Search Results for author: Ed H. Chi

Found 50 papers, 8 papers with code

Large Language Models for User Interest Journeys

no code implementations24 May 2023 Konstantina Christakopoulou, Alberto Lalama, Cj Adams, Iris Qu, Yifat Amir, Samer Chucri, Pierce Vollucci, Fabio Soldo, Dina Bseiso, Sarah Scodel, Lucas Dixon, Ed H. Chi, Minmin Chen

We argue, and demonstrate through extensive experiments, that LLMs as foundation models can reason through user activities, and describe their interests in nuanced and interesting ways, similar to how a human would.

Natural Language Understanding Recommendation Systems

Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals

no code implementations22 May 2023 Ananth Balashankar, Xuezhi Wang, Yao Qin, Ben Packer, Nithum Thain, Jilin Chen, Ed H. Chi, Alex Beutel

We demonstrate that with a small amount of human-annotated counterfactual data (10%), we can generate a counterfactual augmentation dataset with learned labels, that provides an 18-20% improvement in robustness and a 14-21% reduction in errors on 6 out-of-domain datasets, comparable to that of a fully human-annotated counterfactual dataset for both sentiment classification and question paraphrase tasks.

Data Augmentation Sentiment Analysis +1

Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems

no code implementations20 May 2023 Benjamin Coleman, Wang-Cheng Kang, Matthew Fahrbach, Ruoxi Wang, Lichan Hong, Ed H. Chi, Derek Zhiyuan Cheng

Learning high-quality feature embeddings efficiently and effectively is critical for the performance of web-scale machine learning systems.

Recommendation Systems

Recommender Systems with Generative Retrieval

no code implementations8 May 2023 Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan H. Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q. Tran, Jonah Samost, Maciej Kula, Ed H. Chi, Maheswaran Sathiamoorthy

Once we have the Semantic IDs for all the items, a Transformer based sequence-to-sequence model is trained to predict the Semantic ID of the next item.

Recommendation Systems Retrieval

What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel

no code implementations22 Feb 2023 Yao Qin, Xuezhi Wang, Balaji Lakshminarayanan, Ed H. Chi, Alex Beutel

A wide breadth of research has devised data augmentation approaches that can improve both accuracy and generalization performance for neural networks.

Data Augmentation

Latent User Intent Modeling for Sequential Recommenders

no code implementations17 Nov 2022 Bo Chang, Alexandros Karatzoglou, Yuyan Wang, Can Xu, Ed H. Chi, Minmin Chen

We demonstrate the effectiveness of the latent user intent modeling via offline analyses as well as live experiments on a large-scale industrial recommendation platform.

Recommendation Systems

Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)

no code implementations25 Oct 2022 Yin Zhang, Ruoxi Wang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Lichan Hong, James Caverlee, Ed H. Chi

Most existing methods mainly attempt to reduce the bias from the prior perspective, which ignores the discrepancy in the conditional probability.

Memorization Transfer Learning

Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

1 code implementation17 Oct 2022 Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V. Le, Ed H. Chi, Denny Zhou, Jason Wei

BIG-Bench (Srivastava et al., 2022) is a diverse evaluation suite that focuses on tasks believed to be beyond the capabilities of current language models.

Language Modelling

Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations

no code implementations14 Oct 2022 Flavien Prost, Ben Packer, Jilin Chen, Li Wei, Pierre Kremp, Nicholas Blumm, Susan Wang, Tulsee Doshi, Tonia Osadebe, Lukasz Heldt, Ed H. Chi, Alex Beutel

We reconcile these notions and show that the tension is due to differences in distributions of users where items are relevant, and break down the important factors of the user's recommendations.

Fairness Recommendation Systems

Reward Shaping for User Satisfaction in a REINFORCE Recommender

no code implementations30 Sep 2022 Konstantina Christakopoulou, Can Xu, Sai Zhang, Sriraj Badam, Trevor Potter, Daniel Li, Hao Wan, Xinyang Yi, Ya Le, Chris Berg, Eric Bencomo Dixon, Ed H. Chi, Minmin Chen

How might we design Reinforcement Learning (RL)-based recommenders that encourage aligning user trajectories with the underlying user satisfaction?

Imputation Reinforcement Learning (RL)

Improving Multi-Task Generalization via Regularizing Spurious Correlation

no code implementations19 May 2022 Ziniu Hu, Zhe Zhao, Xinyang Yi, Tiansheng Yao, Lichan Hong, Yizhou Sun, Ed H. Chi

First, the risk of having non-causal knowledge is higher, as the shared MTL model needs to encode all knowledge from different tasks, and causal knowledge for one task could be potentially spurious to the other.

Multi-Task Learning Representation Learning

Nonlinear Initialization Methods for Low-Rank Neural Networks

no code implementations2 Feb 2022 Kiran Vodrahalli, Rakesh Shivanna, Maheswaran Sathiamoorthy, Sagar Jain, Ed H. Chi

We propose a novel low-rank initialization framework for training low-rank deep neural networks -- networks where the weight parameters are re-parameterized by products of two low-rank matrices.

Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning

no code implementations4 Jun 2021 Yuyan Wang, Xuezhi Wang, Alex Beutel, Flavien Prost, Jilin Chen, Ed H. Chi

This presents a multi-dimensional Pareto frontier on (1) the trade-off between group fairness and accuracy with respect to each task, as well as (2) the trade-offs across multiple tasks.

Fairness Multi-Task Learning

Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities

no code implementations6 May 2021 Ruohan Zhan, Konstantina Christakopoulou, Ya Le, Jayden Ooi, Martin Mladenov, Alex Beutel, Craig Boutilier, Ed H. Chi, Minmin Chen

We then build a REINFORCE recommender agent, coined EcoAgent, to optimize a joint objective of user utility and the counterfactual utility lift of the provider associated with the recommended content, which we show to be equivalent to maximizing overall user utility and the utilities of all providers on the platform under some mild assumptions.

Recommendation Systems

Measuring Recommender System Effects with Simulated Users

no code implementations12 Jan 2021 Sirui Yao, Yoni Halpern, Nithum Thain, Xuezhi Wang, Kang Lee, Flavien Prost, Ed H. Chi, Jilin Chen, Alex Beutel

Using this simulation framework, we can (a) isolate the effect of the recommender system from the user preferences, and (b) examine how the system performs not just on average for an "average user" but also the extreme experiences under atypical user behavior.

Collaborative Filtering Recommendation Systems

Learned Indexes for a Google-scale Disk-based Database

no code implementations23 Dec 2020 Hussam Abu-Libdeh, Deniz Altınbüken, Alex Beutel, Ed H. Chi, Lyric Doshi, Tim Kraska, Xiaozhou, Li, Andy Ly, Christopher Olston

There is great excitement about learned index structures, but understandable skepticism about the practicality of a new method uprooting decades of research on B-Trees.

Learning to Embed Categorical Features without Embedding Tables for Recommendation

no code implementations21 Oct 2020 Wang-Cheng Kang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Ting Chen, Lichan Hong, Ed H. Chi

Embedding learning of categorical features (e. g. user/item IDs) is at the core of various recommendation models including matrix factorization and neural collaborative filtering.

Collaborative Filtering Natural Language Understanding +2

Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems

no code implementations17 Aug 2020 Zhe Chen, Yuyan Wang, Dong Lin, Derek Zhiyuan Cheng, Lichan Hong, Ed H. Chi, Claire Cui

Despite deep neural network (DNN)'s impressive prediction performance in various domains, it is well known now that a set of DNN models trained with the same model specification and the same data can produce very different prediction results.

Model-based Reinforcement Learning Recommendation Systems

Small Towers Make Big Differences

no code implementations13 Aug 2020 Yuyan Wang, Zhe Zhao, Bo Dai, Christopher Fifty, Dong Lin, Lichan Hong, Ed H. Chi

A delicate balance between multi-task generalization and multi-objective optimization is therefore needed for finding a better trade-off between efficiency and generalization.

Multi-Task Learning

Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval

no code implementations7 Aug 2020 Tao Wu, Ellie Ka-In Chio, Heng-Tze Cheng, Yu Du, Steffen Rendle, Dima Kuzmin, Ritesh Agarwal, Li Zhang, John Anderson, Sarvjeet Singh, Tushar Chandra, Ed H. Chi, Wen Li, Ankit Kumar, Xiang Ma, Alex Soares, Nitin Jindal, Pei Cao

In light of these problems, we observed that most online content platforms have both a search and a recommender system that, while having heterogeneous input spaces, can be connected through their common output item space and a shared semantic representation.

Information Retrieval Recommendation Systems +2

Improving Calibration through the Relationship with Adversarial Robustness

no code implementations NeurIPS 2021 Yao Qin, Xuezhi Wang, Alex Beutel, Ed H. Chi

To this end, we propose Adversarial Robustness based Adaptive Label Smoothing (AR-AdaLS) that integrates the correlations of adversarial robustness and calibration into training by adaptively softening labels for an example based on how easily it can be attacked by an adversary.

Adversarial Robustness

Fairness without Demographics through Adversarially Reweighted Learning

4 code implementations NeurIPS 2020 Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed H. Chi

Much of the previous machine learning (ML) fairness literature assumes that protected features such as race and sex are present in the dataset, and relies upon them to mitigate fairness concerns.


Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model

no code implementations9 Jun 2020 Jiaqi Ma, Xinyang Yi, Weijing Tang, Zhe Zhao, Lichan Hong, Ed H. Chi, Qiaozhu Mei

We investigate the Plackett-Luce (PL) model based listwise learning-to-rank (LTR) on data with partitioned preference, where a set of items are sliced into ordered and disjoint partitions, but the ranking of items within a partition is unknown.

Extreme Multi-Label Classification Learning-To-Rank +1

Developing a Recommendation Benchmark for MLPerf Training and Inference

no code implementations16 Mar 2020 Carole-Jean Wu, Robin Burke, Ed H. Chi, Joseph Konstan, Julian McAuley, Yves Raimond, Hao Zhang

Deep learning-based recommendation models are used pervasively and broadly, for example, to recommend movies, products, or other information most relevant to users, in order to enhance the user experience.

Image Classification object-detection +3

Understanding and Improving Knowledge Distillation

no code implementations10 Feb 2020 Jiaxi Tang, Rakesh Shivanna, Zhe Zhao, Dong Lin, Anima Singh, Ed H. Chi, Sagar Jain

Knowledge Distillation (KD) is a model-agnostic technique to improve model quality while having a fixed capacity budget.

Knowledge Distillation Model Compression

Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems

no code implementations5 Nov 2019 Xuezhi Wang, Nithum Thain, Anu Sinha, Flavien Prost, Ed H. Chi, Jilin Chen, Alex Beutel

In addition to the theoretical results, we find on multiple datasets -- including a large-scale real-world recommender system -- that the overall system's end-to-end fairness is largely achievable by improving fairness in individual components.

Fairness Recommendation Systems

Toward a better trade-off between performance and fairness with kernel-based distribution matching

no code implementations25 Oct 2019 Flavien Prost, Hai Qian, Qiuwen Chen, Ed H. Chi, Jilin Chen, Alex Beutel

As recent literature has demonstrated how classifiers often carry unintended biases toward some subgroups, deploying machine learned models to users demands careful consideration of the social consequences.


The Dynamics of Signal Propagation in Gated Recurrent Neural Networks

no code implementations25 Sep 2019 Dar Gilboa, Bo Chang, Minmin Chen, Greg Yang, Samuel S. Schoenholz, Ed H. Chi, Jeffrey Pennington

We demonstrate the efficacy of our initialization scheme on multiple sequence tasks, on which it enables successful training while a standard initialization either fails completely or is orders of magnitude slower.

Transfer of Machine Learning Fairness across Domains

no code implementations24 Jun 2019 Candice Schumann, Xuezhi Wang, Alex Beutel, Jilin Chen, Hai Qian, Ed H. Chi

A model trained for one setting may be picked up and used in many others, particularly as is common with pre-training and cloud APIs.

BIG-bench Machine Learning Domain Adaptation +1

Quantifying Long Range Dependence in Language and User Behavior to improve RNNs

no code implementations23 May 2019 Francois Belletti, Minmin Chen, Ed H. Chi

Characterizing temporal dependence patterns is a critical step in understanding the statistical properties of sequential data.

Language Modelling Sequential Recommendation +1

Fairness in Recommendation Ranking through Pairwise Comparisons

no code implementations2 Mar 2019 Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow

Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information.

Fairness Recommendation Systems

Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs

no code implementations25 Jan 2019 Dar Gilboa, Bo Chang, Minmin Chen, Greg Yang, Samuel S. Schoenholz, Ed H. Chi, Jeffrey Pennington

We demonstrate the efficacy of our initialization scheme on multiple sequence tasks, on which it enables successful training while a standard initialization either fails completely or is orders of magnitude slower.

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

no code implementations14 Jan 2019 Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Allison Woodruff, Christine Luu, Pierre Kreitmann, Jonathan Bischof, Ed H. Chi

In this paper we provide a case-study on the application of fairness in machine learning research to a production classification system, and offer new insights in how to measure and address algorithmic fairness issues.

BIG-bench Machine Learning Fairness

Counterfactual Fairness in Text Classification through Robustness

no code implementations27 Sep 2018 Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H. Chi, Alex Beutel

In this paper, we study counterfactual fairness in text classification, which asks the question: How would the prediction change if the sensitive attribute referenced in the example were different?

Fairness General Classification +2

The Case for Learned Index Structures

7 code implementations4 Dec 2017 Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, Neoklis Polyzotis

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not.


Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations

no code implementations1 Jul 2017 Alex Beutel, Jilin Chen, Zhe Zhao, Ed H. Chi

How can we learn a classifier that is "fair" for a protected or sensitive group, when we do not know if the input to the classifier belongs to the protected group?

Fairness Recommendation Systems

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