357 citations · 681 across the 28 of their papers we have counts for
28 papers
Representer Point Selection for Explaining Regularized High-dimensional Models
Che-Ping Tsai, Jiong Zhang, Eli Chien +3
We introduce a novel class of sample-based explanations we term high-dimensional representers, that can be used to explain the predictions of a regularized high-dimensional model i…
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation
Eli Chien, Jiong Zhang, Cho-Jui Hsieh +4
The eXtreme Multi-label Classification~(XMC) problem seeks to find relevant labels from an exceptionally large label space. Most of the existing XMC learners focus on the extractio…
Uncertainty in Extreme Multi-label Classification
Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhong +2
Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable machine learning models for decision making. However, most research in this domain ha…
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces
Nilesh Gupta, Patrick H. Chen, Hsiang-Fu Yu +2
Extreme multi-label classification (XMC) is a popular framework for solving many real-world problems that require accurate prediction from a very large number of potential output c…
FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search
Patrick H. Chen, Chang Wei-cheng, Yu Hsiang-fu +2
Approximate K-Nearest Neighbor Search (AKNNS) has now become ubiquitous in modern applications, for example, as a fast search procedure with two tower deep learning models. Graph-b…
Accelerating Primal-dual Methods for Regularized Markov Decision Processes
Haoya Li, Hsiang-fu Yu, Lexing Ying +1
Entropy regularized Markov decision processes have been widely used in reinforcement learning. This paper is concerned with the primal-dual formulation of the entropy regularized p…
Extreme Zero-Shot Learning for Extreme Text Classification
Yuanhao Xiong, Wei-Cheng Chang, Cho-Jui Hsieh +2
The eXtreme Multi-label text Classification (XMC) problem concerns finding most relevant labels for an input text instance from a large label set. However, the XMC setup faces two…
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh +4
Learning on graphs has attracted significant attention in the learning community due to numerous real-world applications. In particular, graph neural networks (GNNs), which take nu…
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification
Jiong Zhang, Wei-cheng Chang, Hsiang-fu Yu +1
Extreme multi-label text classification (XMC) seeks to find relevant labels from an extreme large label collection for a given text input. Many real-world applications can be formu…
Label Disentanglement in Partition-based Extreme Multilabel Classification
Xuanqing Liu, Wei-Cheng Chang, Hsiang-Fu Yu +2
Partition-based methods are increasingly-used in extreme multi-label classification (XMC) problems due to their scalability to large output spaces (e.g., millions or more). However…
Extreme Multi-label Learning for Semantic Matching in Product Search
Wei-Cheng Chang, Daniel Jiang, Hsiang-Fu Yu +9
We consider the problem of semantic matching in product search: given a customer query, retrieve all semantically related products from a huge catalog of size 100 million, or more.…
Enterprise-Scale Search: Accelerating Inference for Sparse Extreme Multi-Label Ranking Trees
Philip A. Etter, Kai Zhong, Hsiang-Fu Yu +2
Tree-based models underpin many modern semantic search engines and recommender systems due to their sub-linear inference times. In industrial applications, these models operate at…
PECOS: Prediction for Enormous and Correlated Output Spaces
Hsiang-Fu Yu, Kai Zhong, Jiong Zhang +2
Many large-scale applications amount to finding relevant results from an enormous output space of potential candidates. For example, finding the best matching product from a large…
Extreme Multi-label Classification from Aggregated Labels
Yanyao Shen, Hsiang-fu Yu, Sujay Sanghavi +1
Extreme multi-label classification (XMC) is the problem of finding the relevant labels for an input, from a very large universe of possible labels. We consider XMC in the setting w…
Learning to Encode Position for Transformer with Continuous Dynamical Model
Xuanqing Liu, Hsiang-Fu Yu, Inderjit Dhillon +1
We introduce a new way of learning to encode position information for non-recurrent models, such as Transformer models. Unlike RNN and LSTM, which contain inductive bias by loading…
Multiresolution Transformer Networks: Recurrence is Not Essential for Modeling Hierarchical Structure
Vikas K. Garg, Inderjit S. Dhillon, Hsiang-Fu Yu
The architecture of Transformer is based entirely on self-attention, and has been shown to outperform models that employ recurrence on sequence transduction tasks such as machine t…
Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering
Liwei Wu, Hsiang-Fu Yu, Nikhil Rao +2
In this paper, we consider recommender systems with side information in the form of graphs. Existing collaborative filtering algorithms mainly utilize only immediate neighborhood i…
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks
Jiong Zhang, Hsiang-fu Yu, Inderjit S. Dhillon
Deep neural networks have yielded superior performance in many applications; however, the gradient computation in a deep model with millions of instances lead to a lengthy training…
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting
Rajat Sen, Hsiang-Fu Yu, Inderjit Dhillon
Forecasting high-dimensional time series plays a crucial role in many applications such as demand forecasting and financial predictions. Modern datasets can have millions of correl…
Taming Pretrained Transformers for Extreme Multi-label Text Classification
Wei-Cheng Chang, Hsiang-Fu Yu, Kai Zhong +2
We consider the extreme multi-label text classification (XMC) problem: given an input text, return the most relevant labels from a large label collection. For example, the input te…
LearningWord Embeddings for Low-resource Languages by PU Learning
Chao Jiang, Hsiang-Fu Yu, Cho-Jui Hsieh +1
Word embedding is a key component in many downstream applications in processing natural languages. Existing approaches often assume the existence of a large collection of text for…
A Greedy Approach for Budgeted Maximum Inner Product Search
Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei +1
Maximum Inner Product Search (MIPS) is an important task in many machine learning applications such as the prediction phase of a low-rank matrix factorization model for a recommend…
Extreme Stochastic Variational Inference: Distributed and Asynchronous
Jiong Zhang, Parameswaran Raman, Shihao Ji +3
Stochastic variational inference (SVI), the state-of-the-art algorithm for scaling variational inference to large-datasets, is inherently serial. Moreover, it requires the paramete…
High-dimensional Time Series Prediction with Missing Values
Hsiang-Fu Yu, Nikhil Rao, Inderjit S. Dhillon
High-dimensional time series prediction is needed in applications as diverse as demand forecasting and climatology. Often, such applications require methods that are both highly sc…