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08/23/2023
Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders
Sequential recommenders have been widely used in industry due to their s...
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07/11/2023
Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification
Despite the rich literature on machine learning fairness, relatively lit...
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06/25/2023
Let's Do a Thought Experiment: Using Counterfactuals to Improve Moral Reasoning
Language models still struggle on moral reasoning, despite their impress...
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04/17/2023
Towards Robust Prompts on Vision-Language Models
With the advent of vision-language models (VLMs) that can perform in-con...
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02/02/2023
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data
“Effective robustness” measures the extra out-of-distribution (OOD) robu...
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11/11/2022
Striving for data-model efficiency: Identifying data externalities on group performance
Building trustworthy, effective, and responsible machine learning system...
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10/18/2022
A Human-ML Collaboration Framework for Improving Video Content Reviews
We deal with the problem of localized in-video taxonomic human annotatio...
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06/28/2022
Flexible text generation for counterfactual fairness probing
A common approach for testing fairness issues in text-based classifiers ...
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10/15/2021
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation
We investigate the robustness of vision transformers (ViTs) through the ...
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02/16/2021
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information
Training and evaluation of fair classifiers is a challenging problem. Th...
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11/06/2020
Underspecification Presents Challenges for Credibility in Modern Machine Learning
ML models often exhibit unexpectedly poor behavior when they are deploye...
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10/12/2020
Measuring and Reducing Gendered Correlations in Pre-trained Models
Pre-trained models have revolutionized natural language understanding. H...
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10/05/2020
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation
NLP models are shown to suffer from robustness issues, i.e., a model's p...
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11/05/2019
Practical Compositional Fairness: Understanding Fairness in Multi-Task ML Systems
Most literature in fairness has focused on improving fairness with respe...
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10/25/2019
Toward a better trade-off between performance and fairness with kernel-based distribution matching
As recent literature has demonstrated how classifiers often carry uninte...
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06/24/2019
Transfer of Machine Learning Fairness across Domains
If our models are used in new or unexpected cases, do we know if they wi...
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03/02/2019
Fairness in Recommendation Ranking through Pairwise Comparisons
Recommender systems are one of the most pervasive applications of machin...
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02/22/2019
Towards Neural Mixture Recommender for Long Range Dependent User Sequences
Understanding temporal dynamics has proved to be highly valuable for acc...
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01/14/2019
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements
As more researchers have become aware of and passionate about algorithmi...
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12/06/2018
Top-K Off-Policy Correction for a REINFORCE Recommender System
Industrial recommender systems deal with extremely large action spaces -...
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09/27/2018
Counterfactual Fairness in Text Classification through Robustness
In this paper, we study counterfactual fairness in text classification, ...
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12/04/2017
The Case for Learned Index Structures
Indexes are models: a B-Tree-Index can be seen as a model to map a key t...
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12/06/2015
Explaining reviews and ratings with PACO: Poisson Additive Co-Clustering
Understanding a user's motivations provides valuable information beyond ...
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11/19/2015
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection
Review fraud is a pervasive problem in online commerce, in which fraudul...
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12/31/2014