Alex Beutel | DeepAI

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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...

0 Yueqi Wang, et al. ∙

∙ 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...

0 James Atwood, et al. ∙

∙ 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...

0 Xiao Ma, et al. ∙

∙ 04/17/2023

Towards Robust Prompts on Vision-Language Models

With the advent of vision-language models (VLMs) that can perform in-con...

0 Jindong Gu, et al. ∙

∙ 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...

0 Zhouxing Shi, et al. ∙

∙ 11/11/2022

Striving for data-model efficiency: Identifying data externalities on group performance

Building trustworthy, effective, and responsible machine learning system...

0 Esther Rolf, et al. ∙

∙ 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...

5 Meghana Deodhar, et al. ∙

∙ 06/28/2022

Flexible text generation for counterfactual fairness probing

A common approach for testing fairness issues in text-based classifiers ...

3 Zee Fryer, et al. ∙

∙ 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 ...

27 Yao Qin, et al. ∙

∙ 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...

16 Pranjal Awasthi, et al. ∙

∙ 11/06/2020

Underspecification Presents Challenges for Credibility in Modern Machine Learning

ML models often exhibit unexpectedly poor behavior when they are deploye...

30 Alexander D'Amour, et al. ∙

∙ 10/12/2020

Measuring and Reducing Gendered Correlations in Pre-trained Models

Pre-trained models have revolutionized natural language understanding. H...

1 Kellie Webster, et al. ∙

∙ 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...

0 Tianlu Wang, et al. ∙

∙ 11/05/2019

Practical Compositional Fairness: Understanding Fairness in Multi-Task ML Systems

Most literature in fairness has focused on improving fairness with respe...

23 Xuezhi Wang, et al. ∙

∙ 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...

39 Flavien Prost, et al. ∙

∙ 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...

4 Candice Schumann, et al. ∙

∙ 03/02/2019

Fairness in Recommendation Ranking through Pairwise Comparisons

Recommender systems are one of the most pervasive applications of machin...

14 Alex Beutel, et al. ∙

∙ 02/22/2019

Towards Neural Mixture Recommender for Long Range Dependent User Sequences

Understanding temporal dynamics has proved to be highly valuable for acc...

20 Jiaxi Tang, et al. ∙

∙ 01/14/2019

Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements

As more researchers have become aware of and passionate about algorithmi...

14 Alex Beutel, et al. ∙

∙ 12/06/2018

Top-K Off-Policy Correction for a REINFORCE Recommender System

Industrial recommender systems deal with extremely large action spaces -...

0 Minmin Chen, et al. ∙

∙ 09/27/2018

Counterfactual Fairness in Text Classification through Robustness

In this paper, we study counterfactual fairness in text classification, ...

8 Sahaj Garg, et al. ∙

∙ 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...

0 Tim Kraska, et al. ∙

∙ 12/06/2015

Explaining reviews and ratings with PACO: Poisson Additive Co-Clustering

Understanding a user's motivations provides valuable information beyond ...

0 Chao-Yuan Wu, et al. ∙

∙ 11/19/2015

BIRDNEST: Bayesian Inference for Ratings-Fraud Detection

Review fraud is a pervasive problem in online commerce, in which fraudul...

0 Bryan Hooi, et al. ∙

∙ 12/31/2014

ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly

Matrix completion and approximation are popular tools to capture a user'...

0 Alex Beutel, et al. ∙

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