pwc-archive/papers-with-abstracts huggingface dataset:
A Primal-Dual link between GANs and Autoencoders
Authors: Hisham Husain, Richard Nock, Robert C. Williamson
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Generalization Bounds

Since the introduction of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAE), the literature on generative modelling has witnessed an overwhelming resurgence. The impressive, yet elusive empirical performance of GANs has lead to the rise of many GAN-VAE hybrids, with the hopes of GAN level performance and additional benefits of VAE, such as an encoder for feature reduction, which is not offered by GANs. Recently, the Wasserstein Autoencoder (WAE) was proposed, achieving performance similar to that of GANs, yet it is still unclear whether the two are fundamentally different or can be further improved into a unified model. In this work, we study the $f$-GAN and WAE models and make two main discoveries. First, we find that the $f$-GAN and WAE objectives partake in a primal-dual relationship and are equivalent under some assumptions, which then allows us to explicate the success of WAE. Second, the equivalence result allows us to, for the first time, prove generalization bounds for Autoencoder models, which is a pertinent problem when it comes to theoretical analyses of generative models. Furthermore, we show that the WAE objective is related to other statistical quantities such as the $f$-divergence and in particular, upper bounded by the Wasserstein distance, which then allows us to tap into existing efficient (regularized) optimal transport solvers. Our findings thus present the first primal-dual relationship between GANs and Autoencoder models, comment on generalization abilities and make a step towards unifying these models.
muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking
Authors: Congchao Wang, Yizhi Wang, Yinxue Wang, Chiung-Ting Wu, Guoqiang Yu
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Multi-Object Tracking, object-detection, Object Detection, Object Tracking

Min-cost flow has been a widely used paradigm for solving data association problems in multi-object tracking (MOT). However, most existing methods of solving min-cost flow problems in MOT are either direct adoption or slight modifications of generic min-cost flow algorithms, yielding sub-optimal computation efficiency and holding the applications back from larger scale of problems. In this paper, by exploiting the special structures and properties of the graphs formulated in MOT problems, we develop an efficient min-cost flow algorithm, namely, minimum-update Successive Shortest Path (muSSP). muSSP is proved to provide exact optimal solution and we demonstrated its efficiency through 40 experiments on five MOT datasets with various object detection results and a number of graph designs. muSSP is always the most efficient in each experiment compared to the three peer solvers, improving the efficiency by 5 to 337 folds relative to the best competing algorithm and averagely 109 to 4089 folds to each of the three peer methods.
Equitable Stable Matchings in Quadratic Time
Authors: Nikolaos Tziavelis, Ioannis Giannakopoulos, Katerina Doka, Nectarios Koziris, Panagiotis Karras
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12

Can a stable matching that achieves high equity among the two sides of a market be reached in quadratic time? The Deferred Acceptance (DA) algorithm finds a stable matching that is biased in favor of one side; optimizing apt equity measures is strongly NP-hard. A proposed approximation algorithm offers a guarantee only with respect to the DA solutions. Recent work introduced Deferred Acceptance with Compensation Chains (DACC), a class of algorithms that can reach any stable matching in O(n^4) time, but did not propose a way to achieve good equity. In this paper, we propose an alternative that is computationally simpler and achieves high equity too. We introduce Monotonic Deferred Acceptance (MDA), a class of algorithms that progresses monotonically towards a stable matching; we couple MDA with a mechanism we call Strongly Deferred Acceptance (SDA), to build an algorithm that reaches an equitable stable matching in quadratic time; we amend this algorithm with a few low-cost local search steps to what we call Deferred Local Search (DLS), and demonstrate experimentally that it outperforms previous solutions in terms of equity measures and matches the most efficient ones in runtime.
AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling
Authors: Bichuan Guo, Yuxing Han, Jiangtao Wen
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Bayesian Inference, Deblurring, Denoising, Image Deblurring, Uncertainty Quantification

In this paper we propose to use a denoising autoencoder (DAE) prior to simultaneously solve a linear inverse problem and estimate its noise parameter. Existing DAE-based methods estimate the noise parameter empirically or treat it as a tunable hyper-parameter. We instead propose autoencoder guided EM, a probabilistically sound framework that performs Bayesian inference with intractable deep priors. We show that efficient posterior sampling from the DAE can be achieved via Metropolis-Hastings, which allows the Monte Carlo EM algorithm to be used. We demonstrate competitive results for signal denoising, image deblurring and image devignetting. Our method is an example of combining the representation power of deep learning with uncertainty quantification from Bayesian statistics.
CPM-Nets: Cross Partial Multi-View Networks
Authors: Changqing Zhang, Zongbo Han, Yajie Cui, Huazhu Fu, Joey Tianyi Zhou, QinGhua Hu
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: MULTI-VIEW LEARNING

Despite multi-view learning progressed fast in past decades, it is still challenging due to the difficulty in modeling complex correlation among different views, especially under the context of view missing. To address the challenge, we propose a novel framework termed Cross Partial Multi-View Networks (CPM-Nets). In this framework, we first give a formal definition of completeness and versatility for multi-view representation and then theoretically prove the versatility of the latent representation learned from our algorithm. To achieve the completeness, the task of learning latent multi-view representation is specifically translated to degradation process through mimicking data transmitting, such that the optimal tradeoff between consistence and complementarity across different views could be achieved. In contrast with methods that either complete missing views or group samples according to view-missing patterns, our model fully exploits all samples and all views to produce structured representation for interpretability. Extensive experimental results validate the effectiveness of our algorithm over existing state-of-the-arts.
Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling
Authors: Andrey Kolobov, Yuval Peres, Cheng Lu, Eric J. Horvitz
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

From traditional Web search engines to virtual assistants and Web accelerators, services that rely on online information need to continually keep track of remote content changes by explicitly requesting content updates from remote sources (e.g., web pages). We propose a novel optimization objective for this setting that has several practically desirable properties, and efficient algorithms for it with optimality guarantees even in the face of mixed content change observability and initially unknown change model parameters. Experiments on 18.5M URLs crawled daily for 14 weeks show significant advantages of this approach over prior art.
RSN: Randomized Subspace Newton
Authors: Robert Gower, Dmitry Koralev, Felix Lieder, Peter Richtarik
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12

We develop a randomized Newton method capable of solving learning problems with huge dimensional feature spaces,  which is a common setting in applications such as medical imaging, genomics and seismology. Our method leverages  randomized sketching in a new way, by finding the Newton direction constrained to the space spanned by a random sketch. We develop a simple global linear convergence theory that holds for practically all sketching techniques, which gives the practitioners the freedom to design custom sketching approaches suitable for particular applications. We perform numerical experiments which demonstrate the efficiency of our method as compared to accelerated gradient descent and the full Newton method. Our method can be seen as a refinement and a randomized extension of the results of Karimireddy, Stich, and Jaggi (2019).
Regret Minimization for Reinforcement Learning with Vectorial Feedback and Complex Objectives
Authors: Wang Chi Cheung
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider an agent who is involved in an online Markov decision process, and receives a vector of outcomes every round. The agent aims to simultaneously  optimize multiple objectives associated with the multi-dimensional outcomes. Due to state transitions, it is challenging to balance the vectorial outcomes for achieving  near-optimality. In particular, contrary to the single objective case, stationary policies are generally sub-optimal. We propose a no-regret algorithm based on the  Frank-Wolfe algorithm (Frank and Wolfe 1956), UCRL2 (Jaksch et al. 2010), as well as a crucial and novel gradient threshold procedure. The procedure involves carefully delaying gradient updates, and returns a non-stationary policy that diversifies the outcomes for optimizing the objectives.
Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression
Authors: Ruidi Chen, Ioannis Paschalidis
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Decision Making, regression

This paper develops a prediction-based prescriptive model for optimal decision
making that (i) predicts the outcome under each action using a robust
nonlinear model, and (ii) adopts a randomized prescriptive policy determined
by the predicted outcomes. The predictive model combines a new regularized
regression technique, which was developed using Distributionally Robust
Optimization (DRO) with an ambiguity set constructed from the Wasserstein
metric, with the K-Nearest Neighbors (K-NN) regression, which helps to
capture the nonlinearity embedded in the data. We show theoretical results
that guarantee the out-of-sample performance of the predictive model, and
prove the optimality of the randomized policy in terms of the expected true
future outcome. We demonstrate the proposed methodology on a hypertension
dataset, showing that our prescribed treatment leads to a larger reduction in
the systolic blood pressure compared to a series of alternatives. A clinically
meaningful threshold level used to activate the randomized policy is also
derived under a sub-Gaussian assumption on the predicted outcome.
Differentiable Cloth Simulation for Inverse Problems
Authors: Junbang Liang, Ming Lin, Vladlen Koltun
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: parameter estimation

We propose a differentiable cloth simulator that can be embedded as a layer in deep neural networks.  This approach provides an effective, robust framework for modeling cloth dynamics, self-collisions, and contacts. Due to the high dimensionality of the dynamical system in modeling cloth, traditional gradient computation for collision response can become impractical. To address this problem, we propose to compute the gradient directly using QR decomposition of a much smaller matrix. Experimental results indicate that our method can speed up backpropagation by two orders of magnitude. We demonstrate the presented approach on a number of inverse problems, including parameter estimation and motion control for cloth.
Volumetric Correspondence Networks for Optical Flow
Authors: Gengshan Yang, Deva Ramanan
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Decoder, Optical Flow Estimation

Many classic tasks in vision -- such as the estimation of optical flow or stereo disparities -- can be cast as dense correspondence matching. Well-known techniques for doing so make use of a cost volume, typically a 4D tensor of match costs between all pixels in a 2D image and their potential matches in a 2D search window. State-of-the-art (SOTA) deep networks for flow/stereo make use of such volumetric representations as internal layers. However, such layers require significant amounts of memory and compute, making them cumbersome to use in practice. As a result, SOTA networks also employ various heuristics designed to limit volumetric processing, leading to limited accuracy and overfitting. Instead, we introduce several simple modifications that dramatically simplify the use of volumetric layers - (1) volumetric encoder-decoder architectures that efficiently capture large receptive fields, (2) multi-channel cost volumes that capture multi-dimensional notions of pixel similarities, and finally, (3) separable volumetric filtering that significantly reduces computation and parameters while preserving accuracy. Our innovations dramatically improve accuracy over SOTA on standard benchmarks while being significantly easier to work with - training converges in 10X fewer iterations, and most importantly, our networks generalize across correspondence tasks. On-the-fly adaptation of search windows allows us to repurpose optical flow networks for stereo (and vice versa), and can also be used to implement adaptive networks that increase search window sizes on-demand.
RUBi: Reducing Unimodal Biases for Visual Question Answering
Authors: Remi Cadene, Corentin Dancette, Hedi Ben Younes, Matthieu Cord, Devi Parikh
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual Question Answering (VQA) is the task of answering questions about an
image.
Some VQA models often exploit unimodal biases to provide the correct answer without using the image information.
As a result, they suffer from a huge drop in performance when evaluated on data outside their training set distribution. This critical issue makes them unsuitable for real-world settings.

We propose RUBi, a new learning strategy to reduce biases in any VQA model.
It reduces the importance of the most biased examples, i.e. examples that can be correctly classified without looking at the image. 
It implicitly forces the VQA model to use the two input modalities instead of relying on statistical regularities between the question and the answer.
We leverage a question-only model that captures the language biases by identifying when these unwanted regularities are used.
It prevents the base VQA model from learning them by influencing its predictions. This leads to dynamically adjusting the loss in order to compensate for biases. 
We validate our contributions by surpassing the current state-of-the-art results on VQA-CP v2. This dataset is specifically designed to assess the robustness of VQA models when exposed to different question biases at test time than what was seen during training.
DATA: Differentiable ArchiTecture Approximation
Authors: Jianlong Chang, Xinbang Zhang, Yiwen Guo, Gaofeng Meng, Shiming Xiang, Chunhong Pan
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: image-classification, Image Classification, Language Modeling, Language Modelling, Neural Architecture Search, Semantic Segmentation

Neural architecture search (NAS) is inherently subject to the gap of architectures during searching and validating. To bridge this gap, we develop Differentiable ArchiTecture Approximation (DATA) with an Ensemble Gumbel-Softmax (EGS) estimator to automatically approximate architectures during searching and validating in a differentiable manner. Technically, the EGS estimator consists of a group of Gumbel-Softmax estimators, which is capable of converting probability vectors to binary codes and passing gradients from binary codes to probability vectors. Benefiting from such modeling, in searching, architecture parameters and network weights in the NAS model can be jointly optimized with the standard back-propagation, yielding an end-to-end learning mechanism for searching deep models in a large enough search space. Conclusively, during validating, a high-performance architecture that approaches to the learned one during searching is readily built. Extensive experiments on a variety of popular datasets strongly evidence that our method is capable of discovering high-performance architectures for image classification, language modeling and semantic segmentation, while guaranteeing the requisite efficiency during searching.
Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge
Authors: Tingting Qiao, Jing Zhang, Duanqing Xu, DaCheng Tao
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Image Generation, Text to Image Generation, Text-to-Image Generation

Text-to-image generation, i.e. generating an image given a text description, is a very challenging task due to the significant semantic gap between the two domains. Humans, however, tackle this problem intelligently. We learn from diverse objects to form a solid prior about semantics, textures, colors, shapes, and layouts. Given a text description, we immediately imagine an overall visual impression using this prior and, based on this, we draw a picture by progressively adding more and more details. In this paper, and inspired by this process, we propose a novel text-to-image method called LeicaGAN to combine the above three phases in a unified framework. First, we formulate the multiple priors learning phase as a textual-visual co-embedding (TVE) comprising a text-image encoder for learning semantic, texture, and color priors and a text-mask encoder for learning shape and layout priors. Then, we formulate the imagination phase as multiple priors aggregation (MPA) by combining these complementary priors and adding noise for diversity. Lastly, we formulate the creation phase by using a cascaded attentive generator (CAG) to progressively draw a picture from coarse to fine. We leverage adversarial learning for LeicaGAN to enforce semantic consistency and visual realism. Thorough experiments on two public benchmark datasets demonstrate LeicaGAN's superiority over the baseline method. Code has been made available at https://github.com/qiaott/LeicaGAN.
Memory-oriented Decoder for Light Field Salient Object Detection
Authors: Miao Zhang, Jingjing Li, Ji Wei, Yongri Piao, Huchuan Lu
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Decoder, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection

Light field data have been demonstrated in favor of many tasks in computer vision, but existing works about light field saliency detection still rely on hand-crafted features. In this paper, we present a deep-learning-based method where a novel memory-oriented decoder is tailored for light field saliency detection. Our goal is to deeply explore and comprehensively exploit internal correlation of focal slices for accurate prediction by designing feature fusion and integration mechanisms. The success of our method is demonstrated by achieving the state of the art on three datasets. We present this problem in a way that is accessible to members of the community and provide a large-scale light field dataset that facilitates comparisons across algorithms. The code and dataset will be made publicly available.
Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels
Authors: Natalia Neverova, David Novotny, Andrea Vedaldi
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12

Many machine learning methods depend on human supervision to achieve optimal performance. However, in tasks such as DensePose, where the goal is to establish dense visual correspondences between images, the quality of manual annotations is intrinsically limited. We address this issue by augmenting neural network predictors with the ability to output a distribution over labels, thus explicitly and introspectively capturing the aleatoric uncertainty in the annotations.
Compared to previous works, we show that correlated error fields arise naturally in applications such as DensePose and these fields can be modeled by deep networks, leading to a better understanding of the annotation errors.
We show that these models, by understanding uncertainty better, can solve the original DensePose task more accurately, thus setting the new state-of-the-art accuracy in this benchmark.
Finally, we demonstrate the utility of the uncertainty estimates in fusing the predictions of produced by multiple models, resulting in a better and more principled approach to model ensembling which can further improve accuracy.
Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer
Authors: Arsenii Vanunts, Alexey Drutsa
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12

We study revenue optimization pricing algorithms for repeated posted-price auctions where a seller interacts with a single strategic buyer that holds a fixed private valuation.
When the participants non-equally discount their cumulative utilities, we show that the optimal constant pricing (which offers the Myerson price) is no longer optimal. 
In the case of more patient seller, we propose a novel multidimensional optimization functional --- a generalization of the one used to determine Myerson's price. This functional allows to find the optimal algorithm and to boost revenue of the optimal static pricing by an efficient low-dimensional approximation.
Numerical experiments are provided to support our results.
A Condition Number for Joint Optimization of Cycle-Consistent Networks
Authors: Leonidas J. Guibas, Qi-Xing Huang, Zhenxiao Liang
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12

A recent trend in optimizing maps such as dense correspondences between objects or neural networks between pairs of domains is to optimize them jointly. In this context, there is a natural \textsl{cycle-consistency} constraint, which regularizes composite maps associated with cycles, i.e., they are forced to be identity maps. However, as there is an exponential number of cycles in a graph, how to sample a subset of cycles becomes critical for efficient and effective enforcement of the cycle-consistency constraint. This paper presents an algorithm that select a subset of weighted cycles to minimize a condition number of the induced joint optimization problem. Experimental results on benchmark datasets justify the effectiveness of our approach for optimizing dense correspondences between 3D shapes and neural networks for predicting dense image flows.
Polynomial Cost of Adaptation for X-Armed Bandits
Authors: Hedi Hadiji
Date: 2019-12-01 00:00:00
Proceeding: NeurIPS 2019 12

In the context of stochastic continuum-armed bandits, we present an algorithm that adapts to the unknown smoothness of the objective function. We exhibit and compute a polynomial cost of adaptation to the Hölder regularity for regret minimization. To do this, we first reconsider the recent lower bound of Locatelli and Carpentier, 2018, and define and characterize admissible rate functions. Our new algorithm matches any of these minimal rate functions. We provide a finite-time analysis and a thorough discussion about asymptotic optimality.