pwc-archive/papers-with-abstracts huggingface dataset:
Stochastic Nonparametric Event-Tensor Decomposition
Authors: Shandian Zhe, Yishuai Du
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Tensor Decomposition

Tensor decompositions are fundamental tools for multiway data analysis. Existing approaches, however, ignore the valuable temporal information along with data, or simply discretize them into time steps so that important temporal patterns are easily missed. Moreover, most methods are limited to multilinear decomposition forms, and hence are unable to capture intricate, nonlinear relationships in data. To address these issues, we formulate event-tensors, to preserve the complete temporal information for multiway data, and propose a novel Bayesian nonparametric decomposition model. Our model can (1) fully exploit the time stamps to capture the critical, causal/triggering effects between the interaction events,  (2) flexibly estimate the complex relationships between the entities in tensor modes, and (3) uncover hidden structures from their temporal interactions. For scalable inference, we develop a doubly stochastic variational Expectation-Maximization algorithm to conduct an online decomposition. Evaluations on both synthetic and real-world datasets show that our model not only improves upon the predictive performance of existing methods, but also discovers interesting clusters underlying the data.
Bayesian Adversarial Learning
Authors: Nanyang Ye, Zhanxing Zhu
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12

Deep neural networks have been known to be vulnerable to adversarial attacks, raising lots of security concerns in the practical deployment. Popular defensive approaches can be formulated as a (distributionally) robust optimization problem, which minimizes a ``point estimate'' of worst-case loss derived from either per-datum perturbation or adversary data-generating distribution within certain pre-defined constraints. This point estimate ignores potential test adversaries that are beyond the pre-defined constraints. The model robustness might deteriorate sharply in the scenario of stronger test adversarial data. In this work, a novel robust training framework is proposed to alleviate this issue, Bayesian Robust Learning, in which a  distribution is put on the adversarial data-generating distribution to account for the uncertainty of the adversarial data-generating process. The uncertainty directly helps to consider the potential adversaries that are stronger than the point estimate in the cases of distributionally robust optimization. The uncertainty of model parameters is also incorporated to accommodate the full Bayesian framework. We design a scalable Markov Chain Monte Carlo sampling strategy to obtain the posterior distribution over model parameters. Various experiments are conducted to verify the superiority of BAL over existing adversarial training methods. The code for BAL is available at \url{https://tinyurl.com/ycxsaewr
}.
Efficient Convex Completion of Coupled Tensors using Coupled Nuclear Norms
Authors: Kishan Wimalawarne, Hiroshi Mamitsuka
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12

Coupled norms have emerged as a convex method to solve coupled tensor completion. A limitation with coupled norms is that they only induce low-rankness using the multilinear rank of coupled tensors. In this paper, we introduce a new set of coupled norms known as coupled nuclear norms by constraining the CP rank of coupled tensors. We propose new coupled completion models using the coupled nuclear norms as regularizers, which can be optimized using computationally efficient optimization methods. We derive excess risk bounds for proposed coupled completion models and show that proposed norms lead to better performance. Through simulation and real-data experiments, we demonstrate that proposed norms achieve better performance for coupled completion compared to existing coupled norms.
Maximizing Induced Cardinality Under a Determinantal Point Process
Authors: Jennifer A. Gillenwater, Alex Kulesza, Sergei Vassilvitskii, Zelda E. Mariet
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Point Processes, Recommendation Systems

Determinantal point processes (DPPs) are well-suited to recommender systems where the goal is to generate collections of diverse, high-quality items. In the existing literature this is usually formulated as finding the mode of the DPP (the so-called MAP set). However, the MAP objective inherently assumes that the DPP models "optimal" recommendation sets, and yet obtaining such a DPP is nontrivial when there is no ready source of example optimal sets. In this paper we advocate an alternative framework for applying DPPs to recommender systems. Our approach assumes that the DPP simply models user engagements with recommended items, which is more consistent with how DPPs for recommender systems are typically trained.  With this assumption, we are able to formulate a metric that measures the expected number of items that a user will engage with.  We formalize this optimization of this metric as the Maximum Induced Cardinality (MIC) problem. Although the MIC objective is not submodular, we show that it can be approximated by a submodular function, and that empirically it is well-optimized by a greedy algorithm.
rho-POMDPs have Lipschitz-Continuous epsilon-Optimal Value Functions
Authors: Mathieu Fehr, Olivier Buffet, Vincent Thomas, Jilles Dibangoye
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12

Many state-of-the-art algorithms for solving Partially Observable Markov Decision Processes (POMDPs) rely on turning the problem into a “fully observable” problem—a belief MDP—and exploiting the piece-wise linearity and convexity (PWLC) of the optimal value function in this new state space (the belief simplex ∆). This approach has been extended to solving ρ-POMDPs—i.e., for information-oriented criteria—when the reward ρ is convex in ∆. General ρ-POMDPs can also be turned into “fully observable” problems, but with no means to exploit the PWLC property. In this paper, we focus on POMDPs and ρ-POMDPs with λ ρ -Lipschitz reward function, and demonstrate that, for finite horizons, the optimal value function is Lipschitz-continuous. Then, value function approximators are proposed for both upper- and lower-bounding the optimal value function, which are shown to provide uniformly improvable bounds. This allows proposing two algorithms derived from HSVI which are empirically evaluated on various benchmark problems.
Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks
Authors: Agastya Kalra, Abdullah Rashwan, Wei-Shou Hsu, Pascal Poupart, Prashant Doshi, Georgios Trimponias
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: valid

Sum-product networks have recently emerged as an attractive representation due to their dual view as a special type of deep neural network with clear semantics and a special type of probabilistic graphical model for which inference is always tractable. Those properties follow from some conditions (i.e., completeness and decomposability) that must be respected by the structure of the network.  As a result, it is not easy to specify a valid sum-product network by hand and therefore structure learning techniques are typically used in practice.  This paper describes a new online structure learning technique for feed-forward and recurrent SPNs. The algorithm is demonstrated on real-world datasets with continuous features for which it is not clear what network architecture might be best, including sequence datasets of varying length.
Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation
Authors: Kush Bhatia, Aldo Pacchiano, Nicolas Flammarion, Peter L. Bartlett, Michael. I. Jordan
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12

In this paper, we study the problems of principle Generalized Eigenvector computation and Canonical Correlation Analysis in the stochastic setting. We propose a simple and efficient algorithm for these problems. We prove the global convergence of our algorithm, borrowing ideas from the theory of fast-mixing Markov chains and two-Time-Scale Stochastic Approximation, showing that it achieves the optimal rate of convergence. In the process, we develop tools for understanding stochastic processes with Markovian noise which might be of independent interest.
Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks
Authors: Anirvan Sengupta, Cengiz Pehlevan, Mariano Tepper, Alexander Genkin, Dmitri Chklovskii
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Hippocampus

Many neurons in the brain, such as place cells in the rodent hippocampus, have localized receptive fields, i.e., they respond to a small neighborhood of stimulus space. What is the functional significance of such representations and how can they arise? Here, we propose that localized receptive fields emerge in similarity-preserving networks of rectifying neurons that learn low-dimensional manifolds populated by sensory inputs.  Numerical simulations of such networks on standard datasets yield manifold-tiling localized receptive fields. More generally, we show analytically that, for data lying on symmetric manifolds, optimal solutions of objectives, from which similarity-preserving networks are derived, have localized receptive fields. Therefore, nonnegative similarity-preserving mapping (NSM) implemented by neural networks can model representations of continuous manifolds in the brain.
Model-Agnostic Private Learning
Authors: Raef Bassily, Abhradeep Guha Thakurta, Om Dipakbhai Thakkar
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: General Classification, model, Transfer Learning

We design differentially private learning algorithms that are agnostic to the learning model assuming access to limited amount of unlabeled public data. First, we give a new differentially private algorithm for answering a sequence of $m$ online classification queries (given by a sequence of $m$ unlabeled public feature vectors) based on a private training set. Our private algorithm follows the paradigm of subsample-and-aggregate, in which any generic non-private learner is trained on disjoint subsets of the private training set, then for each classification query, the votes of the resulting classifiers ensemble are aggregated in a differentially private fashion. Our private aggregation is based on a novel combination of distance-to-instability framework [Smith & Thakurta 2013] and the sparse-vector technique [Dwork et al. 2009, Hardt & Talwar 2010].  We show that our algorithm makes a conservative use of the privacy budget. In particular, if the underlying non-private learner yields classification error at most $\alpha\in (0, 1)$, then our construction answers more queries, by at least a factor of $1/\alpha$ in some cases, than what is implied by a straightforward application of the advanced composition theorem for differential privacy. Next, we apply the knowledge transfer technique to construct a private learner that outputs a classifier, which can be used to answer unlimited number of queries. In the PAC model, we analyze our construction and prove upper bounds on the sample complexity for both the realizable and the non-realizable cases. As in non-private sample complexity, our bounds are completely characterized by the VC dimension of the concept class.
Provably Correct Automatic Sub-Differentiation for Qualified Programs
Authors: Sham M. Kakade, Jason D. Lee
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12

The \emph{Cheap Gradient Principle}~\citep{Griewank:2008:EDP:1455489} --- the computational cost of computing a $d$-dimensional vector of  partial derivatives of a scalar function is nearly the same (often within a factor of $5$)  as that of simply computing the scalar function itself --- is of central importance in optimization; it allows us to quickly obtain (high-dimensional) gradients of scalar loss functions which are subsequently used in black box gradient-based optimization procedures. The current state of affairs is markedly different with regards to computing sub-derivatives: widely used ML libraries, including TensorFlow and PyTorch, do \emph{not} correctly compute (generalized) sub-derivatives even on simple differentiable examples. This work considers the question: is there a \emph{Cheap Sub-gradient Principle}?  Our main result shows that, under certain restrictions on our library of non-smooth functions (standard in non-linear programming), provably correct generalized sub-derivatives can be computed at a computational cost that is within a (dimension-free) factor of $6$ of the cost of computing the scalar function itself.
Deep Homogeneous Mixture Models: Representation, Separation, and Approximation
Authors: Priyank Jaini, Pascal Poupart, Yao-Liang Yu
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Density Estimation

At their core, many unsupervised learning models provide a compact representation of homogeneous density mixtures, but their similarities and differences are not always clearly understood. In this work, we formally establish the relationships among latent tree graphical models (including special cases such as hidden Markov models and tensorial mixture models), hierarchical tensor formats and sum-product networks. Based on this connection, we then give a unified treatment of exponential separation in \emph{exact} representation size between deep mixture architectures and shallow ones. In contrast, for \emph{approximate} representation, we show that the conditional gradient algorithm can approximate any homogeneous mixture within $\epsilon$ accuracy by combining $O(1/\epsilon^2)$ ``shallow'' architectures, where the hidden constant may decrease (exponentially) with respect to the depth. Our experiments on both synthetic and real datasets confirm the benefits of depth in density estimation.
Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks
Authors: Grant Rotskoff, Eric Vanden-Eijnden
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12

The performance of neural networks on high-dimensional data
  distributions suggests that it may be possible to parameterize a
  representation of a given high-dimensional function with
  controllably small errors, potentially outperforming standard
  interpolation methods.  We demonstrate, both theoretically and
  numerically, that this is indeed the case.  We map the parameters of
  a neural network to a system of particles relaxing with an
  interaction potential determined by the loss function.  We show that
  in the limit that the number of parameters $n$ is large, the
  landscape of the mean-squared error becomes convex and the
  representation error in the function scales as $O(n^{-1})$.
  In this limit, we prove a dynamical variant of the universal
  approximation theorem showing that the optimal
  representation can be attained by stochastic gradient
  descent, the algorithm ubiquitously used for parameter optimization
  in machine learning.  In the asymptotic regime, we study the
  fluctuations around the optimal representation and show that they
  arise at a scale $O(n^{-1})$.  These fluctuations in the landscape
  identify the natural scale for the noise in stochastic gradient
  descent.  Our results apply to both single and multi-layer neural
  networks, as well as standard kernel methods like radial basis
  functions.
End-to-End Differentiable Physics for Learning and Control
Authors: Filipe de Avila Belbute-Peres, Kevin Smith, Kelsey Allen, Josh Tenenbaum, J. Zico Kolter
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12

We present a differentiable physics engine that can be integrated as a module in deep neural networks for end-to-end learning.  As a result, structured physics knowledge can be embedded into larger systems, allowing them, for example, to match observations by performing precise simulations, while achieves high sample efficiency.  Specifically, in this paper we demonstrate how to perform backpropagation analytically through a physical simulator defined via a linear complementarity problem.  Unlike traditional finite difference methods, such gradients can be computed analytically, which allows for greater flexibility of the engine. Through experiments in diverse domains, we highlight the system's ability to learn physical parameters from data, efficiently match and simulate observed visual behavior, and readily enable control via gradient-based planning methods. Code for the engine and experiments is included with the paper.
Stimulus domain transfer in recurrent models for large scale cortical population prediction on video
Authors: Fabian Sinz, Alexander S. Ecker, Paul Fahey, Edgar Walker, Erick Cobos, Emmanouil Froudarakis, Dimitri Yatsenko, Zachary Pitkow, Jacob Reimer, Andreas Tolias
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Pupil Dilation

To better understand the representations in visual cortex, we need to generate better predictions of neural activity in awake animals presented with their ecological input: natural video. Despite recent advances in models for static images, models for predicting responses to natural video are scarce and standard linear-nonlinear models perform poorly. We developed a new deep recurrent network architecture that predicts inferred spiking activity of thousands of mouse V1 neurons simultaneously recorded with two-photon microscopy, while accounting for confounding factors such as the animal's gaze position and brain state changes related to running state and pupil dilation. Powerful system identification models provide an opportunity to gain insight into cortical functions through in silico experiments that can subsequently be tested in the brain. However, in many cases this approach requires that the model is able to generalize to stimulus statistics that it was not trained on, such as band-limited noise and other parameterized stimuli. We investigated these domain transfer properties in our model and find that our model trained on natural images is able to correctly predict the orientation tuning of neurons in responses to artificial noise stimuli. Finally, we show that we can fully generalize from movies to noise and maintain high predictive performance on both stimulus domains by fine-tuning only the final layer's weights on a network otherwise trained on natural movies. The converse, however, is not true.
Distributed Multi-Player Bandits - a Game of Thrones Approach
Authors: Ilai Bistritz, Amir Leshem
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12

We consider a multi-armed bandit game where N players compete for K arms for T turns. Each player has different expected rewards for the arms, and the instantaneous rewards are independent and identically distributed. Performance is measured using the expected sum of regrets, compared to the optimal assignment of arms to players. We assume that each player only knows her actions and the reward she received each turn. Players cannot observe the actions of other players, and no communication between players is possible. We present a distributed algorithm and prove that it achieves an expected sum of regrets of near-O\left(\log^{2}T\right). This is the first algorithm to achieve a poly-logarithmic regret in this fully distributed scenario. All other works have assumed that either all players have the same vector of expected rewards or that communication between players is possible.
Training Deep Models Faster with Robust, Approximate Importance Sampling
Authors: Tyler B. Johnson, Carlos Guestrin
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12

In theory, importance sampling speeds up stochastic gradient algorithms for supervised learning by prioritizing training examples. In practice, the cost of computing importances greatly limits the impact of importance sampling. We propose a robust, approximate importance sampling procedure (RAIS) for stochastic gradient de- scent. By approximating the ideal sampling distribution using robust optimization, RAIS provides much of the benefit of exact importance sampling with drastically reduced overhead. Empirically, we find RAIS-SGD and standard SGD follow similar learning curves, but RAIS moves faster through these paths, achieving speed-ups of at least 20% and sometimes much more.
Bandit Learning with Implicit Feedback
Authors: Yi Qi, Qingyun Wu, Hongning Wang, Jie Tang, Maosong Sun
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Bayesian Inference, Thompson Sampling

Implicit feedback, such as user clicks, although abundant in online information service systems, does not provide substantial evidence on users' evaluation of system's output. Without proper modeling, such incomplete supervision inevitably misleads model estimation, especially in a bandit learning setting where the feedback is acquired on the fly. In this work, we perform contextual bandit learning with implicit feedback by modeling the feedback as a composition of user result examination and relevance judgment. Since users' examination behavior is unobserved, we introduce latent variables to model it. We perform Thompson sampling on top of variational Bayesian inference for arm selection and model update. Our upper regret bound analysis of the proposed algorithm proves its feasibility of learning from implicit feedback in a bandit setting; and extensive empirical evaluations on click logs collected from a major MOOC platform further demonstrate its learning effectiveness in practice.
Streaming Kernel PCA with \tilde{O}(\sqrt{n}) Random Features
Authors: Md Enayat Ullah, Poorya Mianjy, Teodor Vanislavov Marinov, Raman Arora
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12

We study the statistical and computational aspects of kernel principal component analysis using random Fourier features and show that under mild assumptions, $O(\sqrt{n} \log n)$ features suffices to achieve $O(1/\epsilon^2)$ sample complexity. Furthermore, we give a memory efficient streaming algorithm based on classical Oja's algorithm that achieves this rate
REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis
Authors: Yu-Shao Peng, Kai-Fu Tang, Hsuan-Tien Lin, Edward Chang
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper proposes REFUEL, a reinforcement learning method with two techniques: {\em reward shaping} and {\em feature rebuilding}, to improve the performance of online symptom checking for disease diagnosis. Reward shaping can guide the search of policy towards better directions. Feature rebuilding can guide the agent to learn correlations between features. Together, they can find symptom queries that can yield positive responses from a patient with high probability. Experimental results justify that the two techniques in REFUEL allows the symptom checker to identify the disease more rapidly and accurately.
Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem
Authors: Victor-Emmanuel Brunel
Date: 2018-12-01 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Point Processes

Symmetric determinantal point processes (DPP) are a class of probabilistic models that encode the random selection of items that have a repulsive behavior. They have attracted a lot of attention in machine learning, where returning diverse sets of items is sought for. Sampling and learning these symmetric DPP's is pretty well understood. In this work, we consider a new class of DPP's, which we call signed DPP's, where we break the symmetry and allow attractive behaviors. We set the ground for learning signed DPP's through a method of moments, by solving the so called principal assignment problem for a class of matrices $K$ that satisfy $K_{i,j}=\pm K_{j,i}$, $i\neq j$, in polynomial time.