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Uncovering Causality from Multivariate Hawkes Integrated Cumulants | https://proceedings.mlr.press/v70/achab17a.html | [
"Massil Achab",
"Emmanuel Bacry",
"Stéphane Gaı̈ffas",
"Iacopo Mastromatteo",
"Jean-François Muzy"
] | null | null | We design a new nonparametric method that allows one to estimate the matrix of integrated kernels of a multivariate Hawkes process. This matrix not only encodes the mutual influences of each node of the process, but also disentangles the causality relationships between them. Our approach is the first that leads to an e... | [] | null | 1 | 1607.06333 | title_snapshot | [
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A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions | https://proceedings.mlr.press/v70/acharya17a.html | [
"Jayadev Acharya",
"Hirakendu Das",
"Alon Orlitsky",
"Ananda Theertha Suresh"
] | null | null | Symmetric distribution properties such as support size, support coverage, entropy, and proximity to uniformity, arise in many applications. Recently, researchers applied different estimators and analysis tools to derive asymptotically sample-optimal approximations for each of these properties. We show that a single, si... | [] | null | 2 | null | null | [
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Constrained Policy Optimization | https://proceedings.mlr.press/v70/achiam17a.html | [
"Joshua Achiam",
"David Held",
"Aviv Tamar",
"Pieter Abbeel"
] | null | null | For many applications of reinforcement learning it can be more convenient to specify both a reward function and constraints, rather than trying to design behavior through the reward function. For example, systems that physically interact with or around humans should satisfy safety constraints. Recent advances in policy... | [] | null | 3 | 1705.10528 | title_snapshot | [
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The Price of Differential Privacy for Online Learning | https://proceedings.mlr.press/v70/agarwal17a.html | [
"Naman Agarwal",
"Karan Singh"
] | null | null | We design differentially private algorithms for the problem of online linear optimization in the full information and bandit settings with optimal $O(T^{0.5})$ regret bounds. In the full-information setting, our results demonstrate that $\epsilon$-differential privacy may be ensured for free – in particular, the regret... | [] | null | 4 | 1701.07953 | title_snapshot | [
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Local Bayesian Optimization of Motor Skills | https://proceedings.mlr.press/v70/akrour17a.html | [
"Riad Akrour",
"Dmitry Sorokin",
"Jan Peters",
"Gerhard Neumann"
] | null | null | Bayesian optimization is renowned for its sample efficiency but its application to higher dimensional tasks is impeded by its focus on global optimization. To scale to higher dimensional problems, we leverage the sample efficiency of Bayesian optimization in a local context. The optimization of the acquisition function... | [] | null | 5 | null | null | [
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Connected Subgraph Detection with Mirror Descent on SDPs | https://proceedings.mlr.press/v70/aksoylar17a.html | [
"Cem Aksoylar",
"Lorenzo Orecchia",
"Venkatesh Saligrama"
] | null | null | We propose a novel, computationally efficient mirror-descent based optimization framework for subgraph detection in graph-structured data. Our aim is to discover anomalous patterns present in a connected subgraph of a given graph. This problem arises in many applications such as detection of network intrusions, communi... | [] | null | 6 | null | null | [
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Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis | https://proceedings.mlr.press/v70/alaa17a.html | [
"Ahmed M. Alaa",
"Scott Hu",
"Mihaela Schaar"
] | null | null | Critically ill patients in regular wards are vulnerable to unanticipated adverse events which require prompt transfer to the intensive care unit (ICU). To allow for accurate prognosis of deteriorating patients, we develop a novel continuous-time probabilistic model for a monitored patient’s temporal sequence of physiol... | [] | null | 7 | 1705.05267 | title_snapshot | [
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A Semismooth Newton Method for Fast, Generic Convex Programming | https://proceedings.mlr.press/v70/ali17a.html | [
"Alnur Ali",
"Eric Wong",
"J. Zico Kolter"
] | null | null | We introduce Newton-ADMM, a method for fast conic optimization. The basic idea is to view the residuals of consecutive iterates generated by the alternating direction method of multipliers (ADMM) as a set of fixed point equations, and then use a nonsmooth Newton method to find a solution; we apply the basic idea to the... | [] | null | 8 | 1705.00772 | title_snapshot | [
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Learning Continuous Semantic Representations of Symbolic Expressions | https://proceedings.mlr.press/v70/allamanis17a.html | [
"Miltiadis Allamanis",
"Pankajan Chanthirasegaran",
"Pushmeet Kohli",
"Charles Sutton"
] | null | null | Combining abstract, symbolic reasoning with continuous neural reasoning is a grand challenge of representation learning. As a step in this direction, we propose a new architecture, called neural equivalence network, for the problem of learning continuous semantic representations of algebraic and logical expressions. Th... | [] | null | 9 | 1611.01423 | title_snapshot | [
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Natasha: Faster Non-Convex Stochastic Optimization via Strongly Non-Convex Parameter | https://proceedings.mlr.press/v70/allen-zhu17a.html | [
"Zeyuan Allen-Zhu"
] | null | null | Given a non-convex function $f(x)$ that is an average of $n$ smooth functions, we design stochastic first-order methods to find its approximate stationary points. The performance of our new methods depend on the smallest (negative) eigenvalue $-\sigma$ of the Hessian. This parameter $\sigma$ captures how strongly non-c... | [] | null | 10 | 1702.00763 | title_snapshot | [
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Doubly Accelerated Methods for Faster CCA and Generalized Eigendecomposition | https://proceedings.mlr.press/v70/allen-zhu17b.html | [
"Zeyuan Allen-Zhu",
"Yuanzhi Li"
] | null | null | We study k-GenEV, the problem of finding the top k generalized eigenvectors, and k-CCA, the problem of finding the top k vectors in canonical-correlation analysis. We propose algorithms LazyEV and LazyCCA to solve the two problems with running times linearly dependent on the input size and on k. Furthermore, our algori... | [] | null | 11 | 1607.06017 | title_snapshot | [
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Faster Principal Component Regression and Stable Matrix Chebyshev Approximation | https://proceedings.mlr.press/v70/allen-zhu17c.html | [
"Zeyuan Allen-Zhu",
"Yuanzhi Li"
] | null | null | We solve principal component regression (PCR), up to a multiplicative accuracy $1+\gamma$, by reducing the problem to $\tilde{O}(\gamma^{-1})$ black-box calls of ridge regression. Therefore, our algorithm does not require any explicit construction of the top principal components, and is suitable for large-scale PCR ins... | [] | null | 12 | 1608.04773 | title_snapshot | [
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Follow the Compressed Leader: Faster Online Learning of Eigenvectors and Faster MMWU | https://proceedings.mlr.press/v70/allen-zhu17d.html | [
"Zeyuan Allen-Zhu",
"Yuanzhi Li"
] | null | null | The online problem of computing the top eigenvector is fundamental to machine learning. The famous matrix-multiplicative-weight-update (MMWU) framework solves this online problem and gives optimal regret. However, since MMWU runs very slow due to the computation of matrix exponentials, researchers proposed the follow-t... | [] | null | 13 | 1701.01722 | title_snapshot | [
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Near-Optimal Design of Experiments via Regret Minimization | https://proceedings.mlr.press/v70/allen-zhu17e.html | [
"Zeyuan Allen-Zhu",
"Yuanzhi Li",
"Aarti Singh",
"Yining Wang"
] | null | null | We consider computationally tractable methods for the experimental design problem, where k out of n design points of dimension p are selected so that certain optimality criteria are approximately satisfied. Our algorithm finds a $(1+\epsilon)$-approximate optimal design when k is a linear function of p; in contrast, ex... | [] | null | 14 | null | null | [
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OptNet: Differentiable Optimization as a Layer in Neural Networks | https://proceedings.mlr.press/v70/amos17a.html | [
"Brandon Amos",
"J. Zico Kolter"
] | null | null | This paper presents OptNet, a network architecture that integrates optimization problems (here, specifically in the form of quadratic programs) as individual layers in larger end-to-end trainable deep networks. These layers encode constraints and complex dependencies between the hidden states that traditional convoluti... | [] | null | 15 | 1703.00443 | title_snapshot | [
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Input Convex Neural Networks | https://proceedings.mlr.press/v70/amos17b.html | [
"Brandon Amos",
"Lei Xu",
"J. Zico Kolter"
] | null | null | This paper presents the input convex neural network architecture. These are scalar-valued (potentially deep) neural networks with constraints on the network parameters such that the output of the network is a convex function of (some of) the inputs. The networks allow for efficient inference via optimization over some ... | [] | null | 16 | 1609.07152 | title_snapshot | [
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An Efficient, Sparsity-Preserving, Online Algorithm for Low-Rank Approximation | https://proceedings.mlr.press/v70/anderson17a.html | [
"David Anderson",
"Ming Gu"
] | null | null | Low-rank matrix approximation is a fundamental tool in data analysis for processing large datasets, reducing noise, and finding important signals. In this work, we present a novel truncated LU factorization called Spectrum-Revealing LU (SRLU) for effective low-rank matrix approximation, and develop a fast algorithm to ... | [] | null | 17 | 1602.05950 | title_snapshot | [
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Modular Multitask Reinforcement Learning with Policy Sketches | https://proceedings.mlr.press/v70/andreas17a.html | [
"Jacob Andreas",
"Dan Klein",
"Sergey Levine"
] | null | null | We describe a framework for multitask deep reinforcement learning guided by policy sketches. Sketches annotate tasks with sequences of named subtasks, providing information about high-level structural relationships among tasks but not how to implement them—specifically not providing the detailed guidance used by much p... | [] | null | 18 | 1611.01796 | title_snapshot | [
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Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning | https://proceedings.mlr.press/v70/anschel17a.html | [
"Oron Anschel",
"Nir Baram",
"Nahum Shimkin"
] | null | null | Instability and variability of Deep Reinforcement Learning (DRL) algorithms tend to adversely affect their performance. Averaged-DQN is a simple extension to the DQN algorithm, based on averaging previously learned Q-values estimates, which leads to a more stable training procedure and improved performance by reducing ... | [] | null | 19 | 1611.01929 | title_snapshot | [
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A Simple Multi-Class Boosting Framework with Theoretical Guarantees and Empirical Proficiency | https://proceedings.mlr.press/v70/appel17a.html | [
"Ron Appel",
"Pietro Perona"
] | null | null | There is a need for simple yet accurate white-box learning systems that train quickly and with little data. To this end, we showcase REBEL, a multi-class boosting method, and present a novel family of weak learners called localized similarities. Our framework provably minimizes the training error of any dataset at an e... | [] | null | 20 | null | null | [
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Deep Voice: Real-time Neural Text-to-Speech | https://proceedings.mlr.press/v70/arik17a.html | [
"Sercan Ö. Arık",
"Mike Chrzanowski",
"Adam Coates",
"Gregory Diamos",
"Andrew Gibiansky",
"Yongguo Kang",
"Xian Li",
"John Miller",
"Andrew Ng",
"Jonathan Raiman",
"Shubho Sengupta",
"Mohammad Shoeybi"
] | null | null | We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks. Deep Voice lays the groundwork for truly end-to-end neural speech synthesis. The system comprises five major building blocks: a segmentation model for locating phoneme boundaries, a grapheme-to-phoneme conv... | [] | null | 21 | 1702.07825 | title_snapshot | [
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Oracle Complexity of Second-Order Methods for Finite-Sum Problems | https://proceedings.mlr.press/v70/arjevani17a.html | [
"Yossi Arjevani",
"Ohad Shamir"
] | null | null | Finite-sum optimization problems are ubiquitous in machine learning, and are commonly solved using first-order methods which rely on gradient computations. Recently, there has been growing interest insecond-ordermethods, which rely on both gradients and Hessians. In principle, second-order methods can require much fewe... | [] | null | 22 | 1611.04982 | title_snapshot | [
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Wasserstein Generative Adversarial Networks | https://proceedings.mlr.press/v70/arjovsky17a.html | [
"Martin Arjovsky",
"Soumith Chintala",
"Léon Bottou"
] | null | null | We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Furthermore, we show that the co... | [] | null | 23 | null | null | [
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Generalization and Equilibrium in Generative Adversarial Nets (GANs) | https://proceedings.mlr.press/v70/arora17a.html | [
"Sanjeev Arora",
"Rong Ge",
"Yingyu Liang",
"Tengyu Ma",
"Yi Zhang"
] | null | null | It is shown that training of generative adversarial network (GAN) may not have good generalization properties; e.g., training may appear successful but the trained distribution may be far from target distribution in standard metrics. However, generalization does occur for a weaker metric called neural net distance. It ... | [] | null | 24 | 1703.00573 | title_snapshot | [
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A Closer Look at Memorization in Deep Networks | https://proceedings.mlr.press/v70/arpit17a.html | [
"Devansh Arpit",
"Stanisław Jastrzębski",
"Nicolas Ballas",
"David Krueger",
"Emmanuel Bengio",
"Maxinder S. Kanwal",
"Tegan Maharaj",
"Asja Fischer",
"Aaron Courville",
"Yoshua Bengio",
"Simon Lacoste-Julien"
] | null | null | We examine the role of memorization in deep learning, drawing connections to capacity, generalization, and adversarial robustness. While deep networks are capable of memorizing noise data, our results suggest that they tend to prioritize learning simple patterns first. In our experiments, we expose qualitative differen... | [] | null | 25 | 1706.05394 | title_snapshot | [
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An Alternative Softmax Operator for Reinforcement Learning | https://proceedings.mlr.press/v70/asadi17a.html | [
"Kavosh Asadi",
"Michael L. Littman"
] | null | null | A softmax operator applied to a set of values acts somewhat like the maximization function and somewhat like an average. In sequential decision making, softmax is often used in settings where it is necessary to maximize utility but also to hedge against problems that arise from putting all of one’s weight behind a sing... | [] | null | 26 | 1612.05628 | title_snapshot | [
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Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees | https://proceedings.mlr.press/v70/avron17a.html | [
"Haim Avron",
"Michael Kapralov",
"Cameron Musco",
"Christopher Musco",
"Ameya Velingker",
"Amir Zandieh"
] | null | null | Random Fourier features is one of the most popular techniques for scaling up kernel methods, such as kernel ridge regression. However, despite impressive empirical results, the statistical properties of random Fourier features are still not well understood. In this paper we take steps toward filling this gap. Specifica... | [] | null | 27 | 1804.09893 | title_snapshot | [
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Minimax Regret Bounds for Reinforcement Learning | https://proceedings.mlr.press/v70/azar17a.html | [
"Mohammad Gheshlaghi Azar",
"Ian Osband",
"Rémi Munos"
] | null | null | We consider the problem of provably optimal exploration in reinforcement learning for finite horizon MDPs. We show that an optimistic modification to value iteration achieves a regret bound of $\tilde {O}( \sqrt{HSAT} + H^2S^2A+H\sqrt{T})$ where $H$ is the time horizon, $S$ the number of states, $A$ the number of actio... | [] | null | 28 | 1703.05449 | title_snapshot | [
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Learning the Structure of Generative Models without Labeled Data | https://proceedings.mlr.press/v70/bach17a.html | [
"Stephen H. Bach",
"Bryan He",
"Alexander Ratner",
"Christopher Ré"
] | null | null | Curating labeled training data has become the primary bottleneck in machine learning. Recent frameworks address this bottleneck with generative models to synthesize labels at scale from weak supervision sources. The generative model’s dependency structure directly affects the quality of the estimated labels, but select... | [] | null | 29 | 1703.00854 | title_snapshot | [
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Uniform Deviation Bounds for k-Means Clustering | https://proceedings.mlr.press/v70/bachem17a.html | [
"Olivier Bachem",
"Mario Lucic",
"S. Hamed Hassani",
"Andreas Krause"
] | null | null | Uniform deviation bounds limit the difference between a model’s expected loss and its loss on an empirical sampleuniformlyfor all models in a learning problem. In this paper, we provide a novel framework to obtain uniform deviation bounds for loss functions which areunbounded. As a result, we obtain competitive uniform... | [] | null | 30 | 1702.08249 | title_judge | [
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Distributed and Provably Good Seedings for k-Means in Constant Rounds | https://proceedings.mlr.press/v70/bachem17b.html | [
"Olivier Bachem",
"Mario Lucic",
"Andreas Krause"
] | null | null | The k-Means++ algorithm is the state of the art algorithm to solve k-Means clustering problems as the computed clusterings are O(log k) competitive in expectation. However, its seeding step requires k inherently sequential passes through the full data set making it hard to scale to massive data sets. The standard remed... | [] | null | 31 | null | null | [
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Learning Algorithms for Active Learning | https://proceedings.mlr.press/v70/bachman17a.html | [
"Philip Bachman",
"Alessandro Sordoni",
"Adam Trischler"
] | null | null | We introduce a model that learns active learning algorithms via metalearning. For a distribution of related tasks, our model jointly learns: a data representation, an item selection heuristic, and a prediction function. Our model uses the item selection heuristic to construct a labeled support set for training the pred... | [] | null | 32 | 1708.00088 | title_snapshot | [
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Improving Viterbi is Hard: Better Runtimes Imply Faster Clique Algorithms | https://proceedings.mlr.press/v70/backurs17a.html | [
"Arturs Backurs",
"Christos Tzamos"
] | null | null | The classic algorithm of Viterbi computes the most likely path in a Hidden Markov Model (HMM) that results in a given sequence of observations. It runs in time $O(Tn^2)$ given a sequence of T observations from a HMM with n states. Despite significant interest in the problem and prolonged effort by different communities... | [] | null | 33 | 1607.04229 | title_snapshot | [
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Differentially Private Clustering in High-Dimensional Euclidean Spaces | https://proceedings.mlr.press/v70/balcan17a.html | [
"Maria-Florina Balcan",
"Travis Dick",
"Yingyu Liang",
"Wenlong Mou",
"Hongyang Zhang"
] | null | null | We study the problem of clustering sensitive data while preserving the privacy of individuals represented in the dataset, which has broad applications in practical machine learning and data analysis tasks. Although the problem has been widely studied in the context of low-dimensional, discrete spaces, much remains unkn... | [] | null | 34 | null | null | [
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Strongly-Typed Agents are Guaranteed to Interact Safely | https://proceedings.mlr.press/v70/balduzzi17a.html | [
"David Balduzzi"
] | null | null | As artificial agents proliferate, it is becoming increasingly important to ensure that their interactions with one another are well-behaved. In this paper, we formalize a common-sense notion of when algorithms are well-behaved: an algorithm is safe if it does no harm. Motivated by recent progress in deep learning, we f... | [] | null | 35 | 1702.07450 | title_snapshot | [
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The Shattered Gradients Problem: If resnets are the answer, then what is the question? | https://proceedings.mlr.press/v70/balduzzi17b.html | [
"David Balduzzi",
"Marcus Frean",
"Lennox Leary",
"J. P. Lewis",
"Kurt Wan-Duo Ma",
"Brian McWilliams"
] | null | null | A long-standing obstacle to progress in deep learning is the problem of vanishing and exploding gradients. Although, the problem has largely been overcome via carefully constructed initializations and batch normalization, architectures incorporating skip-connections such as highway and resnets perform much better than ... | [] | null | 36 | 1702.08591 | title_snapshot | [
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Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks | https://proceedings.mlr.press/v70/balduzzi17c.html | [
"David Balduzzi",
"Brian McWilliams",
"Tony Butler-Yeoman"
] | null | null | Modern convolutional networks, incorporating rectifiers and max-pooling, are neither smooth nor convex; standard guarantees therefore do not apply. Nevertheless, methods from convex optimization such as gradient descent and Adam are widely used as building blocks for deep learning algorithms. This paper provides the fi... | [] | null | 37 | 1611.02345 | title_snapshot | [
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Spectral Learning from a Single Trajectory under Finite-State Policies | https://proceedings.mlr.press/v70/balle17a.html | [
"Borja Balle",
"Odalric-Ambrym Maillard"
] | null | null | We present spectral methods of moments for learning sequential models from a single trajectory, in stark contrast with the classical literature that assumes the availability of multiple i.i.d. trajectories. Our approach leverages an efficient SVD-based learning algorithm for weighted automata and provides the first rig... | [] | null | 38 | null | null | [
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Lost Relatives of the Gumbel Trick | https://proceedings.mlr.press/v70/balog17a.html | [
"Matej Balog",
"Nilesh Tripuraneni",
"Zoubin Ghahramani",
"Adrian Weller"
] | null | null | The Gumbel trick is a method to sample from a discrete probability distribution, or to estimate its normalizing partition function. The method relies on repeatedly applying a random perturbation to the distribution in a particular way, each time solving for the most likely configuration. We derive an entire family of r... | [] | null | 39 | 1706.04161 | title_snapshot | [
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Dynamic Word Embeddings | https://proceedings.mlr.press/v70/bamler17a.html | [
"Robert Bamler",
"Stephan Mandt"
] | null | null | We present a probabilistic language model for time-stamped text data which tracks the semantic evolution of individual words over time. The model represents words and contexts by latent trajectories in an embedding space. At each moment in time, the embedding vectors are inferred from a probabilistic version of word2ve... | [] | null | 40 | 1702.08359 | title_snapshot | [
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End-to-End Differentiable Adversarial Imitation Learning | https://proceedings.mlr.press/v70/baram17a.html | [
"Nir Baram",
"Oron Anschel",
"Itai Caspi",
"Shie Mannor"
] | null | null | Generative Adversarial Networks (GANs) have been successfully applied to the problem ofpolicy imitationin a model-free setup. However, the computation graph of GANs, that include a stochastic policy as the generative model, is no longer differentiable end-to-end, which requires the use of high-variance gradient estimat... | [] | null | 41 | null | null | [
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Emulating the Expert: Inverse Optimization through Online Learning | https://proceedings.mlr.press/v70/barmann17a.html | [
"Andreas Bärmann",
"Sebastian Pokutta",
"Oskar Schneider"
] | null | null | In this paper, we demonstrate how to learn the objective function of a decision maker while only observing the problem input data and the decision maker’s corresponding decisions over multiple rounds. Our approach is based on online learning techniques and works for linear objectives over arbitrary sets for which we ha... | [] | null | 42 | null | null | [
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Unimodal Probability Distributions for Deep Ordinal Classification | https://proceedings.mlr.press/v70/beckham17a.html | [
"Christopher Beckham",
"Christopher Pal"
] | null | null | Probability distributions produced by the cross-entropy loss for ordinal classification problems can possess undesired properties. We propose a straightforward technique to constrain discrete ordinal probability distributions to be unimodal via the use of the Poisson and binomial probability distributions. We evaluate ... | [] | null | 43 | 1705.05278 | title_snapshot | [
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Globally Induced Forest: A Prepruning Compression Scheme | https://proceedings.mlr.press/v70/begon17a.html | [
"Jean-Michel Begon",
"Arnaud Joly",
"Pierre Geurts"
] | null | null | Tree-based ensemble models are heavy memory-wise. An undesired state of affairs considering nowadays datasets, memory-constrained environment and fitting/prediction times. In this paper, we propose the Globally Induced Forest (GIF) to remedy this problem. GIF is a fast prepruning approach to build lightweight ensembles... | [] | null | 44 | null | null | [
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End-to-End Learning for Structured Prediction Energy Networks | https://proceedings.mlr.press/v70/belanger17a.html | [
"David Belanger",
"Bishan Yang",
"Andrew McCallum"
] | null | null | Structured Prediction Energy Networks (SPENs) are a simple, yet expressive family of structured prediction models (Belanger and McCallum, 2016). An energy function over candidate structured outputs is given by a deep network, and predictions are formed by gradient-based optimization. This paper presents end-to-end lear... | [] | null | 45 | 1703.05667 | title_snapshot | [
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Learning to Discover Sparse Graphical Models | https://proceedings.mlr.press/v70/belilovsky17a.html | [
"Eugene Belilovsky",
"Kyle Kastner",
"Gael Varoquaux",
"Matthew B. Blaschko"
] | null | null | We consider structure discovery of undirected graphical models from observational data. Inferring likely structures from few examples is a complex task often requiring the formulation of priors and sophisticated inference procedures. Popular methods rely on estimating a penalized maximum likelihood of the precision mat... | [] | null | 46 | 1605.06359 | title_snapshot | [
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A Distributional Perspective on Reinforcement Learning | https://proceedings.mlr.press/v70/bellemare17a.html | [
"Marc G. Bellemare",
"Will Dabney",
"Rémi Munos"
] | null | null | In this paper we argue for the fundamental importance of the value distribution: the distribution of the random return received by a reinforcement learning agent. This is in contrast to the common approach to reinforcement learning which models the expectation of this return, or value. Although there is an established ... | [] | null | 47 | 1707.06887 | title_snapshot | [
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Neural Optimizer Search with Reinforcement Learning | https://proceedings.mlr.press/v70/bello17a.html | [
"Irwan Bello",
"Barret Zoph",
"Vijay Vasudevan",
"Quoc V. Le"
] | null | null | We present an approach to automate the process of discovering optimization methods, with a focus on deep learning architectures. We train a Recurrent Neural Network controller to generate a string in a specific domain language that describes a mathematical update equation based on a list of primitive functions, such as... | [] | null | 48 | 1709.07417 | title_snapshot | [
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Learning Texture Manifolds with the Periodic Spatial GAN | https://proceedings.mlr.press/v70/bergmann17a.html | [
"Urs Bergmann",
"Nikolay Jetchev",
"Roland Vollgraf"
] | null | null | This paper introduces a novel approach to texture synthesis based on generative adversarial networks (GAN) (Goodfellow et al., 2014), and call this technique Periodic Spatial GAN (PSGAN). The PSGAN has several novel abilities which surpass the current state of the art in texture synthesis. First, we can learn multiple ... | [] | null | 49 | 1705.06566 | title_snapshot | [
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Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models | https://proceedings.mlr.press/v70/bernstein17a.html | [
"Garrett Bernstein",
"Ryan McKenna",
"Tao Sun",
"Daniel Sheldon",
"Michael Hay",
"Gerome Miklau"
] | null | null | We investigate the problem of learning discrete graphical models in a differentially private way. Approaches to this problem range from privileged algorithms that conduct learning completely behind the privacy barrier to schemes that release private summary statistics paired with algorithms to learn parameters from tho... | [] | null | 50 | 1706.04646 | title_snapshot | [
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Efficient Online Bandit Multiclass Learning with $\tilde{O}(\sqrt{T})$ Regret | https://proceedings.mlr.press/v70/beygelzimer17a.html | [
"Alina Beygelzimer",
"Francesco Orabona",
"Chicheng Zhang"
] | null | null | We present an efficient second-order algorithm with $\tilde{O}(1/\eta \sqrt{T})$ regret for the bandit online multiclass problem. The regret bound holds simultaneously with respect to a family of loss functions parameterized by $\eta$, ranging from hinge loss ($\eta=0$) to squared hinge loss ($\eta=1$). This provides a... | [] | null | 51 | 1702.07958 | title_snapshot | [
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Guarantees for Greedy Maximization of Non-submodular Functions with Applications | https://proceedings.mlr.press/v70/bian17a.html | [
"Andrew An Bian",
"Joachim M. Buhmann",
"Andreas Krause",
"Sebastian Tschiatschek"
] | null | null | We investigate the performance of the standard Greedy algorithm for cardinality constrained maximization of non-submodular nondecreasing set functions. While there are strong theoretical guarantees on the performance of Greedy for maximizing submodular functions, there are few guarantees for non-submodular ones. Howeve... | [] | null | 52 | 1703.02100 | title_snapshot | [
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Robust Submodular Maximization: A Non-Uniform Partitioning Approach | https://proceedings.mlr.press/v70/bogunovic17a.html | [
"Ilija Bogunovic",
"Slobodan Mitrović",
"Jonathan Scarlett",
"Volkan Cevher"
] | null | null | We study the problem of maximizing a monotone submodular function subject to a cardinality constraint $k$, with the added twist that a number of items $\tau$ from the returned set may be removed. We focus on the worst-case setting considered by Orlin et al.\ (2016), in which a constant-factor approximation guarantee wa... | [] | null | 53 | 1706.04918 | title_snapshot | [
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Unsupervised Learning by Predicting Noise | https://proceedings.mlr.press/v70/bojanowski17a.html | [
"Piotr Bojanowski",
"Armand Joulin"
] | null | null | Convolutional neural networks provide visual features that perform remarkably well in many computer vision applications. However, training these networks requires significant amounts of supervision; this paper introduces a generic framework to train such networks, end-to-end, with no supervision. We propose to fix a se... | [] | null | 54 | 1704.05310 | title_snapshot | [
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Adaptive Neural Networks for Efficient Inference | https://proceedings.mlr.press/v70/bolukbasi17a.html | [
"Tolga Bolukbasi",
"Joseph Wang",
"Ofer Dekel",
"Venkatesh Saligrama"
] | null | null | We present an approach to adaptively utilize deep neural networks in order to reduce the evaluation time on new examples without loss of accuracy. Rather than attempting to redesign or approximate existing networks, we propose two schemes that adaptively utilize networks. We first pose an adaptive network evaluation sc... | [] | null | 55 | 1702.07811 | title_snapshot | [
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Compressed Sensing using Generative Models | https://proceedings.mlr.press/v70/bora17a.html | [
"Ashish Bora",
"Ajil Jalal",
"Eric Price",
"Alexandros G. Dimakis"
] | null | null | The goal of compressed sensing is to estimate a vector from an underdetermined system of noisy linear measurements, by making use of prior knowledge on the structure of vectors in the relevant domain. For almost all results in this literature, the structure is represented by sparsity in a well-chosen basis. We show how... | [] | null | 56 | 1703.03208 | title_snapshot | [
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Programming with a Differentiable Forth Interpreter | https://proceedings.mlr.press/v70/bosnjak17a.html | [
"Matko Bošnjak",
"Tim Rocktäschel",
"Jason Naradowsky",
"Sebastian Riedel"
] | null | null | Given that in practice training data is scarce for all but a small set of problems, a core question is how to incorporate prior knowledge into a model. In this paper, we consider the case of prior procedural knowledge for neural networks, such as knowing how a program should traverse a sequence, but not what local acti... | [] | null | 57 | 1605.06640 | title_snapshot | [
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Practical Gauss-Newton Optimisation for Deep Learning | https://proceedings.mlr.press/v70/botev17a.html | [
"Aleksandar Botev",
"Hippolyt Ritter",
"David Barber"
] | null | null | We present an efficient block-diagonal approximation to the Gauss-Newton matrix for feedforward neural networks. Our resulting algorithm is competitive against state-of-the-art first-order optimisation methods, with sometimes significant improvement in optimisation performance. Unlike first-order methods, for which hyp... | [] | null | 58 | 1706.03662 | title_snapshot | [
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Lazifying Conditional Gradient Algorithms | https://proceedings.mlr.press/v70/braun17a.html | [
"Gábor Braun",
"Sebastian Pokutta",
"Daniel Zink"
] | null | null | Conditional gradient algorithms (also often called Frank-Wolfe algorithms) are popular due to their simplicity of only requiring a linear optimization oracle and more recently they also gained significant traction for online learning. While simple in principle, in many cases the actual implementation of the linear opti... | [] | null | 59 | 1610.05120 | title_snapshot | [
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Clustering High Dimensional Dynamic Data Streams | https://proceedings.mlr.press/v70/braverman17a.html | [
"Vladimir Braverman",
"Gereon Frahling",
"Harry Lang",
"Christian Sohler",
"Lin F. Yang"
] | null | null | We present data streaming algorithms for the $k$-median problem in high-dimensional dynamic geometric data streams, i.e. streams allowing both insertions and deletions of points from a discrete Euclidean space $\{1, 2, \ldots \Delta\}^d$. Our algorithms use $k \epsilon^{-2} \mathrm{poly}(d \log \Delta)$ space/time and ... | [] | null | 60 | 1706.03887 | title_snapshot | [
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On the Sampling Problem for Kernel Quadrature | https://proceedings.mlr.press/v70/briol17a.html | [
"François-Xavier Briol",
"Chris J. Oates",
"Jon Cockayne",
"Wilson Ye Chen",
"Mark Girolami"
] | null | null | The standard Kernel Quadrature method for numerical integration with random point sets (also called Bayesian Monte Carlo) is known to converge in root mean square error at a rate determined by the ratio s/d, where s and d encode the smoothness and dimension of the integrand. However, an empirical investigation reveals ... | [] | null | 61 | 1706.03369 | title_snapshot | [
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Reduced Space and Faster Convergence in Imperfect-Information Games via Pruning | https://proceedings.mlr.press/v70/brown17a.html | [
"Noam Brown",
"Tuomas Sandholm"
] | null | null | Iterative algorithms such as Counterfactual Regret Minimization (CFR) are the most popular way to solve large zero-sum imperfect-information games. In this paper we introduce Best-Response Pruning (BRP), an improvement to iterative algorithms such as CFR that allows poorly-performing actions to be temporarily pruned. W... | [] | null | 62 | 1609.03234 | title_judge | [
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Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs | https://proceedings.mlr.press/v70/brutzkus17a.html | [
"Alon Brutzkus",
"Amir Globerson"
] | null | null | Deep learning models are often successfully trained using gradient descent, despite the worst case hardness of the underlying non-convex optimization problem. The key question is then under what conditions can one prove that optimization will succeed. Here we provide a strong result of this kind. We consider a neural n... | [] | null | 63 | 1702.07966 | title_snapshot | [
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Deep Tensor Convolution on Multicores | https://proceedings.mlr.press/v70/budden17a.html | [
"David Budden",
"Alexander Matveev",
"Shibani Santurkar",
"Shraman Ray Chaudhuri",
"Nir Shavit"
] | null | null | Deep convolutional neural networks (ConvNets) of 3-dimensional kernels allow joint modeling of spatiotemporal features. These networks have improved performance of video and volumetric image analysis, but have been limited in size due to the low memory ceiling of GPU hardware. Existing CPU implementations overcome this... | [] | null | 64 | 1611.06565 | title_snapshot | [
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Multi-objective Bandits: Optimizing the Generalized Gini Index | https://proceedings.mlr.press/v70/busa-fekete17a.html | [
"Róbert Busa-Fekete",
"Balázs Szörényi",
"Paul Weng",
"Shie Mannor"
] | null | null | We study the multi-armed bandit (MAB) problem where the agent receives a vectorial feedback that encodes many possibly competing objectives to be optimized. The goal of the agent is to find a policy, which can optimize these objectives simultaneously in a fair way. This multi-objective online optimization problem is fo... | [] | null | 65 | 1706.04933 | title_snapshot | [
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Priv’IT: Private and Sample Efficient Identity Testing | https://proceedings.mlr.press/v70/cai17a.html | [
"Bryan Cai",
"Constantinos Daskalakis",
"Gautam Kamath"
] | null | null | We develop differentially private hypothesis testing methods for the small sample regime. Given a sample $\mathcal{D}$ from a categorical distribution $p$ over some domain $\Sigma$, an explicitly described distribution $q$ over $\Sigma$, some privacy parameter $\epsilon$, accuracy parameter $\alpha$, and requirements $... | [] | null | 66 | 1703.10127 | title_snapshot | [
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Second-Order Kernel Online Convex Optimization with Adaptive Sketching | https://proceedings.mlr.press/v70/calandriello17a.html | [
"Daniele Calandriello",
"Alessandro Lazaric",
"Michal Valko"
] | null | null | Kernel online convex optimization (KOCO) is a framework combining the expressiveness of non-parametric kernel models with the regret guarantees of online learning. First-order KOCO methods such as functional gradient descent require only $O(t)$ time and space per iteration, and, when the only information on the losses ... | [] | null | 67 | 1706.04892 | title_snapshot | [
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“Convex Until Proven Guilty”: Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions | https://proceedings.mlr.press/v70/carmon17a.html | [
"Yair Carmon",
"John C. Duchi",
"Oliver Hinder",
"Aaron Sidford"
] | null | null | We develop and analyze a variant of Nesterov’s accelerated gradient descent (AGD) for minimization of smooth non-convex functions. We prove that one of two cases occurs: either our AGD variant converges quickly, as if the function was convex, or we produce a certificate that the function is “guilty” of being non-convex... | [] | null | 68 | 1705.02766 | title_snapshot | [
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Sliced Wasserstein Kernel for Persistence Diagrams | https://proceedings.mlr.press/v70/carriere17a.html | [
"Mathieu Carrière",
"Marco Cuturi",
"Steve Oudot"
] | null | null | Persistence diagrams (PDs) play a key role in topological data analysis (TDA), in which they are routinely used to describe succinctly complex topological properties of complicated shapes. PDs enjoy strong stability properties and have proven their utility in various learning contexts. They do not, however, live in a s... | [] | null | 69 | 1706.03358 | title_snapshot | [
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Multiple Clustering Views from Multiple Uncertain Experts | https://proceedings.mlr.press/v70/chang17a.html | [
"Yale Chang",
"Junxiang Chen",
"Michael H. Cho",
"Peter J. Castaldi",
"Edwin K. Silverman",
"Jennifer G. Dy"
] | null | null | Expert input can improve clustering performance. In today’s collaborative environment, the availability of crowdsourced multiple expert input is becoming common. Given multiple experts’ inputs, most existing approaches can only discover one clustering structure. However, data is multi-faced by nature and can be cluster... | [] | null | 70 | null | null | [
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Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference | https://proceedings.mlr.press/v70/chaudhry17a.html | [
"Aditya Chaudhry",
"Pan Xu",
"Quanquan Gu"
] | null | null | Causal inference among high-dimensional time series data proves an important research problem in many fields. While in the classical regime one often establishes causality among time series via a concept known as “Granger causality,” existing approaches for Granger causal inference in high-dimensional data lack the mea... | [] | null | 71 | null | null | [
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Active Heteroscedastic Regression | https://proceedings.mlr.press/v70/chaudhuri17a.html | [
"Kamalika Chaudhuri",
"Prateek Jain",
"Nagarajan Natarajan"
] | null | null | An active learner is given a model class $\Theta$, a large sample of unlabeled data drawn from an underlying distribution and access to a labeling oracle that can provide a label for any of the unlabeled instances. The goal of the learner is to find a model $\theta \in \Theta$ that fits the data to a given accuracy whi... | [] | null | 72 | null | null | [
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Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning | https://proceedings.mlr.press/v70/chebotar17a.html | [
"Yevgen Chebotar",
"Karol Hausman",
"Marvin Zhang",
"Gaurav Sukhatme",
"Stefan Schaal",
"Sergey Levine"
] | null | null | Reinforcement learning algorithms for real-world robotic applications must be able to handle complex, unknown dynamical systems while maintaining data-efficient learning. These requirements are handled well by model-free and model-based RL approaches, respectively. In this work, we aim to combine the advantages of thes... | [] | null | 73 | 1703.03078 | title_snapshot | [
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Robust Structured Estimation with Single-Index Models | https://proceedings.mlr.press/v70/chen17a.html | [
"Sheng Chen",
"Arindam Banerjee"
] | null | null | In this paper, we investigate general single-index models (SIMs) in high dimensions. Based on U-statistics, we propose two types of robust estimators for the recovery of model parameters, which can be viewed as generalizations of several existing algorithms for one-bit compressed sensing (1-bit CS). With minimal assump... | [] | null | 74 | null | null | [
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Adaptive Multiple-Arm Identification | https://proceedings.mlr.press/v70/chen17b.html | [
"Jiecao Chen",
"Xi Chen",
"Qin Zhang",
"Yuan Zhou"
] | null | null | We study the problem of selecting K arms with the highest expected rewards in a stochastic n-armed bandit game. This problem has a wide range of applications, e.g., A/B testing, crowdsourcing, simulation optimization. Our goal is to develop a PAC algorithm, which, with probability at least $1-\delta$, identifies a set ... | [] | null | 75 | 1706.01026 | title_snapshot | [
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Dueling Bandits with Weak Regret | https://proceedings.mlr.press/v70/chen17c.html | [
"Bangrui Chen",
"Peter I. Frazier"
] | null | null | We consider online content recommendation with implicit feedback through pairwise comparisons, formalized as the so-called dueling bandit problem. We study the dueling bandit problem in the Condorcet winner setting, and consider two notions of regret: the more well-studied strong regret, which is 0 only when both arms ... | [] | null | 76 | 1706.04304 | title_snapshot | [
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Strong NP-Hardness for Sparse Optimization with Concave Penalty Functions | https://proceedings.mlr.press/v70/chen17d.html | [
"Yichen Chen",
"Dongdong Ge",
"Mengdi Wang",
"Zizhuo Wang",
"Yinyu Ye",
"Hao Yin"
] | null | null | Consider the regularized sparse minimization problem, which involves empirical sums of loss functions for $n$ data points (each of dimension $d$) and a nonconvex sparsity penalty. We prove that finding an $\mathcal{O}(n^{c_1}d^{c_2})$-optimal solution to the regularized sparse optimization problem is strongly NP-hard f... | [] | null | 77 | 1501.00622 | title_snapshot | [
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Learning to Learn without Gradient Descent by Gradient Descent | https://proceedings.mlr.press/v70/chen17e.html | [
"Yutian Chen",
"Matthew W. Hoffman",
"Sergio Gómez Colmenarejo",
"Misha Denil",
"Timothy P. Lillicrap",
"Matt Botvinick",
"Nando Freitas"
] | null | null | We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent. We show that these learned optimizers exhibit a remarkable degree of transfer in that they can be used to efficiently optimize a broad range of derivative-free black-box functions, including Gaussian process bandits,... | [] | null | 78 | 1611.03824 | title_snapshot | [
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Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables | https://proceedings.mlr.press/v70/chen17f.html | [
"Bryant Chen",
"Daniel Kumor",
"Elias Bareinboim"
] | null | null | We developed a novel approach to identification and model testing in linear structural equation models (SEMs) based on auxiliary variables (AVs), which generalizes a widely-used family of methods known as instrumental variables. The identification problem is concerned with the conditions under which causal parameters c... | [] | null | 79 | 1612.03451 | title_snapshot | [
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Toward Efficient and Accurate Covariance Matrix Estimation on Compressed Data | https://proceedings.mlr.press/v70/chen17g.html | [
"Xixian Chen",
"Michael R. Lyu",
"Irwin King"
] | null | null | Estimating covariance matrices is a fundamental technique in various domains, most notably in machine learning and signal processing. To tackle the challenges of extensive communication costs, large storage capacity requirements, and high processing time complexity when handling massive high-dimensional and distributed... | [] | null | 80 | null | null | [
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Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability | https://proceedings.mlr.press/v70/chen17h.html | [
"Zhehui Chen",
"Lin F. Yang",
"Chris Junchi Li",
"Tuo Zhao"
] | null | null | Multiview representation learning is popular for latent factor analysis. Many existing approaches formulate the multiview representation learning as convex optimization problems, where global optima can be obtained by certain algorithms in polynomial time. However, many evidences have corroborated that heuristic noncon... | [] | null | 81 | null | null | [
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Learning to Aggregate Ordinal Labels by Maximizing Separating Width | https://proceedings.mlr.press/v70/chen17i.html | [
"Guangyong Chen",
"Shengyu Zhang",
"Di Lin",
"Hui Huang",
"Pheng Ann Heng"
] | null | null | While crowdsourcing has been a cost and time efficient method to label massive samples, one critical issue is quality control, for which the key challenge is to infer the ground truth from noisy or even adversarial data by various users. A large class of crowdsourcing problems, such as those involving age, grade, level... | [] | null | 82 | null | null | [
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Nearly Optimal Robust Matrix Completion | https://proceedings.mlr.press/v70/cherapanamjeri17a.html | [
"Yeshwanth Cherapanamjeri",
"Kartik Gupta",
"Prateek Jain"
] | null | null | In this paper, we consider the problem of Robust Matrix Completion (RMC) where the goal is to recover a low-rank matrix by observing a small number of its entries out of which a few can be arbitrarily corrupted. We propose a simple projected gradient descent-based method to estimate the low-rank matrix that alternately... | [] | null | 83 | 1606.07315 | title_snapshot | [
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Algorithms for $\ell_p$ Low-Rank Approximation | https://proceedings.mlr.press/v70/chierichetti17a.html | [
"Flavio Chierichetti",
"Sreenivas Gollapudi",
"Ravi Kumar",
"Silvio Lattanzi",
"Rina Panigrahy",
"David P. Woodruff"
] | null | null | We consider the problem of approximating a given matrix by a low-rank matrix so as to minimize the entrywise $\ell_p$-approximation error, for any $p \geq 1$; the case $p = 2$ is the classical SVD problem. We obtain the first provably good approximation algorithms for this robust version of low-rank approximation that ... | [] | null | 84 | 1705.06730 | title_snapshot | [
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MEC: Memory-efficient Convolution for Deep Neural Network | https://proceedings.mlr.press/v70/cho17a.html | [
"Minsik Cho",
"Daniel Brand"
] | null | null | Convolution is a critical component in modern deep neural networks, thus several algorithms for convolution have been developed. Direct convolution is simple but suffers from poor performance. As an alternative, multiple indirect methods have been proposed including im2col-based convolution, FFT-based convolution, or W... | [] | null | 85 | 1706.06873 | title_snapshot | [
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On Relaxing Determinism in Arithmetic Circuits | https://proceedings.mlr.press/v70/choi17a.html | [
"Arthur Choi",
"Adnan Darwiche"
] | null | null | The past decade has seen a significant interest in learning tractable probabilistic representations. Arithmetic circuits (ACs) were among the first proposed tractable representations, with some subsequent representations being instances of ACs with weaker or stronger properties. In this paper, we provide a formal basis... | [] | null | 86 | 1708.06846 | title_snapshot | [
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Improving Stochastic Policy Gradients in Continuous Control with Deep Reinforcement Learning using the Beta Distribution | https://proceedings.mlr.press/v70/chou17a.html | [
"Po-Wei Chou",
"Daniel Maturana",
"Sebastian Scherer"
] | null | null | Recently, reinforcement learning with deep neural networks has achieved great success in challenging continuous control problems such as 3D locomotion and robotic manipulation. However, in real-world control problems, the actions one can take are bounded by physical constraints, which introduces a bias when the standar... | [] | null | 87 | null | null | [
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On Kernelized Multi-armed Bandits | https://proceedings.mlr.press/v70/chowdhury17a.html | [
"Sayak Ray Chowdhury",
"Aditya Gopalan"
] | null | null | We consider the stochastic bandit problem with a continuous set of arms, with the expected reward function over the arms assumed to be fixed but unknown. We provide two new Gaussian process-based algorithms for continuous bandit optimization – Improved GP-UCB (IGP-UCB) and GP-Thomson sampling (GP-TS), and derive corres... | [] | null | 88 | 1704.00445 | title_snapshot | [
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Parseval Networks: Improving Robustness to Adversarial Examples | https://proceedings.mlr.press/v70/cisse17a.html | [
"Moustapha Cisse",
"Piotr Bojanowski",
"Edouard Grave",
"Yann Dauphin",
"Nicolas Usunier"
] | null | null | We introduce Parseval networks, a form of deep neural networks in which the Lipschitz constant of linear, convolutional and aggregation layers is constrained to be smaller than $1$. Parseval networks are empirically and theoretically motivated by an analysis of the robustness of the predictions made by deep neural netw... | [] | null | 89 | 1704.08847 | title_snapshot | [
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Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC | https://proceedings.mlr.press/v70/cong17a.html | [
"Yulai Cong",
"Bo Chen",
"Hongwei Liu",
"Mingyuan Zhou"
] | null | null | It is challenging to develop stochastic gradient based scalable inference for deep discrete latent variable models (LVMs), due to the difficulties in not only computing the gradients, but also adapting the step sizes to different latent factors and hidden layers. For the Poisson gamma belief network (PGBN), a recently ... | [] | null | 90 | 1706.01724 | title_snapshot | [
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AdaNet: Adaptive Structural Learning of Artificial Neural Networks | https://proceedings.mlr.press/v70/cortes17a.html | [
"Corinna Cortes",
"Xavier Gonzalvo",
"Vitaly Kuznetsov",
"Mehryar Mohri",
"Scott Yang"
] | null | null | We present a new framework for analyzing and learning artificial neural networks. Our approach simultaneously and adaptively learns both the structure of the network as well as its weights. The methodology is based upon and accompanied by strong data-dependent theoretical learning guarantees, so that the final network ... | [] | null | 91 | 1607.01097 | title_snapshot | [
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Random Feature Expansions for Deep Gaussian Processes | https://proceedings.mlr.press/v70/cutajar17a.html | [
"Kurt Cutajar",
"Edwin V. Bonilla",
"Pietro Michiardi",
"Maurizio Filippone"
] | null | null | The composition of multiple Gaussian Processes as a Deep Gaussian Process DGP enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning problems with sound quantification of uncertainty. Existing inference approaches for DGP models have limited scalability and are notoriously cumbe... | [] | null | 92 | 1610.04386 | title_snapshot | [
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Soft-DTW: a Differentiable Loss Function for Time-Series | https://proceedings.mlr.press/v70/cuturi17a.html | [
"Marco Cuturi",
"Mathieu Blondel"
] | null | null | We propose in this paper a differentiable learning loss between time series, building upon the celebrated dynamic time warping (DTW) discrepancy. Unlike the Euclidean distance, DTW can compare time series of variable size and is robust to shifts or dilatations across the time dimension. To compute DTW, one typically so... | [] | null | 93 | 1703.01541 | title_snapshot | [
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Understanding Synthetic Gradients and Decoupled Neural Interfaces | https://proceedings.mlr.press/v70/czarnecki17a.html | [
"Wojciech Marian Czarnecki",
"Grzegorz Świrszcz",
"Max Jaderberg",
"Simon Osindero",
"Oriol Vinyals",
"Koray Kavukcuoglu"
] | null | null | When training neural networks, the use of Synthetic Gradients (SG) allows layers or modules to be trained without update locking – without waiting for a true error gradient to be backpropagated – resulting in Decoupled Neural Interfaces (DNIs). This unlocked ability of being able to update parts of a neural network asy... | [] | null | 94 | 1703.00522 | title_snapshot | [
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Stochastic Generative Hashing | https://proceedings.mlr.press/v70/dai17a.html | [
"Bo Dai",
"Ruiqi Guo",
"Sanjiv Kumar",
"Niao He",
"Le Song"
] | null | null | Learning-based binary hashing has become a powerful paradigm for fast search and retrieval in massive databases. However, due to the requirement of discrete outputs for the hash functions, learning such functions is known to be very challenging. In addition, the objective functions adopted by existing hashing technique... | [] | null | 95 | 1701.02815 | title_snapshot | [
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Logarithmic Time One-Against-Some | https://proceedings.mlr.press/v70/daume17a.html | [
"Hal Daumé III",
"Nikos Karampatziakis",
"John Langford",
"Paul Mineiro"
] | null | null | We create a new online reduction of multiclass classification to binary classification for which training and prediction time scale logarithmically with the number of classes. We show that several simple techniques give rise to an algorithm which is superior to previous logarithmic time classification approaches while ... | [] | null | 96 | 1606.04988 | title_snapshot | [
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Language Modeling with Gated Convolutional Networks | https://proceedings.mlr.press/v70/dauphin17a.html | [
"Yann N. Dauphin",
"Angela Fan",
"Michael Auli",
"David Grangier"
] | null | null | The pre-dominant approach to language modeling to date is based on recurrent neural networks. Their success on this task is often linked to their ability to capture unbounded context. In this paper we develop a finite context approach through stacked convolutions, which can be more efficient since they allow paralleliz... | [] | null | 97 | 1612.08083 | title_snapshot | [
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An Infinite Hidden Markov Model With Similarity-Biased Transitions | https://proceedings.mlr.press/v70/dawson17a.html | [
"Colin Reimer Dawson",
"Chaofan Huang",
"Clayton T. Morrison"
] | null | null | We describe a generalization of the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) which is able to encode prior information that state transitions are more likely between “nearby” states. This is accomplished by defining a similarity function on the state space and scaling transition probabilities by pai... | [] | null | 98 | 1707.06756 | title_snapshot | [
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Distributed Batch Gaussian Process Optimization | https://proceedings.mlr.press/v70/daxberger17a.html | [
"Erik A. Daxberger",
"Bryan Kian Hsiang Low"
] | null | null | This paper presents a novel distributed batch Gaussian process upper confidence bound (DB-GP-UCB) algorithm for performing batch Bayesian optimization (BO) of highly complex, costly-to-evaluate black-box objective functions. In contrast to existing batch BO algorithms, DB-GP-UCB can jointly optimize a batch of inputs (... | [] | null | 99 | null | null | [
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... |
Consistency Analysis for Binary Classification Revisited | https://proceedings.mlr.press/v70/dembczynski17a.html | [
"Krzysztof Dembczyński",
"Wojciech Kotłowski",
"Oluwasanmi Koyejo",
"Nagarajan Natarajan"
] | null | null | Statistical learning theory is at an inflection point enabled by recent advances in understanding and optimizing a wide range of metrics. Of particular interest are non-decomposable metrics such as the F-measure and the Jaccard measure which cannot be represented as a simple average over examples. Non-decomposability i... | [] | null | 100 | null | null | [
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-0.0016024343203753233,
-0.06398270279169083,
-0... |
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