Search Results for author: Xinyang Yi

Found 24 papers, 4 papers with code

Density Weighting for Multi-Interest Personalized Recommendation

no code implementations3 Aug 2023 Nikhil Mehta, Anima Singh, Xinyang Yi, Sagar Jain, Lichan Hong, Ed H. Chi

When the data distribution is highly skewed, the gains observed by learning multiple representations diminish since the model dominates on head items/interests, leading to poor performance on tail items.

Recommendation Systems

Better Generalization with Semantic IDs: A case study in Ranking for Recommendations

no code implementations13 Jun 2023 Anima Singh, Trung Vu, Raghunandan Keshavan, Nikhil Mehta, Xinyang Yi, Lichan Hong, Lukasz Heldt, Li Wei, Ed Chi, Maheswaran Sathiamoorthy

We showcase how we use them as a replacement of item IDs in a resource-constrained ranking model used in an industrial-scale video sharing platform.

Recommendation Systems

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

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

In this work, we aim to improve tail item recommendations while maintaining the overall performance with less training and serving cost.

Memorization Recommendation Systems +1

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

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

Self-supervised Learning for Large-scale Item Recommendations

1 code implementation25 Jul 2020 Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, Felix Yu, Ting Chen, Aditya Menon, Lichan Hong, Ed H. Chi, Steve Tjoa, Jieqi Kang, Evan Ettinger

Our online results also verify our hypothesis that our framework indeed improves model performance even more on slices that lack supervision.

Data Augmentation Natural Language Understanding +3

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

Recommending what video to watch next: a multitask ranking system

no code implementations RecSys 2019 Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, Ed Chi

In this paper, we introduce a large scale multi-objective ranking system for recommending what video to watch next on an industrial video sharing platform.

Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts

9 code implementations19 Jul 2018 Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, Ed Chi

In this work, we propose a novel multi-task learning approach, Multi-gate Mixture-of-Experts (MMoE), which explicitly learns to model task relationships from data.

Binary Classification Click-Through Rate Prediction +2

Solving a Mixture of Many Random Linear Equations by Tensor Decomposition and Alternating Minimization

no code implementations19 Aug 2016 Xinyang Yi, Constantine Caramanis, Sujay Sanghavi

We give a tractable algorithm for the mixed linear equation problem, and show that under some technical conditions, our algorithm is guaranteed to solve the problem exactly with sample complexity linear in the dimension, and polynomial in $k$, the number of components.

Tensor Decomposition

Fast Algorithms for Robust PCA via Gradient Descent

no code implementations NeurIPS 2016 Xinyang Yi, Dohyung Park, Yudong Chen, Constantine Caramanis

For the partially observed case, we show the complexity of our algorithm is no more than $\mathcal{O}(r^4d \log d \log(1/\varepsilon))$.

Matrix Completion

Regularized EM Algorithms: A Unified Framework and Statistical Guarantees

no code implementations NeurIPS 2015 Xinyang Yi, Constantine Caramanis

In particular, regularizing the M-step using the state-of-the-art high-dimensional prescriptions (e. g., Wainwright (2014)) is not guaranteed to provide this balance.


Optimal linear estimation under unknown nonlinear transform

no code implementations NeurIPS 2015 Xinyang Yi, Zhaoran Wang, Constantine Caramanis, Han Liu

This model is known as the single-index model in statistics, and, among other things, it represents a significant generalization of one-bit compressed sensing.

Binary Embedding: Fundamental Limits and Fast Algorithm

no code implementations19 Feb 2015 Xinyang Yi, Constantine Caramanis, Eric Price

Binary embedding is a nonlinear dimension reduction methodology where high dimensional data are embedded into the Hamming cube while preserving the structure of the original space.

Data Structures and Algorithms Information Theory Information Theory

A Convex Formulation for Mixed Regression with Two Components: Minimax Optimal Rates

no code implementations25 Dec 2013 Yudong Chen, Xinyang Yi, Constantine Caramanis

We consider the mixed regression problem with two components, under adversarial and stochastic noise.


Alternating Minimization for Mixed Linear Regression

no code implementations14 Oct 2013 Xinyang Yi, Constantine Caramanis, Sujay Sanghavi

Mixed linear regression involves the recovery of two (or more) unknown vectors from unlabeled linear measurements; that is, where each sample comes from exactly one of the vectors, but we do not know which one.


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