approaches have been recently proposed to automatically infer users’ personality from their user generated content in social media. Approaches…”
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Soft quantification in statistical relational learning by Farnadi, Golnoosh, Bach, Stephen H., Moens, Marie-Francine, Getoor, Lise, De Cock, Martine

“…We present a new statistical relational learning (SRL) framework that supports reasoning with soft quantifiers, such as “most” and “a few.” We define the…”
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On the Implicit Relation Between Low-Rank Adaptation and Differential Privacy by Malekmohammadi, Saber, Farnadi, Golnoosh

“…A significant approach in natural language processing involves large-scale pre-training models on general domain data followed by their adaptation to specific…”
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Tidying Up the Conversational Recommender Systems' Biases by Moradi, Armin, Farnadi, Golnoosh

“…The growing popularity of language models has sparked interest in conversational recommender systems (CRS) within both industry and research circles. However,…”
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Multilingual Hallucination Gaps in Large Language Models by Chataigner, Cléa, Taïk, Afaf, Farnadi, Golnoosh

“…Large language models (LLMs) are increasingly used as alternatives to traditional search engines given their capacity to generate text that resembles human…”
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Towards More Realistic Extraction Attacks: An Adversarial Perspective by More, Yash, Ganesh, Prakhar, Farnadi, Golnoosh

“…Language models are prone to memorizing parts of their training data which makes them vulnerable to extraction attacks. Existing research often examines…”
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Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities by Farnadi, Golnoosh, Havaei, Mohammad, Rostamzadeh, Negar

“…The rise of foundation models holds immense promise for advancing AI, but this progress may amplify existing risks and inequalities, leaving marginalized…”
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Mitigating Disparate Impact of Differential Privacy in Federated Learning through Robust Clustering by Malekmohammadi, Saber, Taik, Afaf, Farnadi, Golnoosh

“…Federated Learning (FL) is a decentralized machine learning (ML) approach that keeps data localized and often incorporates Differential Privacy (DP) to enhance…”
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Understanding Intrinsic Socioeconomic Biases in Large Language Models by Arzaghi, Mina, Carichon, Florian, Farnadi, Golnoosh

“…Large Language Models (LLMs) are increasingly integrated into critical decision-making processes, such as loan approvals and visa applications, where inherent…”
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Advancing Cultural Inclusivity: Optimizing Embedding Spaces for Balanced Music Recommendations by Moradi, Armin, Neophytou, Nicola, Farnadi, Golnoosh

“…Popularity bias in music recommendation systems -- where artists and tracks with the highest listen counts are recommended more often -- can also propagate…”
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Promoting Fair Vaccination Strategies Through Influence Maximization: A Case Study on COVID-19 Spread by Neophytou, Nicola, Taïk, Afaf, Farnadi, Golnoosh

“…The aftermath of the Covid-19 pandemic saw more severe outcomes for racial minority groups and economically-deprived communities. Such disparities can be…”
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Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness by Ehyaei, Ahmad-Reza, Farnadi, Golnoosh, Samadi, Samira

“…Despite the essential need for comprehensive considerations in responsible AI, factors like robustness, fairness, and causality are often studied in isolation…”
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Fairness Through Domain Awareness: Mitigating Popularity Bias For Music Discovery by Salganik, Rebecca, Diaz, Fernando, Farnadi, Golnoosh

“…As online music platforms grow, music recommender systems play a vital role in helping users navigate and discover content within their vast musical databases…”
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Different Horses for Different Courses: Comparing Bias Mitigation Algorithms in ML by Ganesh, Prakhar, Gohar, Usman, Cheng, Lu, Farnadi, Golnoosh

“…With fairness concerns gaining significant attention in Machine Learning (ML), several bias mitigation techniques have been proposed, often compared against…”
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FairLoRA: Unpacking Bias Mitigation in Vision Models with Fairness-Driven Low-Rank Adaptation by Sukumaran, Rohan, Feizi, Aarash, Romero-Sorian, Adriana, Farnadi, Golnoosh

“…Recent advances in parameter-efficient fine-tuning methods, such as Low Rank Adaptation (LoRA), have gained significant attention for their ability to…”
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Trust No Bot: Discovering Personal Disclosures in Human-LLM Conversations in the Wild by Mireshghallah, Niloofar, Antoniak, Maria, More, Yash, Choi, Yejin, Farnadi, Golnoosh

“…Measuring personal disclosures made in human-chatbot interactions can provide a better understanding of users' AI literacy and facilitate privacy research for…”
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The Cost of Arbitrariness for Individuals: Examining the Legal and Technical Challenges of Model Multiplicity by Ganesh, Prakhar, Daldaban, Ihsan Ibrahim, Cofone, Ignacio, Farnadi, Golnoosh

“…Model multiplicity, the phenomenon where multiple models achieve similar performance despite different underlying learned functions, introduces arbitrariness…”
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Analyzing the Effect of Sampling in GNNs on Individual Fairness by Salganik, Rebecca, Diaz, Fernando, Farnadi, Golnoosh

“…Graph neural network (GNN) based methods have saturated the field of recommender systems. The gains of these systems have been significant, showing the…”
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Fairness Incentives in Response to Unfair Dynamic Pricing by Thibodeau, Jesse, Nekoei, Hadi, Taïk, Afaf, Rajendran, Janarthanan, Farnadi, Golnoosh

“…The use of dynamic pricing by profit-maximizing firms gives rise to demand fairness concerns, measured by discrepancies in consumer groups' demand responses to…”
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