mtybilly/Instruct2DS huggingface dataset:
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning
Authors: Rodrigo Toro Icarte, Toryn Klassen, Richard Valenzano, Sheila McIlraith
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: In this paper we propose Reward Machines — a type of finite state machine that supports the specification of reward functions while exposing reward function structure to the learner and supporting decomposition. We then present Q-Learning for Reward Machines (QRM), an algorithm which appropriately decomposes the reward machine and uses off-policy q-learning to simultaneously learn subpolicies for the different components. QRM is guaranteed to converge to an optimal policy in the tabular case, in contrast to Hierarchical Reinforcement Learning methods which might converge to suboptimal policies. We demonstrate this behavior experimentally in two discrete domains. We also show how function approximation methods like neural networks can be incorporated into QRM, and that doing so can find better policies more quickly than hierarchical methods in a domain with a continuous state space.
Deep Variational Reinforcement Learning for POMDPs
Authors: Maximilian Igl, Luisa Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown. Consequently, there is great need for reinforcement learning methods that can tackle such problems given only a stream of rewards and incomplete and noisy observations. In this paper, we propose deep variational reinforcement learning (DVRL), which introduces an inductive bias that allows an agent to learn a generative model of the environment and perform inference in that model to effectively aggregate the available information. We develop an n-step approximation to the evidence lower bound (ELBO), allowing the model to be trained jointly with the policy. This ensures that the latent state representation is suitable for the control task. In experiments on Mountain Hike and flickering Atari we show that our method outperforms previous approaches relying on recurrent neural networks to encode the past.
Attention-based Deep Multiple Instance Learning
Authors: Maximilian Ilse, Jakub Tomczak, Max Welling
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances. In this paper, we state the MIL problem as learning the Bernoulli distribution of the bag label where the bag label probability is fully parameterized by neural networks. Furthermore, we propose a neural network-based permutation-invariant aggregation operator that corresponds to the attention mechanism. Notably, an application of the proposed attention-based operator provides insight into the contribution of each instance to the bag label. We show empirically that our approach achieves comparable performance to the best MIL methods on benchmark MIL datasets and it outperforms other methods on a MNIST-based MIL dataset and two real-life histopathology datasets without sacrificing interpretability.
Black-box Adversarial Attacks with Limited Queries and Information
Authors: Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Current neural network-based classifiers are susceptible to adversarial examples even in the black-box setting, where the attacker only has query access to the model. In practice, the threat model for real-world systems is often more restrictive than the typical black-box model where the adversary can observe the full output of the network on arbitrarily many chosen inputs. We define three realistic threat models that more accurately characterize many real-world classifiers: the query-limited setting, the partial-information setting, and the label-only setting. We develop new attacks that fool classifiers under these more restrictive threat models, where previous methods would be impractical or ineffective. We demonstrate that our methods are effective against an ImageNet classifier under our proposed threat models. We also demonstrate a targeted black-box attack against a commercial classifier, overcoming the challenges of limited query access, partial information, and other practical issues to break the Google Cloud Vision API.
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model
Authors: Hideaki Imamura, Issei Sato, Masashi Sugiyama
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: While crowdsourcing has become an important means to label data, there is great interest in estimating the ground truth from unreliable labels produced by crowdworkers. The Dawid and Skene (DS) model is one of the most well-known models in the study of crowdsourcing. Despite its practical popularity, theoretical error analysis for the DS model has been conducted only under restrictive assumptions on class priors, confusion matrices, or the number of labels each worker provides. In this paper, we derive a minimax error rate under more practical setting for a broader class of crowdsourcing models including the DS model as a special case. We further propose the worker clustering model, which is more practical than the DS model under real crowdsourcing settings. The wide applicability of our theoretical analysis allows us to immediately investigate the behavior of this proposed model, which can not be analyzed by existing studies. Experimental results showed that there is a strong similarity between the lower bound of the minimax error rate derived by our theoretical analysis and the empirical error of the estimated value.
Improving Regression Performance with Distributional Losses
Authors: Ehsan Imani, Martha White
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: There is growing evidence that converting targets to soft targets in supervised learning can provide considerable gains in performance. Much of this work has considered classification, converting hard zero-one values to soft labels—such as by adding label noise, incorporating label ambiguity or using distillation. In parallel, there is some evidence from a regression setting in reinforcement learning that learning distributions can improve performance. In this work, we investigate the reasons for this improvement, in a regression setting. We introduce a novel distributional regression loss, and similarly find it significantly improves prediction accuracy. We investigate several common hypotheses, around reducing overfitting and improved representations. We instead find evidence for an alternative hypothesis: this loss is easier to optimize, with better behaved gradients, resulting in improved generalization. We provide theoretical support for this alternative hypothesis, by characterizing the norm of the gradients of this loss.
Deep Density Destructors
Authors: David Inouye, Pradeep Ravikumar
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We propose a unified framework for deep density models by formally defining density destructors. A density destructor is an invertible function that transforms a given density to the uniform density—essentially destroying any structure in the original density. This destructive transformation generalizes Gaussianization via ICA and more recent autoregressive models such as MAF and Real NVP. Informally, this transformation can be seen as a generalized whitening procedure or a multivariate generalization of the univariate CDF function. Unlike Gaussianization, our destructive transformation has the elegant property that the density function is equal to the absolute value of the Jacobian determinant. Thus, each layer of a deep density can be seen as a shallow density—uncovering a fundamental connection between shallow and deep densities. In addition, our framework provides a common interface for all previous methods enabling them to be systematically combined, evaluated and improved. Leveraging the connection to shallow densities, we also propose a novel tree destructor based on tree densities and an image-specific destructor based on pixel locality. We illustrate our framework on a 2D dataset, MNIST, and CIFAR-10. Code is available on first author’s website.
Unbiased Objective Estimation in Predictive Optimization
Authors: Shinji Ito, Akihiro Yabe, Ryohei Fujimaki
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: For data-driven decision-making, one promising approach, called predictive optimization, is to solve maximization problems i n which the objective function to be maximized is estimated from data. Predictive optimization, however, suffers from the problem of a calculated optimal solution’s being evaluated too optimistically, i.e., the value of the objective function is overestimated. This paper investigates such optimistic bias and presents two methods for correcting it. The first, which is analogous to cross-validation, successfully corrects the optimistic bias but results in underestimation of the true value. Our second method employs resampling techniques to avoid both overestimation and underestimation. We show that the second method, referred to as the parameter perturbation method, achieves asymptotically unbiased estimation. Empirical results for both artificial and real-world datasets demonstrate that our proposed approach successfully corrects the optimistic bias.
Anonymous Walk Embeddings
Authors: Sergey Ivanov, Evgeny Burnaev
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: The task of representing entire graphs has seen a surge of prominent results, mainly due to learning convolutional neural networks (CNNs) on graph-structured data. While CNNs demonstrate state-of-the-art performance in graph classification task, such methods are supervised and therefore steer away from the original problem of network representation in task-agnostic manner. Here, we coherently propose an approach for embedding entire graphs and show that our feature representations with SVM classifier increase classification accuracy of CNN algorithms and traditional graph kernels. For this we describe a recently discovered graph object, <em>anonymous walk</em>, on which we design task-independent algorithms for learning graph representations in explicit and distributed way. Overall, our work represents a new scalable unsupervised learning of state-of-the-art representations of entire graphs.
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach
Authors: Ariel Jaffe, Roi Weiss, Boaz Nadler, Shai Carmi, Yuval Kluger
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Latent variable models with hidden binary units appear in various applications. Learning such models, in particular in the presence of noise, is a challenging computational problem. In this paper we propose a novel spectral approach to this problem, based on the eigenvectors of both the second order moment matrix and third order moment tensor of the observed data. We prove that under mild non-degeneracy conditions, our method consistently estimates the model parameters at the optimal parametric rate. Our tensor-based method generalizes previous orthogonal tensor decomposition approaches, where the hidden units were assumed to be either statistically independent or mutually exclusive. We illustrate the consistency of our method on simulated data and demonstrate its usefulness in learning a common model for population mixtures in genetics.
Firing Bandits: Optimizing Crowdfunding
Authors: Lalit Jain, Kevin Jamieson
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: In this paper, we model the problem of optimizing crowdfunding platforms, such as the non-profit Kiva or for-profit KickStarter, as a variant of the multi-armed bandit problem. In our setting, Bernoulli arms emit no rewards until their cumulative number of successes over any number of trials exceeds a fixed threshold and then provides no additional reward for any additional trials - a process reminiscent to that of a neuron firing once it reaches the action potential and then saturates. In the spirit of an infinite armed bandit problem, the player can add new arms whose expected probability of success is drawn iid from an unknown distribution – this endless supply of projects models the harsh reality that the number of projects seeking funding greatly exceeds the total capital available by lenders. Crowdfunding platforms naturally fall under this setting where the arms are potential projects, and their probability of success is the probability that a potential funder decides to fund it after reviewing it. The goal is to play arms (prioritize the display of projects on a webpage) to maximize the number of arms that reach the firing threshold (meet their goal amount) using as few total trials (number of impressions) as possible over all the played arms. We provide an algorithm for this setting and prove sublinear regret bounds.
Differentially Private Matrix Completion Revisited
Authors: Prateek Jain, Om Dipakbhai Thakkar, Abhradeep Thakurta
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We provide the first provably joint differentially private algorithm with formal utility guarantees for the problem of user-level privacy-preserving collaborative filtering. Our algorithm is based on the Frank-Wolfe method, and it consistently estimates the underlying preference matrix as long as the number of users $m$ is $ømega(n^5/4)$, where $n$ is the number of items, and each user provides her preference for at least $\sqrtn$ randomly selected items. Along the way, we provide an optimal differentially private algorithm for singular vector computation, based on the celebrated Oja’s method, that provides significant savings in terms of space and time while operating on sparse matrices. We also empirically evaluate our algorithm on a suite of datasets, and show that it consistently outperforms the state-of-the-art private algorithms.
Video Prediction with Appearance and Motion Conditions
Authors: Yunseok Jang, Gunhee Kim, Yale Song
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Video prediction aims to generate realistic future frames by learning dynamic visual patterns. One fundamental challenge is to deal with future uncertainty: How should a model behave when there are multiple correct, equally probable future? We propose an Appearance-Motion Conditional GAN to address this challenge. We provide appearance and motion information as conditions that specify how the future may look like, reducing the level of uncertainty. Our model consists of a generator, two discriminators taking charge of appearance and motion pathways, and a perceptual ranking module that encourages videos of similar conditions to look similar. To train our model, we develop a novel conditioning scheme that consists of different combinations of appearance and motion conditions. We evaluate our model using facial expression and human action datasets and report favorable results compared to existing methods.
Pathwise Derivatives Beyond the Reparameterization Trick
Authors: Martin Jankowiak, Fritz Obermeyer
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We observe that gradients computed via the reparameterization trick are in direct correspondence with solutions of the transport equation in the formalism of optimal transport. We use this perspective to compute (approximate) pathwise gradients for probability distributions not directly amenable to the reparameterization trick: Gamma, Beta, and Dirichlet. We further observe that when the reparameterization trick is applied to the Cholesky-factorized multivariate Normal distribution, the resulting gradients are suboptimal in the sense of optimal transport. We derive the optimal gradients and show that they have reduced variance in a Gaussian Process regression task. We demonstrate with a variety of synthetic experiments and stochastic variational inference tasks that our pathwise gradients are competitive with other methods.
Detecting non-causal artifacts in multivariate linear regression models
Authors: Dominik Janzing, Bernhard Schölkopf
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We consider linear models where d potential causes X_1,...,X_d are correlated with one target quantity Y and propose a method to infer whether the association is causal or whether it is an artifact caused by overfitting or hidden common causes. We employ the idea that in the former case the vector of regression coefficients has ‘generic’ orientation relative to the covariance matrix Sigma_XX of X. Using an ICA based model for confounding, we show that both confounding and overfitting yield regression vectors that concentrate mainly in the space of low eigenvalues of Sigma_XX.
A Unified Framework for Structured Low-rank Matrix Learning
Authors: Pratik Jawanpuria, Bamdev Mishra
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We consider the problem of learning a low-rank matrix, constrained to lie in a linear subspace, and introduce a novel factorization for modeling such matrices. A salient feature of the proposed factorization scheme is it decouples the low-rank and the structural constraints onto separate factors. We formulate the optimization problem on the Riemannian spectrahedron manifold, where the Riemannian framework allows to develop computationally efficient conjugate gradient and trust-region algorithms. Experiments on problems such as standard/robust/non-negative matrix completion, Hankel matrix learning and multi-task learning demonstrate the efficacy of our approach.
Efficient end-to-end learning for quantizable representations
Authors: Yeonwoo Jeong, Hyun Oh Song
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Embedding representation learning via neural networks is at the core foundation of modern similarity based search. While much effort has been put in developing algorithms for learning binary hamming code representations for search efficiency, this still requires a linear scan of the entire dataset per each query and trades off the search accuracy through binarization. To this end, we consider the problem of directly learning a quantizable embedding representation and the sparse binary hash code end-to-end which can be used to construct an efficient hash table not only providing significant search reduction in the number of data but also achieving the state of the art search accuracy outperforming previous state of the art deep metric learning methods. We also show that finding the optimal sparse binary hash code in a mini-batch can be computed exactly in polynomial time by solving a minimum cost flow problem. Our results on Cifar-100 and on ImageNet datasets show the state of the art search accuracy in precision@k and NMI metrics while providing up to 98X and 478X search speedup respectively over exhaustive linear search. The source code is available at https://github.com/maestrojeong/Deep-Hash-Table-ICML18.
Exploring Hidden Dimensions in Accelerating Convolutional Neural Networks
Authors: Zhihao Jia, Sina Lin, Charles R. Qi, Alex Aiken
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: The past few years have witnessed growth in the computational requirements for training deep convolutional neural networks. Current approaches parallelize training onto multiple devices by applying a single parallelization strategy (e.g., data or model parallelism) to all layers in a network. Although easy to reason about, these approaches result in suboptimal runtime performance in large-scale distributed training, since different layers in a network may prefer different parallelization strategies. In this paper, we propose layer-wise parallelism that allows each layer in a network to use an individual parallelization strategy. We jointly optimize how each layer is parallelized by solving a graph search problem. Our evaluation shows that layer-wise parallelism outperforms state-of-the-art approaches by increasing training throughput, reducing communication costs, achieving better scalability to multiple GPUs, while maintaining original network accuracy.
Feedback-Based Tree Search for Reinforcement Learning
Authors: Daniel Jiang, Emmanuel Ekwedike, Han Liu
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Inspired by recent successes of Monte-Carlo tree search (MCTS) in a number of artificial intelligence (AI) application domains, we propose a reinforcement learning (RL) technique that iteratively applies MCTS on batches of small, finite-horizon versions of the original infinite-horizon Markov decision process. The terminal condition of the finite-horizon problems, or the leaf-node evaluator of the decision tree generated by MCTS, is specified using a combination of an estimated value function and an estimated policy function. The recommendations generated by the MCTS procedure are then provided as feedback in order to refine, through classification and regression, the leaf-node evaluator for the next iteration. We provide the first sample complexity bounds for a tree search-based RL algorithm. In addition, we show that a deep neural network implementation of the technique can create a competitive AI agent for the popular multi-player online battle arena (MOBA) game King of Glory.
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
Authors: Heinrich Jiang, Jennifer Jang, Samory Kpotufe
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We provide initial seedings to the Quick Shift clustering algorithm, which approximate the locally high-density regions of the data. Such seedings act as more stable and expressive cluster-cores than the singleton modes found by Quick Shift. We establish statistical consistency guarantees for this modification. We then show strong clustering performance on real datasets as well as promising applications to image segmentation.
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Authors: Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, Li Fei-Fei
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Recent deep networks are capable of memorizing the entire data even when the labels are completely random. To overcome the overfitting on corrupted labels, we propose a novel technique of learning another neural network, called MentorNet, to supervise the training of the base deep networks, namely, StudentNet. During training, MentorNet provides a curriculum (sample weighting scheme) for StudentNet to focus on the sample the label of which is probably correct. Unlike the existing curriculum that is usually predefined by human experts, MentorNet learns a data-driven curriculum dynamically with StudentNet. Experimental results demonstrate that our approach can significantly improve the generalization performance of deep networks trained on corrupted training data. Notably, to the best of our knowledge, we achieve the best-published result on WebVision, a large benchmark containing 2.2 million images of real-world noisy labels.
The Weighted Kendall and High-order Kernels for Permutations
Authors: Yunlong Jiao, Jean-Philippe Vert
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We propose new positive definite kernels for permutations. First we introduce a weighted version of the Kendall kernel, which allows to weight unequally the contributions of different item pairs in the permutations depending on their ranks. Like the Kendall kernel, we show that the weighted version is invariant to relabeling of items and can be computed efficiently in O(n ln(n)) operations, where n is the number of items in the permutation. Second, we propose a supervised approach to learn the weights by jointly optimizing them with the function estimated by a kernel machine. Third, while the Kendall kernel considers pairwise comparison between items, we extend it by considering higher-order comparisons among tuples of items and show that the supervised approach of learning the weights can be systematically generalized to higher-order permutation kernels.
Junction Tree Variational Autoencoder for Molecular Graph Generation
Authors: Wengong Jin, Regina Barzilay, Tommi Jaakkola
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We seek to automate the design of molecules based on specific chemical properties. In computational terms, this task involves continuous embedding and generation of molecular graphs. Our primary contribution is the direct realization of molecular graphs, a task previously approached by generating linear SMILES strings instead of graphs. Our junction tree variational autoencoder generates molecular graphs in two phases, by first generating a tree-structured scaffold over chemical substructures, and then combining them into a molecule with a graph message passing network. This approach allows us to incrementally expand molecules while maintaining chemical validity at every step. We evaluate our model on multiple tasks ranging from molecular generation to optimization. Across these tasks, our model outperforms previous state-of-the-art baselines by a significant margin.
Network Global Testing by Counting Graphlets
Authors: Jiashun Jin, Zheng Ke, Shengming Luo
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Consider a large social network with possibly severe degree heterogeneity and mixed-memberships. We are interested in testing whether the network has only one community or there are more than one communities. The problem is known to be non-trivial, partially due to the presence of severe degree heterogeneity. We construct a class of test statistics using the numbers of short paths and short cycles, and the key to our approach is a general framework for canceling the effects of degree heterogeneity. The tests compare favorably with existing methods. We support our methods with careful analysis and numerical study with simulated data and a real data example.
Regret Minimization for Partially Observable Deep Reinforcement Learning
Authors: Peter Jin, Kurt Keutzer, Sergey Levine
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Deep reinforcement learning algorithms that estimate state and state-action value functions have been shown to be effective in a variety of challenging domains, including learning control strategies from raw image pixels. However, algorithms that estimate state and state-action value functions typically assume a fully observed state and must compensate for partial observations by using finite length observation histories or recurrent networks. In this work, we propose a new deep reinforcement learning algorithm based on counterfactual regret minimization that iteratively updates an approximation to an advantage-like function and is robust to partially observed state. We demonstrate that this new algorithm can substantially outperform strong baseline methods on several partially observed reinforcement learning tasks: learning first-person 3D navigation in Doom and Minecraft, and acting in the presence of partially observed objects in Doom and Pong.
WSNet: Compact and Efficient Networks Through Weight Sampling
Authors: Xiaojie Jin, Yingzhen Yang, Ning Xu, Jianchao Yang, Nebojsa Jojic, Jiashi Feng, Shuicheng Yan
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We present a new approach and a novel architecture, termed WSNet, for learning compact and efficient deep neural networks. Existing approaches conventionally learn full model parameters independently and then compress them via ad hoc processing such as model pruning or filter factorization. Alternatively, WSNet proposes learning model parameters by sampling from a compact set of learnable parameters, which naturally enforces parameter sharing throughout the learning process. We demonstrate that such a novel weight sampling approach (and induced WSNet) promotes both weights and computation sharing favorably. By employing this method, we can more efficiently learn much smaller networks with competitive performance compared to baseline networks with equal numbers of convolution filters. Specifically, we consider learning compact and efficient 1D convolutional neural networks for audio classification. Extensive experiments on multiple audio classification datasets verify the effectiveness of WSNet. Combined with weight quantization, the resulted models are up to 180x smaller and theoretically up to 16x faster than the well-established baselines, without noticeable performance drop.
Large-Scale Cox Process Inference using Variational Fourier Features
Authors: ST John, James Hensman
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Gaussian process modulated Poisson processes provide a flexible framework for modeling spatiotemporal point patterns. So far this had been restricted to one dimension, binning to a pre-determined grid, or small data sets of up to a few thousand data points. Here we introduce Cox process inference based on Fourier features. This sparse representation induces global rather than local constraints on the function space and is computationally efficient. This allows us to formulate a grid-free approximation that scales well with the number of data points and the size of the domain. We demonstrate that this allows MCMC approximations to the non-Gaussian posterior. In practice, we find that Fourier features have more consistent optimization behavior than previous approaches. Our approximate Bayesian method can fit over 100 000 events with complex spatiotemporal patterns in three dimensions on a single GPU.
Composite Functional Gradient Learning of Generative Adversarial Models
Authors: Rie Johnson, Tong Zhang
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: This paper first presents a theory for generative adversarial methods that does not rely on the traditional minimax formulation. It shows that with a strong discriminator, a good generator can be learned so that the KL divergence between the distributions of real data and generated data improves after each functional gradient step until it converges to zero. Based on the theory, we propose a new stable generative adversarial method. A theoretical insight into the original GAN from this new viewpoint is also provided. The experiments on image generation show the effectiveness of our new method.
Kronecker Recurrent Units
Authors: Cijo Jose, Moustapha Cisse, Francois Fleuret
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Our work addresses two important issues with recurrent neural networks: (1) they are over-parametrized, and (2) the recurrent weight matrix is ill-conditioned. The former increases the sample complexity of learning and the training time. The latter causes the vanishing and exploding gradient problem. We present a flexible recurrent neural network model called Kronecker Recurrent Units (KRU). KRU achieves parameter efficiency in RNNs through a Kronecker factored recurrent matrix. It overcomes the ill-conditioning of the recurrent matrix by enforcing soft unitary constraints on the factors. Thanks to the small dimensionality of the factors, maintaining these constraints is computationally efficient. Our experimental results on seven standard data-sets reveal that KRU can reduce the number of parameters by three orders of magnitude in the recurrent weight matrix compared to the existing recurrent models, without trading the statistical performance. These results in particular show that while there are advantages in having a high dimensional recurrent space, the capacity of the recurrent part of the model can be dramatically reduced.
Fast Decoding in Sequence Models Using Discrete Latent Variables
Authors: Lukasz Kaiser, Samy Bengio, Aurko Roy, Ashish Vaswani, Niki Parmar, Jakob Uszkoreit, Noam Shazeer
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Autoregressive sequence models based on deep neural networks, such as RNNs, Wavenet and Transformer are the state-of-the-art on many tasks. However, they lack parallelism and are thus slow for long sequences. RNNs lack parallelism both during training and decoding, while architectures like WaveNet and Transformer are much more parallel during training, but still lack parallelism during decoding. We present a method to extend sequence models using discrete latent variables that makes decoding much more parallel. The main idea behind this approach is to first autoencode the target sequence into a shorter discrete latent sequence, which is generated autoregressively, and finally decode the full sequence from this shorter latent sequence in a parallel manner. To this end, we introduce a new method for constructing discrete latent variables and compare it with previously introduced methods. Finally, we verify that our model works on the task of neural machine translation, where our models are an order of magnitude faster than comparable autoregressive models and, while lower in BLEU than purely autoregressive models, better than previously proposed non-autogregressive translation.
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
Authors: Takafumi Kajihara, Motonobu Kanagawa, Keisuke Yamazaki, Kenji Fukumizu
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We propose a novel approach to parameter estimation for simulator-based statistical models with intractable likelihood. Our proposed method involves recursive application of kernel ABC and kernel herding to the same observed data. We provide a theoretical explanation regarding why the approach works, showing (for the population setting) that, under a certain assumption, point estimates obtained with this method converge to the true parameter, as recursion proceeds. We have conducted a variety of numerical experiments, including parameter estimation for a real-world pedestrian flow simulator, and show that in most cases our method outperforms existing approaches.
Efficient Neural Audio Synthesis
Authors: Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aaron van den Oord, Sander Dieleman, Koray Kavukcuoglu
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Sequential models achieve state-of-the-art results in audio, visual and textual domains with respect to both estimating the data distribution and generating desired samples. Efficient sampling for this class of models at the cost of little to no loss in quality has however remained an elusive problem. With a focus on text-to-speech synthesis, we describe a set of general techniques for reducing sampling time while maintaining high output quality. We first describe a single-layer recurrent neural network, the WaveRNN, with a dual softmax layer that matches the quality of the state-of-the-art WaveNet model. The compact form of the network makes it possible to generate 24 kHz 16-bit audio 4 times faster than real time on a GPU. Secondly, we apply a weight pruning technique to reduce the number of weights in the WaveRNN. We find that, for a constant number of parameters, large sparse networks perform better than small dense networks and this relationship holds past sparsity levels of more than 96%. The small number of weights in a Sparse WaveRNN makes it possible to sample high-fidelity audio on a mobile phone CPU in real time. Finally, we describe a new dependency scheme for sampling that lets us trade a constant number of non-local, distant dependencies for the ability to generate samples in batches. The Batch WaveRNN produces 8 samples per step without loss of quality and offers orthogonal ways of further increasing sampling efficiency.
Learning Diffusion using Hyperparameters
Authors: Dimitris Kalimeris, Yaron Singer, Karthik Subbian, Udi Weinsberg
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: In this paper we advocate for a hyperparametric approach to learn diffusion in the independent cascade (IC) model. The sample complexity of this model is a function of the number of edges in the network and consequently learning becomes infeasible when the network is large. We study a natural restriction of the hypothesis class using additional information available in order to dramatically reduce the sample complexity of the learning process. In particular we assume that diffusion probabilities can be described as a function of a global hyperparameter and features of the individuals in the network. One of the main challenges with this approach is that training a model reduces to optimizing a non-convex objective. Despite this obstacle, we can shrink the best-known sample complexity bound for learning IC by a factor of |E|/d where |E| is the number of edges in the graph and d is the dimension of the hyperparameter. We show that under mild assumptions about the distribution generating the samples one can provably train a model with low generalization error. Finally, we use large-scale diffusion data from Facebook to show that a hyperparametric model using approximately 20 features per node achieves remarkably high accuracy.
Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit
Authors: Sreejith Kallummil, Sheetal Kalyani
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Orthogonal matching pursuit (OMP) is a widely used algorithm for recovering sparse high dimensional vectors in linear regression models. The optimal performance of OMP requires a priori knowledge of either the sparsity of regression vector or noise statistics. Both these statistics are rarely known a priori and are very difficult to estimate. In this paper, we present a novel technique called residual ratio thresholding (RRT) to operate OMP without any a priori knowledge of sparsity and noise statistics and establish finite sample and large sample support recovery guarantees for the same. Both analytical results and numerical simulations in real and synthetic data sets indicate that RRT has a performance comparable to OMP with a priori knowledge of sparsity and noise statistics.
Residual Unfairness in Fair Machine Learning from Prejudiced Data
Authors: Nathan Kallus, Angela Zhou
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Recent work in fairness in machine learning has proposed adjusting for fairness by equalizing accuracy metrics across groups and has also studied how datasets affected by historical prejudices may lead to unfair decision policies. We connect these lines of work and study the residual unfairness that arises when a fairness-adjusted predictor is not actually fair on the target population due to systematic censoring of training data by existing biased policies. This scenario is particularly common in the same applications where fairness is a concern. We characterize theoretically the impact of such censoring on standard fairness metrics for binary classifiers and provide criteria for when residual unfairness may or may not appear. We prove that, under certain conditions, fairness-adjusted classifiers will in fact induce residual unfairness that perpetuates the same injustices, against the same groups, that biased the data to begin with, thus showing that even state-of-the-art fair machine learning can have a "bias in, bias out" property. When certain benchmark data is available, we show how sample reweighting can estimate and adjust fairness metrics while accounting for censoring. We use this to study the case of Stop, Question, and Frisk (SQF) and demonstrate that attempting to adjust for fairness perpetuates the same injustices that the policy is infamous for.
Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations
Authors: Ashwin Kalyan, Stefan Lee, Anitha Kannan, Dhruv Batra
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Many structured prediction problems (particularly in vision and language domains) are ambiguous, with multiple outputs being ‘correct’ for an input – e.g. there are many ways of describing an image, multiple ways of translating a sentence; however, exhaustively annotating the applicability of all possible outputs is intractable due to exponentially large output spaces (e.g. all English sentences). In practice, these problems are cast as multi-class prediction, with the likelihood of only a sparse set of annotations being maximized – unfortunately penalizing for placing beliefs on plausible but unannotated outputs. We make and test the following hypothesis – for a given input, the annotations of its neighbors may serve as an additional supervisory signal. Specifically, we propose an objective that transfers supervision from neighboring examples. We first study the properties of our developed method in a controlled toy setup before reporting results on multi-label classification and two image-grounded sequence modeling tasks – captioning and question generation. We evaluate using standard task-specific metrics and measures of output diversity, finding consistent improvements over standard maximum likelihood training and other baselines.
Semi-Supervised Learning via Compact Latent Space Clustering
Authors: Konstantinos Kamnitsas, Daniel Castro, Loic Le Folgoc, Ian Walker, Ryutaro Tanno, Daniel Rueckert, Ben Glocker, Antonio Criminisi, Aditya Nori
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation. The key idea is to dynamically create a graph over embeddings of labeled and unlabeled samples of a training batch to capture underlying structure in feature space, and use label propagation to estimate its high and low density regions. We then devise a cost function based on Markov chains on the graph that regularizes the latent space to form a single compact cluster per class, while avoiding to disturb existing clusters during optimization. We evaluate our approach on three benchmarks and compare to state-of-the art with promising results. Our approach combines the benefits of graph-based regularization with efficient, inductive inference, does not require modifications to a network architecture, and can thus be easily applied to existing networks to enable an effective use of unlabeled data.
Policy Optimization with Demonstrations
Authors: Bingyi Kang, Zequn Jie, Jiashi Feng
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Exploration remains a significant challenge to reinforcement learning methods, especially in environments where reward signals are sparse. Recent methods of learning from demonstrations have shown to be promising in overcoming exploration difficulties but typically require considerable high-quality demonstrations that are difficult to collect. We propose to effectively leverage available demonstrations to guide exploration through enforcing occupancy measure matching between the learned policy and current demonstrations, and develop a novel Policy Optimization from Demonstration (POfD) method. We show that POfD induces implicit dynamic reward shaping and brings provable benefits for policy improvement. Furthermore, it can be combined with policy gradient methods to produce state-of-the-art results, as demonstrated experimentally on a range of popular benchmark sparse-reward tasks, even when the demonstrations are few and imperfect.
Improving Sign Random Projections With Additional Information
Authors: Keegan Kang, Weipin Wong
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Sign random projections (SRP) is a technique which allows the user to quickly estimate the angular similarity and inner products between data. We propose using additional information to improve these estimates which is easy to implement and cost efficient. We prove that the variance of our estimator is lower than the variance of SRP. Our proposed method can also be used together with other modifications of SRP, such as Super-Bit LSH (SBLSH). We demonstrate the effectiveness of our method on the MNIST test dataset and the Gisette dataset. We discuss how our proposed method can be extended to random projections or even other hashing algorithms.
Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games
Authors: Ehsan Asadi Kangarshahi, Ya-Ping Hsieh, Mehmet Fatih Sahin, Volkan Cevher
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We revisit the problem of solving two-player zero-sum games in the decentralized setting. We propose a simple algorithmic framework that simultaneously achieves the best rates for honest regret as well as adversarial regret, and in addition resolves the open problem of removing the logarithmic terms in convergence to the value of the game. We achieve this goal in three steps. First, we provide a novel analysis of the optimistic mirror descent (OMD), showing that it can be modified to guarantee fast convergence for both honest regret and value of the game, when the players are playing collaboratively. Second, we propose a new algorithm, dubbed as robust optimistic mirror descent (ROMD), which attains optimal adversarial regret without knowing the time horizon beforehand. Finally, we propose a simple signaling scheme, which enables us to bridge OMD and ROMD to achieve the best of both worlds. Numerical examples are presented to support our theoretical claims and show that our non-adaptive ROMD algorithm can be competitive to OMD with adaptive step-size selection.