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Found 22 papers, 8 papers with code
Robust high dimensional factor models with applications to statistical machine learning
Factor models are a class of powerful statistical models that have been widely used to deal with dependent measurements that arise frequently from various applications from genomics and neuroscience to economics and finance.
Fairness-Aware Recommendation of Information Curators
This paper highlights our ongoing efforts to create effective information curator recommendation models that can be personalized for individual users, while maintaining important fairness properties.
Learning Markov models via low-rank optimization
We show that one can estimate the full transition model accurately using a trajectory of length that is proportional to the total number of states.
Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition
Knowledge of a disease includes information of various aspects of the disease, such as signs and symptoms, diagnosis and treatment.
Popularity-Opportunity Bias in Collaborative Filtering
This paper connects equal opportunity to popularity bias in implicit recommenders to introduce the problem of popularity-opportunity bias.
Fairness-aware Personalized Ranking Recommendation via Adversarial Learning
This is paper is an extended and reorganized version of our SIGIR 2020~\cite{zhu2020measuring} paper.
End-to-end Learning for Fair Ranking Systems
The end-to-end SPOFR framework includes a constrained optimization sub-model and produces ranking policies that are guaranteed to satisfy fairness constraints while allowing for fine control of the fairness-utility tradeoff.
Session-based Recommendation with Hypergraph Attention Networks
Session-based recommender systems aim to improve recommendations in short-term sessions that can be found across many platforms.
Supervised Homogeneity Fusion: a Combinatorial Approach
Fusing regression coefficients into homogenous groups can unveil those coefficients that share a common value within each group.
Evolution of Popularity Bias: Empirical Study and Debiasing
and how to debias in this long-term dynamic process?
Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems
Conversational recommender systems (CRS) have shown great success in accurately capturing a user's current and detailed preference through the multi-round interaction cycle while effectively guiding users to a more personalized recommendation.
Towards Fair Conversational Recommender Systems
Conversational recommender systems have demonstrated great success.
Understanding Best Subset Selection: A Tale of Two C(omplex)ities
Furthermore, we show that a margin condition depending on similar margin quantity and complexity measures is also necessary for model consistency of BSS.
Evolution of Filter Bubbles and Polarization in News Recommendation
However, most existing work focuses on a static setting or over a short-time window, leaving open questions about the long-term and dynamic impacts of news recommendations.
Enhancing User Personalization in Conversational Recommenders
Conversational recommenders are emerging as a powerful tool to personalize a user's recommendation experience.
PromptAttack: Probing Dialogue State Trackers with Adversarial Prompts
A key component of modern conversational systems is the Dialogue State Tracker (or DST), which models a user's goals and needs.
A meta learning scheme for fast accent domain expansion in Mandarin speech recognition
We combine the methods of meta learning and freeze of model parameters, which makes the recognition performance more stable in different cases and the training faster about 20%.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Co$^2$PT: Mitigating Bias in Pre-trained Language Models through Counterfactual Contrastive Prompt Tuning
Pre-trained Language Models are widely used in many important real-world applications.
Unsupervised Candidate Answer Extraction through Differentiable Masker-Reconstructor Model
Question generation is a widely used data augmentation approach with extensive applications, and extracting qualified candidate answers from context passages is a critical step for most question generation systems.
Global Voices, Local Biases: Socio-Cultural Prejudices across Languages
Finally, we highlight the significance of these social biases and the new dimensions through an extensive comparison of embedding methods, reinforcing the need to address them in pursuit of more equitable language models.
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
Exploring the full spectrum of trade-offs between privacy, model utility, and runtime efficiency is critical for practical unlearning scenarios.
Countering Mainstream Bias via End-to-End Adaptive Local Learning
In this paper, we identify two root causes of this mainstream bias: (i) discrepancy modeling, whereby CF algorithms focus on modeling mainstream users while neglecting niche users with unique preferences; and (ii) unsynchronized learning, where niche users require more training epochs than mainstream users to reach peak performance.
