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Hsiang-Fu Yu

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20 papers

2 author rows

ICML Conference 2023 Conference Paper

PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation

Eli Chien
Jiong Zhang 0001
Cho-Jui Hsieh
Jyun-Yu Jiang
Wei-Cheng Chang
Olgica Milenkovic
Hsiang-Fu Yu

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 extraction of semantic features from input query text. However, conventional XMC studies usually neglect the side information of instances and labels, which can be of use in many real-world applications such as recommendation systems and e-commerce product search. We propose Predicted Instance Neighborhood Aggregation (PINA), a data augmentation method for the general XMC problem that leverages beneficial side information. Unlike most existing XMC frameworks that treat labels and input instances as featureless indicators and independent entries, PINA extracts information from the label metadata and the correlations among training instances. Extensive experimental results demonstrate the consistent gain of PINA on various XMC tasks compared to the state-of-the-art methods: PINA offers a gain in accuracy compared to standard XR-Transformers on five public benchmark datasets. Moreover, PINA achieves a $\sim 5$% gain in accuracy on the largest dataset LF-AmazonTitles-1. 3M.

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ICML Conference 2023 Conference Paper

Representer Point Selection for Explaining Regularized High-dimensional Models

Che-Ping Tsai
Jiong Zhang 0001
Hsiang-Fu Yu
Eli Chien
Cho-Jui Hsieh
Pradeep Ravikumar

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 in terms of importance weights for each of the training samples. Our workhorse is a novel representer theorem for general regularized high-dimensional models, which decomposes the model prediction in terms of contributions from each of the training samples: with positive (negative) values corresponding to positive (negative) impact training samples to the model’s prediction. We derive consequences for the canonical instances of $\ell_1$ regularized sparse models and nuclear norm regularized low-rank models. As a case study, we further investigate the application of low-rank models in the context of collaborative filtering, where we instantiate high-dimensional representers for specific popular classes of models. Finally, we study the empirical performance of our proposed methods on three real-world binary classification datasets and two recommender system datasets. We also showcase the utility of high-dimensional representers in explaining model recommendations.

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NeurIPS Conference 2022 Conference Paper

ELIAS: End-to-End Learning to Index and Search in Large Output Spaces

Nilesh Gupta
Patrick Chen
Hsiang-Fu Yu
Cho-Jui Hsieh
Inderjit Dhillon

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 choices. A popular approach for dealing with the large label space is to arrange the labels into a shallow tree-based index and then learn an ML model to efficiently search this index via beam search. Existing methods initialize the tree index by clustering the label space into a few mutually exclusive clusters based on pre-defined features and keep it fixed throughout the training procedure. This approach results in a sub-optimal indexing structure over the label space and limits the search performance to the quality of choices made during the initialization of the index. In this paper, we propose a novel method ELIAS which relaxes the tree-based index to a specialized weighted graph-based index which is learned end-to-end with the final task objective. More specifically, ELIAS models the discrete cluster-to-label assignments in the existing tree-based index as soft learnable parameters that are learned jointly with the rest of the ML model. ELIAS achieves state-of-the-art performance on several large-scale extreme classification benchmarks with millions of labels. In particular, ELIAS can be up to 2. 5% better at precision@$1$ and up to 4% better at recall@$100$ than existing XMC methods. A PyTorch implementation of ELIAS along with other resources is available at https: //github. com/nilesh2797/ELIAS.

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ICLR Conference 2022 Conference Paper

Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction

Eli Chien
Wei-Cheng Chang
Cho-Jui Hsieh
Hsiang-Fu Yu
Jiong Zhang 0001
Olgica Milenkovic
Inderjit S. Dhillon

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 \emph{numerical} node features and graph structure as inputs, have been shown to achieve state-of-the-art performance on various graph-related learning tasks. Recent works exploring the correlation between numerical node features and graph structure via self-supervised learning have paved the way for further performance improvements of GNNs. However, methods used for extracting numerical node features from \emph{raw data} are still \emph{graph-agnostic} within standard GNN pipelines. This practice is sub-optimal as it prevents one from fully utilizing potential correlations between graph topology and node attributes. To mitigate this issue, we propose a new self-supervised learning framework, Graph Information Aided Node feature exTraction (GIANT). GIANT makes use of the eXtreme Multi-label Classification (XMC) formalism, which is crucial for fine-tuning the language model based on graph information, and scales to large datasets. We also provide a theoretical analysis that justifies the use of XMC over link prediction and motivates integrating XR-Transformers, a powerful method for solving XMC problems, into the GIANT framework. We demonstrate the superior performance of GIANT over the standard GNN pipeline on Open Graph Benchmark datasets: For example, we improve the accuracy of the top-ranked method GAMLP from $68.25\%$ to $69.67\%$, SGC from $63.29\%$ to $66.10\%$ and MLP from $47.24\%$ to $61.10\%$ on the ogbn-papers100M dataset by leveraging GIANT.

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JMLR Journal 2022 Journal Article

PECOS: Prediction for Enormous and Correlated Output Spaces

Hsiang-Fu Yu
Kai Zhong
Jiong Zhang
Wei-Cheng Chang
Inderjit S. Dhillon

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 catalog or suggesting related search phrases on a search engine. The size of the output space for these problems can range from millions to billions, and can even be infinite in some applications. Moreover, training data is often limited for the “long-tail” items in the output space. Fortunately, items in the output space are often correlated thereby presenting an opportunity to alleviate the data sparsity issue. In this paper, we propose the Prediction for Enormous and Correlated Output Spaces (PECOS) framework, a versatile and modular machine learning framework for solving prediction problems for very large output spaces, and apply it to the eXtreme Multilabel Ranking (XMR) problem: given an input instance, find and rank the most relevant items from an enormous but fixed and finite output space. We propose a three phase framework for PECOS: (i) in the first phase, PECOS organizes the output space using a semantic indexing scheme, (ii) in the second phase, PECOS uses the indexing to narrow down the output space by orders of magnitude using a machine learned matching scheme, and (iii) in the third phase, PECOS ranks the matched items using a final ranking scheme. The versatility and modularity of PECOS allows for easy plug-and-play of various choices for the indexing, matching, and ranking phases. The indexing and matching phases alleviate the data sparsity issue by leveraging correlations across different items in the output space. For the critical matching phase, we develop a recursive machine learned matching strategy with both linear and neural matchers. When applied to eXtreme Multilabel Ranking where the input instances are in textual form, we find that the recursive Transformer matcher gives state-of-the-art accuracy results, at the cost of two orders of magnitude increased training time compared to the recursive linear matcher. For example, on a dataset where the output space is of size 2.8 million, the recursive Transformer matcher results in a 6% increase in precision@1 (from 48.6% to 54.2%) over the recursive linear matcher but takes 100x more time to train. Thus it is up to the practitioner to evaluate the trade-offs and decide whether the increased training time and infrastructure cost is warranted for their application; indeed, the flexibility of the PECOS framework seamlessly allows different strategies to be used. We also develop very fast inference procedures which allow us to perform XMR predictions in real time; for example, inference takes less than 1 millisecond per input on the dataset with 2.8 million labels. The PECOS software is available at https://libpecos.org. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2022. ( edit, beta )

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NeurIPS Conference 2021 Conference Paper

DRONE: Data-aware Low-rank Compression for Large NLP Models

Patrick Chen
Hsiang-Fu Yu
Inderjit Dhillon
Cho-Jui Hsieh

The representations learned by large-scale NLP models such as BERT have been widely used in various tasks. However, the increasing model size of the pre-trained models also brings efficiency challenges, including inference speed and model size when deploying models on mobile devices. Specifically, most operations in BERT consist of matrix multiplications. These matrices are not low-rank and thus canonical matrix decomposition could not find an efficient approximation. In this paper, we observe that the learned representation of each layer lies in a low-dimensional space. Based on this observation, we propose DRONE (data-aware low-rank compression), a provably optimal low-rank decomposition of weight matrices, which has a simple closed form solution that can be efficiently computed. DRONE can be applied to both fully connected and self-attention layers appearing in the BERT model. In addition to compressing standard models, out method can also be used on distilled BERT models to further improve compression rate. Experimental results show that DRONE is able to improve both model size and inference speed with limited loss in accuracy. Specifically, DRONE alone achieves 1. 92x speedup on the MRPC task with only 1. 5% loss in accuracy, and when DRONE is combined with distillation, it further achieves over 12. 3x speedup on various natural language inference tasks.

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NeurIPS Conference 2021 Conference Paper

Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification

Jiong Zhang
Wei-Cheng Chang
Hsiang-Fu Yu
Inderjit Dhillon

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 formulated as XMC problems, such as recommendation systems, document tagging and semantic search. Recently, transformer based XMC methods, such as X-Transformer and LightXML, have shown significant improvement over other XMC methods. Despite leveraging pre-trained transformer models for text representation, the fine-tuning procedure of transformer models on large label space still has lengthy computational time even with powerful GPUs. In this paper, we propose a novel recursive approach, XR-Transformer to accelerate the procedure through recursively fine-tuning transformer models on a series of multi-resolution objectives related to the original XMC objective function. Empirical results show that XR-Transformer takes significantly less training time compared to other transformer-based XMC models while yielding better state-of-the-art results. In particular, on the public Amazon-3M dataset with 3 million labels, XR-Transformer is not only 20x faster than X-Transformer but also improves the Precision@1 from 51% to 54%.

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NeurIPS Conference 2021 Conference Paper

Label Disentanglement in Partition-based Extreme Multilabel Classification

Xuanqing Liu
Wei-Cheng Chang
Hsiang-Fu Yu
Cho-Jui Hsieh
Inderjit Dhillon

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, existing methods partition the large label space into mutually exclusive clusters, which is sub-optimal when labels have multi-modality and rich semantics. For instance, the label “Apple” can be the fruit or the brand name, which leads to the following research question: can we disentangle these multi-modal labels with non-exclusive clustering tailored for downstream XMC tasks? In this paper, we show that the label assignment problem in partition-based XMC can be formulated as an optimization problem, with the objective of maximizing precision rates. This leads to an efficient algorithm to form flexible and overlapped label clusters, and a method that can alternatively optimizes the cluster assignments and the model parameters for partition-based XMC. Experimental results on synthetic and real datasets show that our method can successfully disentangle multi-modal labels, leading to state-of-the-art (SOTA) results on four XMC benchmarks.

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ICML Conference 2020 Conference Paper

Extreme Multi-label Classification from Aggregated Labels

Yanyao Shen
Hsiang-Fu Yu
Sujay Sanghavi
Inderjit S. Dhillon

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 where labels are available only for groups of samples - but not for individual ones. Current XMC approaches are not built for such multi-instance multi-label (MIML) training data, and MIML approaches do not scale to XMC sizes. We develop a new and scalable algorithm to impute individual-sample labels from the group labels; this can be paired with any existing XMC method to solve the aggregated label problem. We characterize the statistical properties of our algorithm under mild assumptions, and provide a new end-to-end framework for MIML as an extension. Experiments on both aggregated label XMC and MIML tasks show the advantages over existing approaches.

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ICML Conference 2020 Conference Paper

Learning to Encode Position for Transformer with Continuous Dynamical Model

Xuanqing Liu
Hsiang-Fu Yu
Inderjit S. Dhillon
Cho-Jui Hsieh

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 the input tokens sequentially, non-recurrent models are less sensitive to position. The main reason is that position information among input units is not encoded inherently, i. e. , they are permutation equivalent, this problem justifies why all of the existing models are accompanied by position encoding/embedding layer at the input. However, this solution has clear limitations: the sinusoidal position encoding is not flexible enough as it is manually designed and does not contain any learnable parameters, whereas the position embedding restricts the maximum length of input sequences. It is thus desirable to design a new position layer that contains learnable parameters to adjust to different datasets and different architectures. At the same time, we would also like it to extrapolate in accordance with the variable length of inputs. In our proposed solution, we borrow from the recent Neural ODE approach, which may be viewed as a versatile continuous version of a ResNet. This model is capable of modeling many kinds of dynamical systems. We model the evolution of encoded results along position index by such a dynamical system, thereby overcoming the above limitations of existing methods. We evaluate our new position layers on a variety of neural machine translation and language understanding tasks, the experimental results show consistent improvements over the baselines.

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NeurIPS Conference 2019 Conference Paper

AutoAssist: A Framework to Accelerate Training of Deep Neural Networks

Jiong Zhang
Hsiang-Fu Yu
Inderjit Dhillon

Deep neural networks have yielded superior performance in many contemporary applications. However, the gradient computation in a deep model with millions of instances leads to a lengthy training process even with modern GPU/TPU hardware acceleration. In this paper, we propose AutoAssist, a simple framework to accelerate training of a deep neural network. Typically, as the training procedure evolves, the amount of improvement by a stochastic gradient update varies dynamically with the choice of instances in the mini-batch. In AutoAssist, we utilize this fact and design an instance shrinking operation that is used to filter out instances with relatively low marginal improvement to the current model; thus the computationally intensive gradient computations are performed on informative instances as much as possible. Specifically, we train a very lightweight Assistant model jointly with the original deep network, which we refer to as Boss. The Assistant model is designed to gauge the importance of a given instance with respect to the current Boss such that the shrinking operation can be applied in the batch generator. With careful design, we train the Boss and Assistant in a nonblocking and asynchronous fashion such that overhead is minimal. To demonstrate the effectiveness of AutoAssist, we conduct experiments on two contemporary applications: image classification using ResNets with varied number of layers, and neural machine translation using LSTMs, ConvS2S and Transformer models. For each application, we verify that AutoAssist leads to significant reduction in training time; in particular, 30% to 40% of the total operation count can be reduced which leads to faster convergence and a corresponding decrease in training time.

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NeurIPS Conference 2019 Conference Paper

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 correlated time-series that evolve together, i. e they are extremely high dimensional (one dimension for each individual time-series). There is a need for exploiting global patterns and coupling them with local calibration for better prediction. However, most recent deep learning approaches in the literature are one-dimensional, i. e, even though they are trained on the whole dataset, during prediction, the future forecast for a single dimension mainly depends on past values from the same dimension. In this paper, we seek to correct this deficiency and propose DeepGLO, a deep forecasting model which thinks globally and acts locally. In particular, DeepGLO is a hybrid model that combines a global matrix factorization model regularized by a temporal convolution network, along with another temporal network that can capture local properties of each time-series and associated covariates. Our model can be trained effectively on high-dimensional but diverse time series, where different time series can have vastly different scales, without a priori normalization or rescaling. Empirical results demonstrate that DeepGLO can outperform state-of-the-art approaches; for example, we see more than 25% improvement in WAPE over other methods on a public dataset that contains more than 100K-dimensional time series.

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NeurIPS Conference 2017 Conference Paper

A Greedy Approach for Budgeted Maximum Inner Product Search

Hsiang-Fu Yu
Cho-Jui Hsieh
Qi Lei
Inderjit Dhillon

Maximum Inner Product Search (MIPS) is an important task in many machine learning applications such as the prediction phase of low-rank matrix factorization models and deep learning models. Recently, there has been substantial research on how to perform MIPS in sub-linear time, but most of the existing work does not have the flexibility to control the trade-off between search efficiency and search quality. In this paper, we study the important problem of MIPS with a computational budget. By carefully studying the problem structure of MIPS, we develop a novel Greedy-MIPS algorithm, which can handle budgeted MIPS by design. While simple and intuitive, Greedy-MIPS yields surprisingly superior performance compared to state-of-the-art approaches. As a specific example, on a candidate set containing half a million vectors of dimension 200, Greedy-MIPS runs 200x faster than the naive approach while yielding search results with the top-5 precision greater than 75%.

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AAAI Conference 2017 Conference Paper

A Unified Algorithm for One-Cass Structured Matrix Factorization with Side Information

Hsiang-Fu Yu
Hsin-Yuan Huang
Inderjit Dhillon
Chih-Jen Lin

In many applications such as recommender systems and multi-label learning the task is to complete a partially observed binary matrix. Such PU learning (positive-unlabeled) problems can be solved by one-class matrix factorization (MF). In practice side information such as user or item features in recommender systems are often available besides the observed positive user-item connections. In this work we consider a generalization of one-class MF so that two types of side information are incorporated and a general convex loss function can be used. The resulting optimization problem is very challenging, but we derive an efﬁcient and effective alternating minimization procedure. Experiments on largescale multi-label learning and one-class recommender systems demonstrate the effectiveness of our proposed approach.

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NeurIPS Conference 2016 Conference Paper

Asynchronous Parallel Greedy Coordinate Descent

Yang You
Xiangru Lian
Ji Liu
Hsiang-Fu Yu
Inderjit Dhillon
James Demmel
Cho-Jui Hsieh

n this paper, we propose and study an Asynchronous parallel Greedy Coordinate Descent (Asy-GCD) algorithm for minimizing a smooth function with bounded constraints. At each iteration, workers asynchronously conduct greedy coordinate descent updates on a block of variables. In the first part of the paper, we analyze the theoretical behavior of Asy-GCD and prove a linear convergence rate. In the second part, we develop an efficient kernel SVM solver based on Asy-GCD in the shared memory multi-core setting. Since our algorithm is fully asynchronous---each core does not need to idle and wait for the other cores---the resulting algorithm enjoys good speedup and outperforms existing multi-core kernel SVM solvers including asynchronous stochastic coordinate descent and multi-core LIBSVM.

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NeurIPS Conference 2016 Conference Paper

Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction

Hsiang-Fu Yu
Nikhil Rao
Inderjit Dhillon

Time series prediction problems are becoming increasingly high-dimensional in modern applications, such as climatology and demand forecasting. For example, in the latter problem, the number of items for which demand needs to be forecast might be as large as 50, 000. In addition, the data is generally noisy and full of missing values. Thus, modern applications require methods that are highly scalable, and can deal with noisy data in terms of corruptions or missing values. However, classical time series methods usually fall short of handling these issues. In this paper, we present a temporal regularized matrix factorization (TRMF) framework which supports data-driven temporal learning and forecasting. We develop novel regularization schemes and use scalable matrix factorization methods that are eminently suited for high-dimensional time series data that has many missing values. Our proposed TRMF is highly general, and subsumes many existing approaches for time series analysis. We make interesting connections to graph regularization methods in the context of learning the dependencies in an autoregressive framework. Experimental results show the superiority of TRMF in terms of scalability and prediction quality. In particular, TRMF is two orders of magnitude faster than other methods on a problem of dimension 50, 000, and generates better forecasts on real-world datasets such as Wal-mart E-commerce datasets.

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NeurIPS Conference 2015 Conference Paper

Collaborative Filtering with Graph Information: Consistency and Scalable Methods

Nikhil Rao
Hsiang-Fu Yu
Pradeep Ravikumar
Inderjit Dhillon

Low rank matrix completion plays a fundamental role in collaborative filtering applications, the key idea being that the variables lie in a smaller subspace than the ambient space. Often, additional information about the variables is known, and it is reasonable to assume that incorporating this information will lead to better predictions. We tackle the problem of matrix completion when pairwise relationships among variables are known, via a graph. We formulate and derive a highly efficient, conjugate gradient based alternating minimization scheme that solves optimizations with over 55 million observations up to 2 orders of magnitude faster than state-of-the-art (stochastic) gradient-descent based methods. On the theoretical front, we show that such methods generalize weighted nuclear norm formulations, and derive statistical consistency guarantees. We validate our results on both real and synthetic datasets.

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ICML Conference 2015 Conference Paper

PASSCoDe: Parallel ASynchronous Stochastic dual Co-ordinate Descent

Cho-Jui Hsieh
Hsiang-Fu Yu
Inderjit S. Dhillon

Stochastic Dual Coordinate Descent (DCD) is one of the most efficient ways to solve the family of L2-regularized empirical risk minimization problems, including linear SVM, logistic regression, and many others. The vanilla implementation of DCD is quite slow; however, by maintaining primal variables while updating dual variables, the time complexity of DCD can be significantly reduced. Such a strategy forms the core algorithm in the widely-used LIBLINEAR package. In this paper, we parallelize the DCD algorithms in LIBLINEAR. In recent research, several synchronized parallel DCD algorithms have been proposed, however, they fail to achieve good speedup in the shared memory multi-core setting. In this paper, we propose a family of parallel asynchronous stochastic dual coordinate descent algorithms (PASSCoDe). Each thread repeatedly selects a random dual variable and conducts coordinate updates using the primal variables that are stored in the shared memory. We analyze the convergence properties of DCD when different locking/atomic mechanisms are applied. For implementation with atomic operations, we show linear convergence under mild conditions. For implementation without any atomic operations or locking, we present a novel error analysis for PASSCoDe under the multi-core environment, showing that the converged solution is the exact solution for a primal problem with a perturbed regularizer. Experimental results show that our methods are much faster than previous parallel coordinate descent solvers.

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ICML Conference 2014 Conference Paper

Large-scale Multi-label Learning with Missing Labels

Hsiang-Fu Yu
Prateek Jain 0002
Purushottam Kar
Inderjit S. Dhillon

The multi-label classification problem has generated significant interest in recent years. However, existing approaches do not adequately address two key challenges: (a) scaling up to problems with a large number (say millions) of labels, and (b) handling data with missing labels. In this paper, we directly address both these problems by studying the multi-label problem in a generic empirical risk minimization (ERM) framework. Our framework, despite being simple, is surprisingly able to encompass several recent label-compression based methods which can be derived as special cases of our method. To optimize the ERM problem, we develop techniques that exploit the structure of specific loss functions - such as the squared loss function - to obtain efficient algorithms. We further show that our learning framework admits excess risk bounds even in the presence of missing labels. Our bounds are tight and demonstrate better generalization performance for low-rank promoting trace-norm regularization when compared to (rank insensitive) Frobenius norm regularization. Finally, we present extensive empirical results on a variety of benchmark datasets and show that our methods perform significantly better than existing label compression based methods and can scale up to very large datasets such as a Wikipedia dataset that has more than 200, 000 labels.

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IJCAI Conference 2011 Conference Paper

Large Linear Classification When Data Cannot Fit in Memory

Hsiang-Fu Yu
Cho-Jui Hsieh
Kai-Wei Chang
Chih-Jen Lin

Linear classification is a useful tool for dealing with large-scale data in applications such as document classification and natural language processing. Recent developments of linear classification have shown that the training process can be efficiently conducted. However, when the data size exceeds the memory capacity, most training methods suffer from very slow convergence due to the severe disk swapping. Although some methods have attempted to handle such a situation, they are usually too complicated to support some important functions such as parameter selection. In this paper, we introduce a block minimization framework for data larger than memory. Under the framework, a solver splits data into blocks and stores them into separate files. Then, at each time, the solver trains a data block loaded from disk. Although the framework is simple, the experimental results show that it effectively handles a data set 20 times larger than the memory capacity.

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