mtybilly/Instruct2DS huggingface dataset:
Exact Inference for Integer Latent-Variable Models
Authors: Kevin Winner, Debora Sujono, Dan Sheldon
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Graphical models with latent count variables arise in a number of areas. However, standard inference algorithms do not apply to these models due to the infinite support of the latent variables. Winner and Sheldon (2016) recently developed a new technique using probability generating functions (PGFs) to perform efficient, exact inference for certain Poisson latent variable models. However, the method relies on symbolic manipulation of PGFs, and it is unclear whether this can be extended to more general models. In this paper we introduce a new approach for inference with PGFs: instead of manipulating PGFs symbolically, we adapt techniques from the autodiff literature to compute the higher-order derivatives necessary for inference. This substantially generalizes the class of models for which efficient, exact inference algorithms are available. Specifically, our results apply to a class of models that includes branching processes, which are widely used in applied mathematics and population ecology, and autoregressive models for integer data. Experiments show that our techniques are more scalable than existing approximate methods and enable new applications.
Tensor Belief Propagation
Authors: Andrew Wrigley, Wee Sun Lee, Nan Ye
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: We propose a new approximate inference algorithm for graphical models, tensor belief propagation, based on approximating the messages passed in the junction tree algorithm. Our algorithm represents the potential functions of the graphical model and all messages on the junction tree compactly as mixtures of rank-1 tensors. Using this representation, we show how to perform the operations required for inference on the junction tree efficiently: marginalisation can be computed quickly due to the factored form of rank-1 tensors while multiplication can be approximated using sampling. Our analysis gives sufficient conditions for the algorithm to perform well, including for the case of high-treewidth graphs, for which exact inference is intractable. We compare our algorithm experimentally with several approximate inference algorithms and show that it performs well.
A Unified View of Multi-Label Performance Measures
Authors: Xi-Zhu Wu, Zhi-Hua Zhou
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Multi-label classification deals with the problem where each instance is associated with multiple class labels. Because evaluation in multi-label classification is more complicated than single-label setting, a number of performance measures have been proposed. It is noticed that an algorithm usually performs differently on different measures. Therefore, it is important to understand which algorithms perform well on which measure(s) and why. In this paper, we propose a unified margin view to revisit eleven performance measures in multi-label classification. In particular, we define label-wise margin and instance-wise margin, and prove that through maximizing these margins, different corresponding performance measures are to be optimized. Based on the defined margins, a max-margin approach called LIMO is designed and empirical results validate our theoretical findings.
Dual Supervised Learning
Authors: Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu, Tie-Yan Liu
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Many supervised learning tasks are emerged in dual forms, e.g., English-to-French translation vs. French-to-English translation, speech recognition vs. text to speech, and image classification vs. image generation. Two dual tasks have intrinsic connections with each other due to the probabilistic correlation between their models. This connection is, however, not effectively utilized today, since people usually train the models of two dual tasks separately and independently. In this work, we propose training the models of two dual tasks simultaneously, and explicitly exploiting the probabilistic correlation between them to regularize the training process. For ease of reference, we call the proposed approach dual supervised learning. We demonstrate that dual supervised learning can improve the practical performances of both tasks, for various applications including machine translation, image processing, and sentiment analysis.
Learning Latent Space Models with Angular Constraints
Authors: Pengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yaoliang Yu, James Zou, Eric P. Xing
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: The large model capacity of latent space models (LSMs) enables them to achieve great performance on various applications, but meanwhile renders LSMs to be prone to overfitting. Several recent studies investigate a new type of regularization approach, which encourages components in LSMs to be diverse, for the sake of alleviating overfitting. While they have shown promising empirical effectiveness, in theory why larger “diversity” results in less overfitting is still unclear. To bridge this gap, we propose a new diversity-promoting approach that is both theoretically analyzable and empirically effective. Specifically, we use near-orthogonality to characterize “diversity” and impose angular constraints (ACs) on the components of LSMs to promote diversity. A generalization error analysis shows that larger diversity results in smaller estimation error and larger approximation error. An efficient ADMM algorithm is developed to solve the constrained LSM problems. Experiments demonstrate that ACs improve generalization performance of LSMs and outperform other diversity-promoting approaches.
Uncorrelation and Evenness: a New Diversity-Promoting Regularizer
Authors: Pengtao Xie, Aarti Singh, Eric P. Xing
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Latent space models (LSMs) provide a principled and effective way to extract hidden patterns from observed data. To cope with two challenges in LSMs: (1) how to capture infrequent patterns when pattern frequency is imbalanced and (2) how to reduce model size without sacrificing their expressiveness, several studies have been proposed to “diversify” LSMs, which design regularizers to encourage the components therein to be “diverse”. In light of the limitations of existing approaches, we design a new diversity-promoting regularizer by considering two factors: uncorrelation and evenness, which encourage the components to be uncorrelated and to play equally important roles in modeling data. Formally, this amounts to encouraging the covariance matrix of the components to have more uniform eigenvalues. We apply the regularizer to two LSMs and develop an efficient optimization algorithm. Experiments on healthcare, image and text data demonstrate the effectiveness of the regularizer.
Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence
Authors: Yi Xu, Qihang Lin, Tianbao Yang
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: In this paper, a new theory is developed for first-order stochastic convex optimization, showing that the global convergence rate is sufficiently quantified by a local growth rate of the objective function in a neighborhood of the optimal solutions. In particular, if the objective function $F(퐰)$ in the $ε$-sublevel set grows as fast as $\|퐰 - 퐰_*\|_2^1/þeta$, where $퐰_*$ represents the closest optimal solution to $퐰$ and $þetaın(0,1]$ quantifies the local growth rate, the iteration complexity of first-order stochastic optimization for achieving an $ε$-optimal solution can be $\widetilde O(1/ε^2(1-þeta))$, which is <em>optimal at most</em> up to a logarithmic factor. This result is fundamentally better in contrast with the previous works that either assume a global growth condition in the entire domain or achieve a local faster convergence under the local faster growth condition. To achieve the faster global convergence, we develop two different <em>accelerated stochastic subgradient</em> methods by iteratively solving the original problem approximately in a local region around a historical solution with the size of the local region gradually decreasing as the solution approaches the optimal set. Besides the theoretical improvements, this work also include new contributions towards making the proposed algorithms practical: (i) we present practical variants of accelerated stochastic subgradient methods that can run without the knowledge of multiplicative growth constant and even the growth rate $þeta$; (ii) we consider a broad family of problems in machine learning to demonstrate that the proposed algorithms enjoy faster convergence than traditional stochastic subgradient method. For example, when applied to the $\ell_1$ regularized empirical polyhedral loss minimization (e.g., hinge loss, absolute loss), the proposed stochastic methods have a logarithmic iteration complexity.
Learning Hawkes Processes from Short Doubly-Censored Event Sequences
Authors: Hongteng Xu, Dixin Luo, Hongyuan Zha
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Many real-world applications require robust algorithms to learn point process models based on a type of incomplete data — the so-called short doubly-censored (SDC) event sequences. In this paper, we study this critical problem of quantitative asynchronous event sequence analysis under the framework of Hawkes processes by leveraging the general idea of data synthesis. In particular, given SDC event sequences observed in a variety of time intervals, we propose a sampling-stitching data synthesis method — sampling predecessor and successor for each SDC event sequence from potential candidates and stitching them together to synthesize long training sequences. The rationality and the feasibility of our method are discussed in terms of arguments based on likelihood. Experiments on both synthetic and real-world data demonstrate that the proposed data synthesis method improves learning results indeed for both time-invariant and time-varying Hawkes processes.
Adaptive Consensus ADMM for Distributed Optimization
Authors: Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: The alternating direction method of multipliers (ADMM) is commonly used for distributed model fitting problems, but its performance and reliability depend strongly on user-defined penalty parameters. We study distributed ADMM methods that boost performance by using different fine-tuned algorithm parameters on each worker node. We present a O(1/k) convergence rate for adaptive ADMM methods with node-specific parameters, and propose adaptive consensus ADMM (ACADMM), which automatically tunes parameters without user oversight.
High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation
Authors: Zhuoran Yang, Krishnakumar Balasubramanian, Han Liu
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: We consider estimating the parametric component of single index models in high dimensions. Compared with existing work, we do not require the covariate to be normally distributed. Utilizing Stein’s Lemma, we propose estimators based on the score function of the covariate. Moreover, to handle score function and response variables that are heavy-tailed, our estimators are constructed via carefully thresholding their empirical counterparts. Under a bounded fourth moment condition, we establish optimal statistical rates of convergence for the proposed estimators. Extensive numerical experiments are provided to back up our theory.
Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering
Authors: Bo Yang, Xiao Fu, Nicholas D. Sidiropoulos, Mingyi Hong
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Most learning approaches treat dimensionality reduction (DR) and clustering separately (i.e., sequentially), but recent research has shown that optimizing the two tasks jointly can substantially improve the performance of both. The premise behind the latter genre is that the data samples are obtained via linear transformation of latent representations that are easy to cluster; but in practice, the transformation from the latent space to the data can be more complicated. In this work, we assume that this transformation is an unknown and possibly nonlinear function. To recover the `clustering-friendly’ latent representations and to better cluster the data, we propose a joint DR and K-means clustering approach in which DR is accomplished via learning a deep neural network (DNN). The motivation is to keep the advantages of jointly optimizing the two tasks, while exploiting the deep neural network’s ability to approximate any nonlinear function. This way, the proposed approach can work well for a broad class of generative models. Towards this end, we carefully design the DNN structure and the associated joint optimization criterion, and propose an effective and scalable algorithm to handle the formulated optimization problem. Experiments using different real datasets are employed to showcase the effectiveness of the proposed approach.
On The Projection Operator to A Three-view Cardinality Constrained Set
Authors: Haichuan Yang, Shupeng Gui, Chuyang Ke, Daniel Stefankovic, Ryohei Fujimaki, Ji Liu
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: The cardinality constraint is an intrinsic way to restrict the solution structure in many domains, for example, sparse learning, feature selection, and compressed sensing. To solve a cardinality constrained problem, the key challenge is to solve the projection onto the cardinality constraint set, which is NP-hard in general when there exist multiple overlapped cardinality constraints. In this paper, we consider the scenario where the overlapped cardinality constraints satisfy a Three-view Cardinality Structure (TVCS), which reflects the natural restriction in many applications, such as identification of gene regulatory networks and task-worker assignment problem. We cast the projection into a linear programming, and show that for TVCS, the vertex solution of this linear programming is the solution for the original projection problem. We further prove that such solution can be found with the complexity proportional to the number of variables and constraints. We finally use synthetic experiments and two interesting applications in bioinformatics and crowdsourcing to validate the proposed TVCS model and method.
Improved Variational Autoencoders for Text Modeling using Dilated Convolutions
Authors: Zichao Yang, Zhiting Hu, Ruslan Salakhutdinov, Taylor Berg-Kirkpatrick
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Recent work on generative text modeling has found that variational autoencoders (VAE) with LSTM decoders perform worse than simpler LSTM language models (Bowman et al., 2015). This negative result is so far poorly understood, but has been attributed to the propensity of LSTM decoders to ignore conditioning information from the encoder. In this paper, we experiment with a new type of decoder for VAE: a dilated CNN. By changing the decoder’s dilation architecture, we control the size of context from previously generated words. In experiments, we find that there is a trade-off between contextual capacity of the decoder and effective use of encoding information. We show that when carefully managed, VAEs can outperform LSTM language models. We demonstrate perplexity gains on two datasets, representing the first positive language modeling result with VAE. Further, we conduct an in-depth investigation of the use of VAE (with our new decoding architecture) for semi-supervised and unsupervised labeling tasks, demonstrating gains over several strong baselines.
Tensor-Train Recurrent Neural Networks for Video Classification
Authors: Yinchong Yang, Denis Krompass, Volker Tresp
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: The Recurrent Neural Networks and their variants have shown promising performances in sequence modeling tasks such as Natural Language Processing. These models, however, turn out to be impractical and difficult to train when exposed to very high-dimensional inputs due to the large input-to-hidden weight matrix. This may have prevented RNNs’ large-scale application in tasks that involve very high input dimensions such as video modeling; current approaches reduce the input dimensions using various feature extractors. To address this challenge, we propose a new, more general and efficient approach by factorizing the input-to-hidden weight matrix using Tensor-Train decomposition which is trained simultaneously with the weights themselves. We test our model on classification tasks using multiple real-world video datasets and achieve competitive performances with state-of-the-art models, even though our model architecture is orders of magnitude less complex. We believe that the proposed approach provides a novel and fundamental building block for modeling high-dimensional sequential data with RNN architectures and opens up many possibilities to transfer the expressive and advanced architectures from other domains such as NLP to modeling high-dimensional sequential data.
A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates
Authors: Tianbao Yang, Qihang Lin, Lijun Zhang
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: This paper focuses on convex constrained optimization problems, where the solution is subject to a convex inequality constraint. In particular, we aim at challenging problems for which both projection into the constrained domain and a linear optimization under the inequality constraint are time-consuming, which render both projected gradient methods and conditional gradient methods (a.k.a. the Frank-Wolfe algorithm) expensive. In this paper, we develop projection reduced optimization algorithms for both smooth and non-smooth optimization with improved convergence rates under a certain regularity condition of the constraint function. We first present a general theory of optimization with only one projection. Its application to smooth optimization with only one projection yields $O(1/ε)$ iteration complexity, which improves over the $O(1/ε^2)$ iteration complexity established before for non-smooth optimization and can be further reduced under strong convexity. Then we introduce a local error bound condition and develop faster algorithms for non-strongly convex optimization at the price of a logarithmic number of projections. In particular, we achieve an iteration complexity of $\widetilde O(1/ε^2(1-þeta))$ for non-smooth optimization and $\widetilde O(1/ε^1-þeta)$ for smooth optimization, where $þetaın(0,1]$ appearing the local error bound condition characterizes the functional local growth rate around the optimal solutions. Novel applications in solving the constrained $\ell_1$ minimization problem and a positive semi-definite constrained distance metric learning problem demonstrate that the proposed algorithms achieve significant speed-up compared with previous algorithms.
Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-Convexity
Authors: Eunho Yang, Aurélie C. Lozano
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Imposing sparse + group-sparse superposition structures in high-dimensional parameter estimation is known to provide flexible regularization that is more realistic for many real-world problems. For example, such a superposition enables partially-shared support sets in multi-task learning, thereby striking the right balance between parameter overlap across tasks and task specificity. Existing theoretical results on estimation consistency, however, are problematic as they require too stringent an assumption: the incoherence between sparse and group-sparse superposed components. In this paper, we fill the gap between the practical success and suboptimal analysis of sparse + group-sparse models, by providing the first consistency results that do not require unrealistic assumptions. We also study non-convex counterparts of sparse + group-sparse models. Interestingly, we show that these are guaranteed to recover the true support set under much milder conditions and with smaller sample size than convex models, which might be critical in practical applications as illustrated by our experiments.
Scalable Bayesian Rule Lists
Authors: Hongyu Yang, Cynthia Rudin, Margo Seltzer
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: We present an algorithm for building probabilistic rule lists that is two orders of magnitude faster than previous work. Rule list algorithms are competitors for decision tree algorithms. They are associative classifiers, in that they are built from pre-mined association rules. They have a logical structure that is a sequence of IF-THEN rules, identical to a decision list or one-sided decision tree. Instead of using greedy splitting and pruning like decision tree algorithms, we aim to fully optimize over rule lists, striking a practical balance between accuracy, interpretability, and computational speed. The algorithm presented here uses a mixture of theoretical bounds (tight enough to have practical implications as a screening or bounding procedure), computational reuse, and highly tuned language libraries to achieve computational efficiency. Currently, for many practical problems, this method achieves better accuracy and sparsity than decision trees. In many cases, the computational time is practical and often less than that of decision trees.
Approximate Newton Methods and Their Local Convergence
Authors: Haishan Ye, Luo Luo, Zhihua Zhang
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Many machine learning models are reformulated as optimization problems. Thus, it is important to solve a large-scale optimization problem in big data applications. Recently, subsampled Newton methods have emerged to attract much attention for optimization due to their efficiency at each iteration, rectified a weakness in the ordinary Newton method of suffering a high cost in each iteration while commanding a high convergence rate. Other efficient stochastic second order methods are also proposed. However, the convergence properties of these methods are still not well understood. There are also several important gaps between the current convergence theory and performance in real applications. In this paper, we aim to fill these gaps. We propose a unifying framework to analyze local convergence properties of second order methods. Based on this framework, our theoretical analysis matches the performance in real applications.
A Simulated Annealing Based Inexact Oracle for Wasserstein Loss Minimization
Authors: Jianbo Ye, James Z. Wang, Jia Li
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Learning under a Wasserstein loss, a.k.a. Wasserstein loss minimization (WLM), is an emerging research topic for gaining insights from a large set of structured objects. Despite being conceptually simple, WLM problems are computationally challenging because they involve minimizing over functions of quantities (i.e. Wasserstein distances) that themselves require numerical algorithms to compute. In this paper, we introduce a stochastic approach based on simulated annealing for solving WLMs. Particularly, we have developed a Gibbs sampler to approximate effectively and efficiently the partial gradients of a sequence of Wasserstein losses. Our new approach has the advantages of numerical stability and readiness for warm starts. These characteristics are valuable for WLM problems that often require multiple levels of iterations in which the oracle for computing the value and gradient of a loss function is embedded. We applied the method to optimal transport with Coulomb cost and the Wasserstein non-negative matrix factorization problem, and made comparisons with the existing method of entropy regularization.
Latent Feature Lasso
Authors: Ian En-Hsu Yen, Wei-Cheng Lee, Sung-En Chang, Arun Sai Suggala, Shou-De Lin, Pradeep Ravikumar
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: The latent feature model (LFM), proposed in (Griffiths & Ghahramani, 2005), but possibly with earlier origins, is a generalization of a mixture model, where each instance is generated not from a single latent class but from a combination of <em>latent features</em>. Thus, each instance has an associated latent binary feature incidence vector indicating the presence or absence of a feature. Due to its combinatorial nature, inference of LFMs is considerably intractable, and accordingly, most of the attention has focused on nonparametric LFMs, with priors such as the Indian Buffet Process (IBP) on infinite binary matrices. Recent efforts to tackle this complexity either still have computational complexity that is exponential, or sample complexity that is high-order polynomial w.r.t. the number of latent features. In this paper, we address this outstanding problem of tractable estimation of LFMs via a novel atomic-norm regularization, which gives an algorithm with polynomial run-time and sample complexity without impractical assumptions on the data distribution.
Combined Group and Exclusive Sparsity for Deep Neural Networks
Authors: Jaehong Yoon, Sung Ju Hwang
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: The number of parameters in a deep neural network is usually very large, which helps with its learning capacity but also hinders its scalability and practicality due to memory/time inefficiency and overfitting. To resolve this issue, we propose a sparsity regularization method that exploits both positive and negative correlations among the features to enforce the network to be sparse, and at the same time remove any redundancies among the features to fully utilize the capacity of the network. Specifically, we propose to use an exclusive sparsity regularization based on (1,2)-norm, which promotes competition for features between different weights, thus enforcing them to fit to disjoint sets of features. We further combine the exclusive sparsity with the group sparsity based on (2,1)-norm, to promote both sharing and competition for features in training of a deep neural network. We validate our method on multiple public datasets, and the results show that our method can obtain more compact and efficient networks while also improving the performance over the base networks with full weights, as opposed to existing sparsity regularizations that often obtain efficiency at the expense of prediction accuracy.
Latent LSTM Allocation: Joint Clustering and Non-Linear Dynamic Modeling of Sequence Data
Authors: Manzil Zaheer, Amr Ahmed, Alexander J. Smola
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Recurrent neural networks, such as long-short term memory (LSTM) networks, are powerful tools for modeling sequential data like user browsing history (Tan et al., 2016; Korpusik et al., 2016) or natural language text (Mikolov et al., 2010). However, to generalize across different user types, LSTMs require a large number of parameters, notwithstanding the simplicity of the underlying dynamics, rendering it uninterpretable, which is highly undesirable in user modeling. The increase in complexity and parameters arises due to a large action space in which many of the actions have similar intent or topic. In this paper, we introduce Latent LSTM Allocation (LLA) for user modeling combining hierarchical Bayesian models with LSTMs. In LLA, each user is modeled as a sequence of actions, and the model jointly groups actions into topics and learns the temporal dynamics over the topic sequence, instead of action space directly. This leads to a model that is highly interpretable, concise, and can capture intricate dynamics. We present an efficient Stochastic EM inference algorithm for our model that scales to millions of users/documents. Our experimental evaluations show that the proposed model compares favorably with several state-of-the-art baselines.
Canopy  Fast Sampling with Cover Trees
Authors: Manzil Zaheer, Satwik Kottur, Amr Ahmed, José Moura, Alex Smola
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Hierarchical Bayesian models often capture distributions over a very large number of distinct atoms. The need for these models arises when organizing huge amount of unsupervised data, for instance, features extracted using deep convnets that can be exploited to organize abundant unlabeled images. Inference for hierarchical Bayesian models in such cases can be rather nontrivial, leading to approximate approaches. In this work, we propose <em>Canopy</em>, a sampler based on Cover Trees that is exact, has guaranteed runtime logarithmic in the number of atoms, and is provably polynomial in the inherent dimensionality of the underlying parameter space. In other words, the algorithm is as fast as search over a hierarchical data structure. We provide theory for Canopy and demonstrate its effectiveness on both synthetic and real datasets, consisting of over 100 million images.
Continual Learning Through Synaptic Intelligence
Authors: Friedemann Zenke, Ben Poole, Surya Ganguli
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: While deep learning has led to remarkable advances across diverse applications, it struggles in domains where the data distribution changes over the course of learning. In stark contrast, biological neural networks continually adapt to changing domains, possibly by leveraging complex molecular machinery to solve many tasks simultaneously. In this study, we introduce intelligent synapses that bring some of this biological complexity into artificial neural networks. Each synapse accumulates task relevant information over time, and exploits this information to rapidly store new memories without forgetting old ones. We evaluate our approach on continual learning of classification tasks, and show that it dramatically reduces forgetting while maintaining computational efficiency.
Stochastic Gradient Monomial Gamma Sampler
Authors: Yizhe Zhang, Changyou Chen, Zhe Gan, Ricardo Henao, Lawrence Carin
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Scaling Markov Chain Monte Carlo (MCMC) to estimate posterior distributions from large datasets has been made possible as a result of advances in stochastic gradient techniques. Despite their success, mixing performance of existing methods when sampling from multimodal distributions can be less efficient with insufficient Monte Carlo samples; this is evidenced by slow convergence and insufficient exploration of posterior distributions. We propose a generalized framework to improve the sampling efficiency of stochastic gradient MCMC, by leveraging a generalized kinetics that delivers superior stationary mixing, especially in multimodal distributions, and propose several techniques to overcome the practical issues. We show that the proposed approach is better at exploring a complicated multimodal posterior distribution, and demonstrate improvements over other stochastic gradient MCMC methods on various applications.
Adversarial Feature Matching for Text Generation
Authors: Yizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao, Dinghan Shen, Lawrence Carin
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: The Generative Adversarial Network (GAN) has achieved great success in generating realistic (real-valued) synthetic data. However, convergence issues and difficulties dealing with discrete data hinder the applicability of GAN to text. We propose a framework for generating realistic text via adversarial training. We employ a long short-term memory network as generator, and a convolutional network as discriminator. Instead of using the standard objective of GAN, we propose matching the high-dimensional latent feature distributions of real and synthetic sentences, via a kernelized discrepancy metric. This eases adversarial training by alleviating the mode-collapsing problem. Our experiments show superior performance in quantitative evaluation, and demonstrate that our model can generate realistic-looking sentences.
Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction
Authors: Weizhong Zhang, Bin Hong, Wei Liu, Jieping Ye, Deng Cai, Xiaofei He, Jie Wang
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Sparse support vector machine (SVM) is a popular classification technique that can simultaneously learn a small set of the most interpretable features and identify the support vectors. It has achieved great successes in many real-world applications. However, for large-scale problems involving a huge number of samples and extremely high-dimensional features, solving sparse SVMs remains challenging. By noting that sparse SVMs induce sparsities in both feature and sample spaces, we propose a novel approach, which is based on accurate estimations of the primal and dual optima of sparse SVMs, to simultaneously identify the features and samples that are guaranteed to be irrelevant to the outputs. Thus, we can remove the identified inactive samples and features from the training phase, leading to substantial savings in both the memory usage and computational cost without sacrificing accuracy. To the best of our knowledge, the proposed method is the <em>first</em> <em>static</em> feature and sample reduction method for sparse SVMs. Experiments on both synthetic and real datasets (e.g., the kddb dataset with about 20 million samples and 30 million features) demonstrate that our approach significantly outperforms state-of-the-art methods and the speedup gained by our approach can be orders of magnitude.
Re-revisiting Learning on Hypergraphs: Confidence Interval and Subgradient Method
Authors: Chenzi Zhang, Shuguang Hu, Zhihao Gavin Tang, T-H. Hubert Chan
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: We revisit semi-supervised learning on hypergraphs. Same as previous approaches, our method uses a convex program whose objective function is not everywhere differentiable. We exploit the non-uniqueness of the optimal solutions, and consider confidence intervals which give the exact ranges that unlabeled vertices take in any optimal solution. Moreover, we give a much simpler approach for solving the convex program based on the subgradient method. Our experiments on real-world datasets confirm that our confidence interval approach on hypergraphs outperforms existing methods, and our sub-gradient method gives faster running times when the number of vertices is much larger than the number of edges.
ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning
Authors: Hantian Zhang, Jerry Li, Kaan Kara, Dan Alistarh, Ji Liu, Ce Zhang
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Recently there has been significant interest in training machine-learning models at low precision: by reducing precision, one can reduce computation and communication by one order of magnitude. We examine training at reduced precision, both from a theoretical and practical perspective, and ask: is it possible to train models at end-to-end low precision with provable guarantees? Can this lead to consistent order-of-magnitude speedups? We mainly focus on linear models, and the answer is yes for linear models. We develop a simple framework called ZipML based on one simple but novel strategy called double sampling. Our ZipML framework is able to execute training at low precision with no bias, guaranteeing convergence, whereas naive quantization would introduce significant bias. We validate our framework across a range of applications, and show that it enables an FPGA prototype that is up to $6.5\times$ faster than an implementation using full 32-bit precision. We further develop a variance-optimal stochastic quantization strategy and show that it can make a significant difference in a variety of settings. When applied to linear models together with double sampling, we save up to another $1.7\times$ in data movement compared with uniform quantization. When training deep networks with quantized models, we achieve higher accuracy than the state-of-the-art XNOR-Net.
Convexified Convolutional Neural Networks
Authors: Yuchen Zhang, Percy Liang, Martin J. Wainwright
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: We describe the class of convexified convolutional neural networks (CCNNs), which capture the parameter sharing of convolutional neural networks in a convex manner. By representing the nonlinear convolutional filters as vectors in a reproducing kernel Hilbert space, the CNN parameters can be represented as a low-rank matrix, which can be relaxed to obtain a convex optimization problem. For learning two-layer convolutional neural networks, we prove that the generalization error obtained by a convexified CNN converges to that of the best possible CNN. For learning deeper networks, we train CCNNs in a layer-wise manner. Empirically, CCNNs achieve competitive or better performance than CNNs trained by backpropagation, SVMs, fully-connected neural networks, stacked denoising auto-encoders, and other baseline methods.
Projection-free Distributed Online Learning in Networks
Authors: Wenpeng Zhang, Peilin Zhao, Wenwu Zhu, Steven C. H. Hoi, Tong Zhang
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: The conditional gradient algorithm has regained a surge of research interest in recent years due to its high efficiency in handling large-scale machine learning problems. However, none of existing studies has explored it in the distributed online learning setting, where locally light computation is assumed. In this paper, we fill this gap by proposing the distributed online conditional gradient algorithm, which eschews the expensive projection operation needed in its counterpart algorithms by exploiting much simpler linear optimization steps. We give a regret bound for the proposed algorithm as a function of the network size and topology, which will be smaller on smaller graphs or “well-connected” graphs. Experiments on two large-scale real-world datasets for a multiclass classification task confirm the computational benefit of the proposed algorithm and also verify the theoretical regret bound.
Multi-Class Optimal Margin Distribution Machine
Authors: Teng Zhang, Zhi-Hua Zhou
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Recent studies disclose that maximizing the minimum margin like support vector machines does not necessarily lead to better generalization performances, and instead, it is crucial to optimize the margin distribution. Although it has been shown that for binary classification, characterizing the margin distribution by the first- and second-order statistics can achieve superior performance. It still remains open for multi-class classification, and due to the complexity of margin for multi-class classification, optimizing its distribution by mean and variance can also be difficult. In this paper, we propose mcODM (multi-class Optimal margin Distribution Machine), which can solve this problem efficiently. We also give a theoretical analysis for our method, which verifies the significance of margin distribution for multi-class classification. Empirical study further shows that mcODM always outperforms all four versions of multi-class SVMs on all experimental data sets.
Leveraging Node Attributes for Incomplete Relational Data
Authors: He Zhao, Lan Du, Wray Buntine
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Relational data are usually highly incomplete in practice, which inspires us to leverage side information to improve the performance of community detection and link prediction. This paper presents a Bayesian probabilistic approach that incorporates various kinds of node attributes encoded in binary form in relational models with Poisson likelihood. Our method works flexibly with both directed and undirected relational networks. The inference can be done by efficient Gibbs sampling which leverages sparsity of both networks and node attributes. Extensive experiments show that our models achieve the state-of-the-art link prediction results, especially with highly incomplete relational data.
Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank
Authors: Liang Zhao, Siyu Liao, Yanzhi Wang, Zhe Li, Jian Tang, Bo Yuan
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Recently low displacement rank (LDR) matrices, or so-called structured matrices, have been proposed to compress large-scale neural networks. Empirical results have shown that neural networks with weight matrices of LDR matrices, referred as LDR neural networks, can achieve significant reduction in space and computational complexity while retaining high accuracy. This paper gives theoretical study on LDR neural networks. First, we prove the universal approximation property of LDR neural networks with a mild condition on the displacement operators. We then show that the error bounds of LDR neural networks are as efficient as general neural networks with both single-layer and multiple-layer structure. Finally, we propose back-propagation based training algorithm for general LDR neural networks.
Learning Hierarchical Features from Deep Generative Models
Authors: Shengjia Zhao, Jiaming Song, Stefano Ermon
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Deep neural networks have been shown to be very successful at learning feature hierarchies in supervised learning tasks. Generative models, on the other hand, have benefited less from hierarchical models with multiple layers of latent variables. In this paper, we prove that hierarchical latent variable models do not take advantage of the hierarchical structure when trained with existing variational methods, and provide some limitations on the kind of features existing models can learn. Finally we propose an alternative architecture that do not suffer from these limitations. Our model is able to learn highly interpretable and disentangled hierarchical features on several natural image datasets with no task specific regularization or prior knowledge.
Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture
Authors: Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi S. Jaakkola, Matt T. Bianchi
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: We focus on predicting sleep stages from radio measurements without any attached sensors on subjects. We introduce a new predictive model that combines convolutional and recurrent neural networks to extract sleep-specific subject-invariant features from RF signals and capture the temporal progression of sleep. A key innovation underlying our approach is a modified adversarial training regime that discards extraneous information specific to individuals or measurement conditions, while retaining all information relevant to the predictive task. We analyze our game theoretic setup and empirically demonstrate that our model achieves significant improvements over state-of-the-art solutions.
Follow the Moving Leader in Deep Learning
Authors: Shuai Zheng, James T. Kwok
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: Deep networks are highly nonlinear and difficult to optimize. During training, the parameter iterate may move from one local basin to another, or the data distribution may even change. Inspired by the close connection between stochastic optimization and online learning, we propose a variant of the <em>follow the regularized leader</em> (FTRL) algorithm called <em>follow the moving leader</em> (FTML). Unlike the FTRL family of algorithms, the recent samples are weighted more heavily in each iteration and so FTML can adapt more quickly to changes. We show that FTML enjoys the nice properties of RMSprop and Adam, while avoiding their pitfalls. Experimental results on a number of deep learning models and tasks demonstrate that FTML converges quickly, and outperforms other state-of-the-art optimizers.
Asynchronous Stochastic Gradient Descent with Delay Compensation
Authors: Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, Nenghai Yu, Zhi-Ming Ma, Tie-Yan Liu
Published in: Proceedings of the 34th International Conference on Machine Learning (Main Conference Track), 2017

Abstract: With the fast development of deep learning, it has become common to learn big neural networks using massive training data. Asynchronous Stochastic Gradient Descent (ASGD) is widely adopted to fulfill this task for its efficiency, which is, however, known to suffer from the problem of delayed gradients. That is, when a local worker adds its gradient to the global model, the global model may have been updated by other workers and this gradient becomes “delayed”. We propose a novel technology to compensate this delay, so as to make the optimization behavior of ASGD closer to that of sequential SGD. This is achieved by leveraging Taylor expansion of the gradient function and efficient approximators to the Hessian matrix of the loss function. We call the new algorithm Delay Compensated ASGD (DC-ASGD). We evaluated the proposed algorithm on CIFAR-10 and ImageNet datasets, and the experimental results demonstrate that DC-ASGD outperforms both synchronous SGD and asynchronous SGD, and nearly approaches the performance of sequential SGD.