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Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems | https://proceedings.mlr.press/v80/abeille18a.html | [
"Marc Abeille",
"Alessandro Lazaric"
] | null | null | Thompson sampling (TS) is an effective approach to trade off exploration and exploration in reinforcement learning. Despite its empirical success and recent advances, its theoretical analysis is often limited to the Bayesian setting, finite state-action spaces, or finite-horizon problems. In this paper, we study an ins... | [] | null | 1 | null | null | [
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State Abstractions for Lifelong Reinforcement Learning | https://proceedings.mlr.press/v80/abel18a.html | [
"David Abel",
"Dilip Arumugam",
"Lucas Lehnert",
"Michael Littman"
] | null | null | In lifelong reinforcement learning, agents must effectively transfer knowledge across tasks while simultaneously addressing exploration, credit assignment, and generalization. State abstraction can help overcome these hurdles by compressing the representation used by an agent, thereby reducing the computational and sta... | [] | null | 2 | null | null | [
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Policy and Value Transfer in Lifelong Reinforcement Learning | https://proceedings.mlr.press/v80/abel18b.html | [
"David Abel",
"Yuu Jinnai",
"Sophie Yue Guo",
"George Konidaris",
"Michael Littman"
] | null | null | We consider the problem of how best to use prior experience to bootstrap lifelong learning, where an agent faces a series of task instances drawn from some task distribution. First, we identify the initial policy that optimizes expected performance over the distribution of tasks for increasingly complex classes of poli... | [] | null | 3 | null | null | [
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INSPECTRE: Privately Estimating the Unseen | https://proceedings.mlr.press/v80/acharya18a.html | [
"Jayadev Acharya",
"Gautam Kamath",
"Ziteng Sun",
"Huanyu Zhang"
] | null | null | We develop differentially private methods for estimating various distributional properties. Given a sample from a discrete distribution p, some functional f, and accuracy and privacy parameters alpha and epsilon, the goal is to estimate f(p) up to accuracy alpha, while maintaining epsilon-differential privacy of the sa... | [] | null | 4 | 1803.00008 | title_snapshot | [
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Learning Representations and Generative Models for 3D Point Clouds | https://proceedings.mlr.press/v80/achlioptas18a.html | [
"Panos Achlioptas",
"Olga Diamanti",
"Ioannis Mitliagkas",
"Leonidas Guibas"
] | null | null | Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling. In this paper, we look at geometric data represented as point clouds. We introduce a deep AutoEncoder (AE) network with state-of-the-art reconstruction quality and generalization ability. The learned... | [] | null | 5 | 1707.02392 | title_snapshot | [
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Discovering Interpretable Representations for Both Deep Generative and Discriminative Models | https://proceedings.mlr.press/v80/adel18a.html | [
"Tameem Adel",
"Zoubin Ghahramani",
"Adrian Weller"
] | null | null | Interpretability of representations in both deep generative and discriminative models is highly desirable. Current methods jointly optimize an objective combining accuracy and interpretability. However, this may reduce accuracy, and is not applicable to already trained models. We propose two interpretability frameworks... | [] | null | 6 | null | null | [
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A Reductions Approach to Fair Classification | https://proceedings.mlr.press/v80/agarwal18a.html | [
"Alekh Agarwal",
"Alina Beygelzimer",
"Miroslav Dudik",
"John Langford",
"Hanna Wallach"
] | null | null | We present a systematic approach for achieving fairness in a binary classification setting. While we focus on two well-known quantitative definitions of fairness, our approach encompasses many other previously studied definitions as special cases. The key idea is to reduce fair classification to a sequence of cost-sens... | [] | null | 7 | 1803.02453 | title_snapshot | [
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Accelerated Spectral Ranking | https://proceedings.mlr.press/v80/agarwal18b.html | [
"Arpit Agarwal",
"Prathamesh Patil",
"Shivani Agarwal"
] | null | null | The problem of rank aggregation from pairwise and multiway comparisons has a wide range of implications, ranging from recommendation systems to sports rankings to social choice. Some of the most popular algorithms for this problem come from the class of spectral ranking algorithms; these include the rank centrality (RC... | [] | null | 8 | null | null | [
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MISSION: Ultra Large-Scale Feature Selection using Count-Sketches | https://proceedings.mlr.press/v80/aghazadeh18a.html | [
"Amirali Aghazadeh",
"Ryan Spring",
"Daniel Lejeune",
"Gautam Dasarathy",
"Anshumali Shrivastava",
"baraniuk"
] | null | null | Feature selection is an important challenge in machine learning. It plays a crucial role in the explainability of machine-driven decisions that are rapidly permeating throughout modern society. Unfortunately, the explosion in the size and dimensionality of real-world datasets poses a severe challenge to standard featur... | [] | null | 9 | 1806.04310 | title_snapshot | [
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Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models | https://proceedings.mlr.press/v80/agrawal18a.html | [
"Raj Agrawal",
"Caroline Uhler",
"Tamara Broderick"
] | null | null | Learning a Bayesian network (BN) from data can be useful for decision-making or discovering causal relationships. However, traditional methods often fail in modern applications, which exhibit a larger number of observed variables than data points. The resulting uncertainty about the underlying network as well as the de... | [] | null | 10 | 1803.05554 | title_snapshot | [
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Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy | https://proceedings.mlr.press/v80/agrawal18b.html | [
"Shipra Agrawal",
"Morteza Zadimoghaddam",
"Vahab Mirrokni"
] | null | null | Inspired by many applications of bipartite matching in online advertising and machine learning, we study a simple and natural iterative proportional allocation algorithm: Maintain a priority score $\priority_a$ for each node $a\in \mathds{A}$ on one side of the bipartition, initialized as $\priority_a=1$. Iteratively a... | [] | null | 11 | null | null | [
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Bucket Renormalization for Approximate Inference | https://proceedings.mlr.press/v80/ahn18a.html | [
"Sungsoo Ahn",
"Michael Chertkov",
"Adrian Weller",
"Jinwoo Shin"
] | null | null | Probabilistic graphical models are a key tool in machine learning applications. Computing the partition function, i.e., normalizing constant, is a fundamental task of statistical inference but is generally computationally intractable, leading to extensive study of approximation methods. Iterative variational methods ar... | [] | null | 12 | 1803.05104 | title_snapshot | [
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oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis | https://proceedings.mlr.press/v80/ainsworth18a.html | [
"Samuel K. Ainsworth",
"Nicholas J. Foti",
"Adrian K. C. Lee",
"Emily B. Fox"
] | null | null | Deep generative models have recently yielded encouraging results in producing subjectively realistic samples of complex data. Far less attention has been paid to making these generative models interpretable. In many scenarios, ranging from scientific applications to finance, the observed variables have a natural groupi... | [] | null | 13 | null | null | [
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Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design | https://proceedings.mlr.press/v80/alaa18a.html | [
"Ahmed Alaa",
"Mihaela Schaar"
] | null | null | Estimating heterogeneous treatment effects from observational data is a central problem in many domains. Because counterfactual data is inaccessible, the problem differs fundamentally from supervised learning, and entails a more complex set of modeling choices. Despite a variety of recently proposed algorithmic solutio... | [] | null | 14 | null | null | [
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AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning | https://proceedings.mlr.press/v80/alaa18b.html | [
"Ahmed Alaa",
"Mihaela Schaar"
] | null | null | Clinical prognostic models derived from largescale healthcare data can inform critical diagnostic and therapeutic decisions. To enable off-theshelf usage of machine learning (ML) in prognostic research, we developed AUTOPROGNOSIS: a system for automating the design of predictive modeling pipelines tailored for clinical... | [] | null | 15 | 1802.07207 | title_snapshot | [
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Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization | https://proceedings.mlr.press/v80/alabdulmohsin18a.html | [
"Ibrahim Alabdulmohsin"
] | null | null | In this paper, we derive bounds on the mutual information of the empirical risk minimization (ERM) procedure for both 0-1 and strongly-convex loss classes. We prove that under the Axiom of Choice, the existence of an ERM learning rule with a vanishing mutual information is equivalent to the assertion that the loss clas... | [] | null | 16 | null | null | [
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Fixing a Broken ELBO | https://proceedings.mlr.press/v80/alemi18a.html | [
"Alexander Alemi",
"Ben Poole",
"Ian Fischer",
"Joshua Dillon",
"Rif A. Saurous",
"Kevin Murphy"
] | null | null | Recent work in unsupervised representation learning has focused on learning deep directed latentvariable models. Fitting these models by maximizing the marginal likelihood or evidence is typically intractable, thus a common approximation is to maximize the evidence lower bound (ELBO) instead. However, maximum likelihoo... | [] | null | 17 | 1711.00464 | title_snapshot | [
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Differentially Private Identity and Equivalence Testing of Discrete Distributions | https://proceedings.mlr.press/v80/aliakbarpour18a.html | [
"Maryam Aliakbarpour",
"Ilias Diakonikolas",
"Ronitt Rubinfeld"
] | null | null | We study the fundamental problems of identity and equivalence testing over a discrete population from random samples. Our goal is to develop efficient testers while guaranteeing differential privacy to the individuals of the population. We provide sample-efficient differentially private testers for these problems. Our ... | [] | null | 18 | 1707.05497 | title_judge | [
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Katyusha X: Simple Momentum Method for Stochastic Sum-of-Nonconvex Optimization | https://proceedings.mlr.press/v80/allen-zhu18a.html | [
"Zeyuan Allen-Zhu"
] | null | null | The problem of minimizing sum-of-nonconvex functions (i.e., convex functions that are average of non-convex ones) is becoming increasing important in machine learning, and is the core machinery for PCA, SVD, regularized Newton’s method, accelerated non-convex optimization, and more. We show how to provably obtain an ac... | [] | null | 19 | 1802.03866 | title_judge | [
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Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits | https://proceedings.mlr.press/v80/allen-zhu18b.html | [
"Zeyuan Allen-Zhu",
"Sebastien Bubeck",
"Yuanzhi Li"
] | null | null | Regret bounds in online learning compare the player’s performance to $L*$, the optimal performance in hindsight with a fixed strategy. Typically such bounds scale with the square root of the time horizon $T$. The more refined concept of first-order regret bound replaces this with a scaling $\sqrt{L*}$, which may be muc... | [] | null | 20 | 1802.03386 | title_snapshot | [
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Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data | https://proceedings.mlr.press/v80/almahairi18a.html | [
"Amjad Almahairi",
"Sai Rajeshwar",
"Alessandro Sordoni",
"Philip Bachman",
"Aaron Courville"
] | null | null | Learning inter-domain mappings from unpaired data can improve performance in structured prediction tasks, such as image segmentation, by reducing the need for paired data. CycleGAN was recently proposed for this problem, but critically assumes the underlying inter-domain mapping is approximately deterministic and one-t... | [] | null | 21 | 1802.10151 | title_snapshot | [
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Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory | https://proceedings.mlr.press/v80/amit18a.html | [
"Ron Amit",
"Ron Meir"
] | null | null | In meta-learning an agent extracts knowledge from observed tasks, aiming to facilitate learning of novel future tasks. Under the assumption that future tasks are ‘related’ to previous tasks, accumulated knowledge should be learned in such a way that they capture the common structure across learned tasks, while allowing... | [] | null | 22 | 1711.01244 | title_snapshot | [
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MAGAN: Aligning Biological Manifolds | https://proceedings.mlr.press/v80/amodio18a.html | [
"Matthew Amodio",
"Smita Krishnaswamy"
] | null | null | It is increasingly common in many types of natural and physical systems (especially biological systems) to have different types of measurements performed on the same underlying system. In such settings, it is important to align the manifolds arising from each measurement in order to integrate such data and gain an impr... | [] | null | 23 | 1803.00385 | title_snapshot | [
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Subspace Embedding and Linear Regression with Orlicz Norm | https://proceedings.mlr.press/v80/andoni18a.html | [
"Alexandr Andoni",
"Chengyu Lin",
"Ying Sheng",
"Peilin Zhong",
"Ruiqi Zhong"
] | null | null | We consider a generalization of the classic linear regression problem to the case when the loss is an Orlicz norm. An Orlicz norm is parameterized by a non-negative convex function G: R_+ - > R_+ with G(0) = 0: the Orlicz norm of a n-dimensional vector x is defined as |x|_G = inf{ alpha > 0 | sum_{i = 1}^n G( |x_i| / a... | [] | null | 24 | 1806.06430 | title_snapshot | [
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Efficient Gradient-Free Variational Inference using Policy Search | https://proceedings.mlr.press/v80/arenz18a.html | [
"Oleg Arenz",
"Gerhard Neumann",
"Mingjun Zhong"
] | null | null | Inference from complex distributions is a common problem in machine learning needed for many Bayesian methods. We propose an efficient, gradient-free method for learning general GMM approximations of multimodal distributions based on recent insights from stochastic search methods. Our method establishes information-geo... | [] | null | 25 | null | null | [
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On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization | https://proceedings.mlr.press/v80/arora18a.html | [
"Sanjeev Arora",
"Nadav Cohen",
"Elad Hazan"
] | null | null | Conventional wisdom in deep learning states that increasing depth improves expressiveness but complicates optimization. This paper suggests that, sometimes, increasing depth can speed up optimization. The effect of depth on optimization is decoupled from expressiveness by focusing on settings where additional layers am... | [] | null | 26 | 1802.06509 | title_snapshot | [
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Stronger Generalization Bounds for Deep Nets via a Compression Approach | https://proceedings.mlr.press/v80/arora18b.html | [
"Sanjeev Arora",
"Rong Ge",
"Behnam Neyshabur",
"Yi Zhang"
] | null | null | Deep nets generalize well despite having more parameters than the number of training samples. Recent works try to give an explanation using PAC-Bayes and Margin-based analyses, but do not as yet result in sample complexity bounds better than naive parameter counting. The current paper shows generalization bounds that a... | [] | null | 27 | 1802.05296 | title_snapshot | [
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Lipschitz Continuity in Model-based Reinforcement Learning | https://proceedings.mlr.press/v80/asadi18a.html | [
"Kavosh Asadi",
"Dipendra Misra",
"Michael Littman"
] | null | null | We examine the impact of learning Lipschitz continuous models in the context of model-based reinforcement learning. We provide a novel bound on multi-step prediction error of Lipschitz models where we quantify the error using the Wasserstein metric. We go on to prove an error bound for the value-function estimate arisi... | [] | null | 28 | 1804.07193 | title_snapshot | [
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Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples | https://proceedings.mlr.press/v80/athalye18a.html | [
"Anish Athalye",
"Nicholas Carlini",
"David Wagner"
] | null | null | We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples. While defenses that cause obfuscated gradients appear to defeat iterative optimization-based attacks, we find defenses relying on this effect can be circumvente... | [] | null | 29 | 1802.00420 | title_snapshot | [
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Synthesizing Robust Adversarial Examples | https://proceedings.mlr.press/v80/athalye18b.html | [
"Anish Athalye",
"Logan Engstrom",
"Andrew Ilyas",
"Kevin Kwok"
] | null | null | Standard methods for generating adversarial examples for neural networks do not consistently fool neural network classifiers in the physical world due to a combination of viewpoint shifts, camera noise, and other natural transformations, limiting their relevance to real-world systems. We demonstrate the existence of ro... | [] | null | 30 | 1707.07397 | title_snapshot | [
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Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing | https://proceedings.mlr.press/v80/bacciu18a.html | [
"Davide Bacciu",
"Federico Errica",
"Alessio Micheli"
] | null | null | We introduce the Contextual Graph Markov Model, an approach combining ideas from generative models and neural networks for the processing of graph data. It founds on a constructive methodology to build a deep architecture comprising layers of probabilistic models that learn to encode the structured information in an in... | [] | null | 31 | 1805.10636 | title_snapshot | [
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Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions | https://proceedings.mlr.press/v80/bai18a.html | [
"Wenruo Bai",
"Jeff Bilmes"
] | null | null | We analyze the performance of the greedy algorithm, and also a discrete semi-gradient based algorithm, for maximizing the sum of a suBmodular and suPermodular (BP) function (both of which are non-negative monotone non-decreasing) under two types of constraints, either a cardinality constraint or $p\geq 1$ matroid indep... | [] | null | 32 | 1801.07413 | title_judge | [
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Comparing Dynamics: Deep Neural Networks versus Glassy Systems | https://proceedings.mlr.press/v80/baity-jesi18a.html | [
"Marco Baity-Jesi",
"Levent Sagun",
"Mario Geiger",
"Stefano Spigler",
"Gerard Ben Arous",
"Chiara Cammarota",
"Yann LeCun",
"Matthieu Wyart",
"Giulio Biroli"
] | null | null | We analyze numerically the training dynamics of deep neural networks (DNN) by using methods developed in statistical physics of glassy systems. The two main issues we address are the complexity of the loss-landscape and of the dynamics within it, and to what extent DNNs share similarities with glassy systems. Our findi... | [] | null | 33 | 1803.06969 | title_snapshot | [
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SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions | https://proceedings.mlr.press/v80/bajaj18a.html | [
"Chandrajit Bajaj",
"Tingran Gao",
"Zihang He",
"Qixing Huang",
"Zhenxiao Liang"
] | null | null | We introduce a principled approach forsimultaneous mapping and clustering(SMAC) for establishing consistent maps across heterogeneous object collections (e.g., 2D images or 3D shapes). Our approach takes as input a heterogeneous object collection and a set of maps computed between some pairs of objects, and outputs a h... | [] | null | 34 | null | null | [
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A Boo(n) for Evaluating Architecture Performance | https://proceedings.mlr.press/v80/bajgar18a.html | [
"Ondrej Bajgar",
"Rudolf Kadlec",
"Jan Kleindienst"
] | null | null | We point out important problems with the common practice of using the best single model performance for comparing deep learning architectures, and we propose a method that corrects these flaws. Each time a model is trained, one gets a different result due to random factors in the training process, which include random ... | [] | null | 35 | 1807.01961 | title_snapshot | [
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Learning to Branch | https://proceedings.mlr.press/v80/balcan18a.html | [
"Maria-Florina Balcan",
"Travis Dick",
"Tuomas Sandholm",
"Ellen Vitercik"
] | null | null | Tree search algorithms, such as branch-and-bound, are the most widely used tools for solving combinatorial problems. These algorithms recursively partition the search space to find an optimal solution. To keep the tree small, it is crucial to carefully decide, when expanding a tree node, which variable to branch on at ... | [] | null | 36 | 1803.10150 | title_snapshot | [
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The Mechanics of n-Player Differentiable Games | https://proceedings.mlr.press/v80/balduzzi18a.html | [
"David Balduzzi",
"Sebastien Racaniere",
"James Martens",
"Jakob Foerster",
"Karl Tuyls",
"Thore Graepel"
] | null | null | The cornerstone underpinning deep learning is the guarantee that gradient descent on an objective converges to local minima. Unfortunately, this guarantee fails in settings, such as generative adversarial nets, where there are multiple interacting losses. The behavior of gradient-based methods in games is not well unde... | [] | null | 37 | 1802.05642 | title_snapshot | [
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Spline Filters For End-to-End Deep Learning | https://proceedings.mlr.press/v80/balestriero18a.html | [
"Randall Balestriero",
"Romain Cosentino",
"Herve Glotin",
"Richard Baraniuk"
] | null | null | We propose to tackle the problem of end-to-end learning for raw waveform signals by introducing learnable continuous time-frequency atoms. The derivation of these filters is achieved by defining a functional space with a given smoothness order and boundary conditions. From this space, we derive the parametric analytica... | [] | null | 38 | null | null | [
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A Spline Theory of Deep Learning | https://proceedings.mlr.press/v80/balestriero18b.html | [
"Randall Balestriero",
"baraniuk"
] | null | null | We build a rigorous bridge between deep networks (DNs) and approximation theory via spline functions and operators. Our key result is that a large class of DNs can be written as a composition ofmax-affine spline operators(MASOs), which provide a powerful portal through which to view and analyze their inner workings. Fo... | [] | null | 39 | null | null | [
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Approximation Guarantees for Adaptive Sampling | https://proceedings.mlr.press/v80/balkanski18a.html | [
"Eric Balkanski",
"Yaron Singer"
] | null | null | In this paper we analyze an adaptive sampling approach for submodular maximization. Adaptive sampling is a technique that has recently been shown to achieve a constant factor approximation guarantee for submodular maximization under a cardinality constraint with exponentially fewer adaptive rounds than any previously s... | [] | null | 40 | null | null | [
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Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising | https://proceedings.mlr.press/v80/balle18a.html | [
"Borja Balle",
"Yu-Xiang Wang"
] | null | null | The Gaussian mechanism is an essential building block used in multitude of differentially private data analysis algorithms. In this paper we revisit the Gaussian mechanism and show that the original analysis has several important limitations. Our analysis reveals that the variance formula for the original mechanism is ... | [] | null | 41 | 1805.06530 | title_snapshot | [
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Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients | https://proceedings.mlr.press/v80/balles18a.html | [
"Lukas Balles",
"Philipp Hennig"
] | null | null | The ADAM optimizer is exceedingly popular in the deep learning community. Often it works very well, sometimes it doesn’t. Why? We interpret ADAM as a combination of two aspects: for each weight, the update direction is determined by the sign of stochastic gradients, whereas the update magnitude is determined by an esti... | [] | null | 42 | 1705.07774 | title_snapshot | [
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Differentially Private Database Release via Kernel Mean Embeddings | https://proceedings.mlr.press/v80/balog18a.html | [
"Matej Balog",
"Ilya Tolstikhin",
"Bernhard Schölkopf"
] | null | null | We lay theoretical foundations for new database release mechanisms that allow third-parties to construct consistent estimators of population statistics, while ensuring that the privacy of each individual contributing to the database is protected. The proposed framework rests on two main ideas. First, releasing (an esti... | [] | null | 43 | 1710.01641 | title_snapshot | [
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Improving Optimization for Models With Continuous Symmetry Breaking | https://proceedings.mlr.press/v80/bamler18a.html | [
"Robert Bamler",
"Stephan Mandt"
] | null | null | Many loss functions in representation learning are invariant under a continuous symmetry transformation. For example, the loss function of word embeddings (Mikolov et al., 2013) remains unchanged if we simultaneously rotate all word and context embedding vectors. We show that representation learning models for time ser... | [] | null | 44 | 1803.03234 | title_snapshot | [
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Improved Training of Generative Adversarial Networks Using Representative Features | https://proceedings.mlr.press/v80/bang18a.html | [
"Duhyeon Bang",
"Hyunjung Shim"
] | null | null | Despite the success of generative adversarial networks (GANs) for image generation, the trade-off between visual quality and image diversity remains a significant issue. This paper achieves both aims simultaneously by improving the stability of training GANs. The key idea of the proposed approach is to implicitly regul... | [] | null | 45 | 1801.09195 | title_snapshot | [
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Using Inherent Structures to design Lean 2-layer RBMs | https://proceedings.mlr.press/v80/bansal18a.html | [
"Abhishek Bansal",
"Abhinav Anand",
"Chiranjib Bhattacharyya"
] | null | null | Understanding the representational power of Restricted Boltzmann Machines (RBMs) with multiple layers is an ill-understood problem and is an area of active research. Motivated from the approach ofInherent Structure formalism(Stillinger & Weber, 1982), extensively used in analysing Spin Glasses, we propose a novel measu... | [] | null | 46 | 1806.04577 | title_snapshot | [
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Classification from Pairwise Similarity and Unlabeled Data | https://proceedings.mlr.press/v80/bao18a.html | [
"Han Bao",
"Gang Niu",
"Masashi Sugiyama"
] | null | null | Supervised learning needs a huge amount of labeled data, which can be a big bottleneck under the situation where there is a privacy concern or labeling cost is high. To overcome this problem, we propose a new weakly-supervised learning setting where only similar (S) data pairs (two examples belong to the same class) an... | [] | null | 47 | 1802.04381 | title_snapshot | [
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0.0... |
Bayesian Optimization of Combinatorial Structures | https://proceedings.mlr.press/v80/baptista18a.html | [
"Ricardo Baptista",
"Matthias Poloczek"
] | null | null | The optimization of expensive-to-evaluate black-box functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences. The combinatorial explosion of the search space and costly evaluations pose challenges for current techniques in discrete optimization and machine ... | [] | null | 48 | 1806.08838 | title_snapshot | [
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... |
Geodesic Convolutional Shape Optimization | https://proceedings.mlr.press/v80/baque18a.html | [
"Pierre Baque",
"Edoardo Remelli",
"Francois Fleuret",
"Pascal Fua"
] | null | null | Aerodynamic shape optimization has many industrial applications. Existing methods, however, are so computationally demanding that typical engineering practices are to either simply try a limited number of hand-designed shapes or restrict oneself to shapes that can be parameterized using only few degrees of freedom. In ... | [] | null | 49 | 1802.04016 | title_snapshot | [
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0.029227... |
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems | https://proceedings.mlr.press/v80/bargiacchi18a.html | [
"Eugenio Bargiacchi",
"Timothy Verstraeten",
"Diederik Roijers",
"Ann Nowé",
"Hado Hasselt"
] | null | null | Learning to coordinate between multiple agents is an important problem in many reinforcement learning problems. Key to learning to coordinate is exploiting loose couplings, i.e., conditional independences between agents. In this paper we study learning in repeated fully cooperative games, multi-agent multi-armed bandit... | [] | null | 50 | null | null | [
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Testing Sparsity over Known and Unknown Bases | https://proceedings.mlr.press/v80/barman18a.html | [
"Siddharth Barman",
"Arnab Bhattacharyya",
"Suprovat Ghoshal"
] | null | null | Sparsity is a basic property of real vectors that is exploited in a wide variety of machine learning applications. In this work, we describe property testing algorithms for sparsity that observe a low-dimensional projec- tion of the input. We consider two settings. In the first setting, we test sparsity with respect to... | [] | null | 51 | 1608.01275 | title_snapshot | [
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Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement | https://proceedings.mlr.press/v80/barreto18a.html | [
"Andre Barreto",
"Diana Borsa",
"John Quan",
"Tom Schaul",
"David Silver",
"Matteo Hessel",
"Daniel Mankowitz",
"Augustin Zidek",
"Remi Munos"
] | null | null | The ability to transfer skills across tasks has the potential to scale up reinforcement learning (RL) agents to environments currently out of reach. Recently, a framework based on two ideas, successor features (SFs) and generalised policy improvement (GPI), has been introduced as a principled way of transferring skills... | [] | null | 52 | 1901.10964 | title_snapshot | [
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Measuring abstract reasoning in neural networks | https://proceedings.mlr.press/v80/barrett18a.html | [
"David Barrett",
"Felix Hill",
"Adam Santoro",
"Ari Morcos",
"Timothy Lillicrap"
] | null | null | Whether neural networks can learn abstract reasoning or whether they merely rely on superficial statistics is a topic of recent debate. Here, we propose a dataset and challenge designed to probe abstract reasoning, inspired by a well-known human IQ test. To succeed at this challenge, models must cope with various gener... | [] | null | 53 | 1807.04225 | title_snapshot | [
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Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks | https://proceedings.mlr.press/v80/bartlett18a.html | [
"Peter Bartlett",
"Dave Helmbold",
"Philip Long"
] | null | null | We analyze algorithms for approximating a function $f(x) = \Phi x$ mapping $\Re^d$ to $\Re^d$ using deep linear neural networks, i.e. that learn a function $h$ parameterized by matrices $\Theta_1,...,\Theta_L$ and defined by $h(x) = \Theta_L \Theta_{L-1} ... \Theta_1 x$. We focus on algorithms that learn through gradie... | [] | null | 54 | 1802.06093 | title_snapshot | [
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Mutual Information Neural Estimation | https://proceedings.mlr.press/v80/belghazi18a.html | [
"Mohamed Ishmael Belghazi",
"Aristide Baratin",
"Sai Rajeshwar",
"Sherjil Ozair",
"Yoshua Bengio",
"Aaron Courville",
"Devon Hjelm"
] | null | null | We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent over neural networks. We present a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size, trainable through back-prop, an... | [] | null | 55 | null | null | [
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To Understand Deep Learning We Need to Understand Kernel Learning | https://proceedings.mlr.press/v80/belkin18a.html | [
"Mikhail Belkin",
"Siyuan Ma",
"Soumik Mandal"
] | null | null | Generalization performance of classifiers in deep learning has recently become a subject of intense study. Deep models, which are typically heavily over-parametrized, tend to fit the training data exactly. Despite this “overfitting", they perform well on test data, a phenomenon not yet fully understood. The first point... | [] | null | 56 | 1802.01396 | title_snapshot | [
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Understanding and Simplifying One-Shot Architecture Search | https://proceedings.mlr.press/v80/bender18a.html | [
"Gabriel Bender",
"Pieter-Jan Kindermans",
"Barret Zoph",
"Vijay Vasudevan",
"Quoc Le"
] | null | null | There is growing interest in automating neural network architecture design. Existing architecture search methods can be computationally expensive, requiring thousands of different architectures to be trained from scratch. Recent work has exploredweight sharingacross models to amortize the cost of training. Although pre... | [] | null | 57 | null | null | [
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signSGD: Compressed Optimisation for Non-Convex Problems | https://proceedings.mlr.press/v80/bernstein18a.html | [
"Jeremy Bernstein",
"Yu-Xiang Wang",
"Kamyar Azizzadenesheli",
"Animashree Anandkumar"
] | null | null | Training large neural networks requires distributing learning across multiple workers, where the cost of communicating gradients can be a significant bottleneck. signSGD alleviates this problem by transmitting just the sign of each minibatch stochastic gradient. We prove that it can get the best of both worlds: compres... | [] | null | 58 | 1802.04434 | title_snapshot | [
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Distributed Clustering via LSH Based Data Partitioning | https://proceedings.mlr.press/v80/bhaskara18a.html | [
"Aditya Bhaskara",
"Maheshakya Wijewardena"
] | null | null | Given the importance of clustering in the analysisof large scale data, distributed algorithms for formulations such as k-means, k-median, etc. have been extensively studied. A successful approach here has been the “reduce and merge” paradigm, in which each machine reduces its input size to {Õ}(k), and this data reducti... | [] | null | 59 | null | null | [
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Autoregressive Convolutional Neural Networks for Asynchronous Time Series | https://proceedings.mlr.press/v80/binkowski18a.html | [
"Mikolaj Binkowski",
"Gautier Marti",
"Philippe Donnat"
] | null | null | We propose Significance-Offset Convolutional Neural Network, a deep convolutional network architecture for regression of multivariate asynchronous time series. The model is inspired by standard autoregressive (AR) models and gating mechanisms used in recurrent neural networks. It involves an AR-like weighting system, w... | [] | null | 60 | 1703.04122 | title_snapshot | [
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Adaptive Sampled Softmax with Kernel Based Sampling | https://proceedings.mlr.press/v80/blanc18a.html | [
"Guy Blanc",
"Steffen Rendle"
] | null | null | Softmax is the most commonly used output function for multiclass problems and is widely used in areas such as vision, natural language processing, and recommendation. A softmax model has linear costs in the number of classes which makes it too expensive for many real-world problems. A common approach to speed up traini... | [] | null | 61 | 1712.00527 | title_snapshot | [
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Optimizing the Latent Space of Generative Networks | https://proceedings.mlr.press/v80/bojanowski18a.html | [
"Piotr Bojanowski",
"Armand Joulin",
"David Lopez-Pas",
"Arthur Szlam"
] | null | null | Generative Adversarial Networks (GANs) have achieved remarkable results in the task of generating realistic natural images. In most successful applications, GAN models share two common aspects: solving a challenging saddle point optimization problem, interpreted as an adversarial game between a generator and a discrimi... | [] | null | 62 | 1707.05776 | title_snapshot | [
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NetGAN: Generating Graphs via Random Walks | https://proceedings.mlr.press/v80/bojchevski18a.html | [
"Aleksandar Bojchevski",
"Oleksandr Shchur",
"Daniel Zügner",
"Stephan Günnemann"
] | null | null | We propose NetGAN - the first implicit generative model for graphs able to mimic real-world networks. We pose the problem of graph generation as learning the distribution of biased random walks over the input graph. The proposed model is based on a stochastic neural network that generates discrete output samples and is... | [] | null | 63 | 1803.00816 | title_snapshot | [
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A Progressive Batching L-BFGS Method for Machine Learning | https://proceedings.mlr.press/v80/bollapragada18a.html | [
"Raghu Bollapragada",
"Jorge Nocedal",
"Dheevatsa Mudigere",
"Hao-Jun Shi",
"Ping Tak Peter Tang"
] | null | null | The standard L-BFGS method relies on gradient approximations that are not dominated by noise, so that search directions are descent directions, the line search is reliable, and quasi-Newton updating yields useful quadratic models of the objective function. All of this appears to call for a full batch approach, but sinc... | [] | null | 64 | 1802.05374 | title_snapshot | [
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Prediction Rule Reshaping | https://proceedings.mlr.press/v80/bonakdarpour18a.html | [
"Matt Bonakdarpour",
"Sabyasachi Chatterjee",
"Rina Foygel Barber",
"John Lafferty"
] | null | null | Two methods are proposed for high-dimensional shape-constrained regression and classification. These methods reshape pre-trained prediction rules to satisfy shape constraints like monotonicity and convexity. The first method can be applied to any pre-trained prediction rule, while the second method deals specifically w... | [] | null | 65 | 1805.06439 | title_snapshot | [
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QuantTree: Histograms for Change Detection in Multivariate Data Streams | https://proceedings.mlr.press/v80/boracchi18a.html | [
"Giacomo Boracchi",
"Diego Carrera",
"Cristiano Cervellera",
"Danilo Macciò"
] | null | null | We address the problem of detecting distribution changes in multivariate data streams by means of histograms. Histograms are very general and flexible models, which have been relatively ignored in the change-detection literature as they often require a number of bins that grows unfeasibly with the data dimension. We pr... | [] | null | 66 | null | null | [
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Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order | https://proceedings.mlr.press/v80/braverman18a.html | [
"Vladimir Braverman",
"Stephen Chestnut",
"Robert Krauthgamer",
"Yi Li",
"David Woodruff",
"Lin Yang"
] | null | null | A central problem in mining massive data streams is characterizing which functions of an underlying frequency vector can be approximated efficiently. Given the prevalence of large scale linear algebra problems in machine learning, recently there has been considerable effort in extending this data stream problem to that... | [] | null | 67 | 1609.05885 | title_snapshot | [
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Predict and Constrain: Modeling Cardinality in Deep Structured Prediction | https://proceedings.mlr.press/v80/brukhim18a.html | [
"Nataly Brukhim",
"Amir Globerson"
] | null | null | Many machine learning problems require the prediction of multi-dimensional labels. Such structured prediction models can benefit from modeling dependencies between labels. Recently, several deep learning approaches to structured prediction have been proposed. Here we focus on capturing cardinality constraints in such m... | [] | null | 68 | 1802.04721 | title_snapshot | [
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Quasi-Monte Carlo Variational Inference | https://proceedings.mlr.press/v80/buchholz18a.html | [
"Alexander Buchholz",
"Florian Wenzel",
"Stephan Mandt"
] | null | null | Many machine learning problems involve Monte Carlo gradient estimators. As a prominent example, we focus on Monte Carlo variational inference (MCVI) in this paper. The performance of MCVI crucially depends on the variance of its stochastic gradients. We propose variance reduction by means of Quasi-Monte Carlo (QMC) sam... | [] | null | 69 | 1807.01604 | title_snapshot | [
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Path-Level Network Transformation for Efficient Architecture Search | https://proceedings.mlr.press/v80/cai18a.html | [
"Han Cai",
"Jiacheng Yang",
"Weinan Zhang",
"Song Han",
"Yong Yu"
] | null | null | We introduce a new function-preserving transformation for efficient neural architecture search. This network transformation allows reusing previously trained networks and existing successful architectures that improves sample efficiency. We aim to address the limitation of current network transformation operations that... | [] | null | 70 | 1806.02639 | title_snapshot | [
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Improved large-scale graph learning through ridge spectral sparsification | https://proceedings.mlr.press/v80/calandriello18a.html | [
"Daniele Calandriello",
"Alessandro Lazaric",
"Ioannis Koutis",
"Michal Valko"
] | null | null | The representation and learning benefits of methods based on graph Laplacians, such as Laplacian smoothing or harmonic function solution for semi-supervised learning (SSL), are empirically and theoretically well supported. Nonetheless, the exact versions of these methods scale poorly with the number of nodes $n$ of the... | [] | null | 71 | 2604.20078 | title_snapshot | [
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0.0150... |
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent | https://proceedings.mlr.press/v80/campbell18a.html | [
"Trevor Campbell",
"Tamara Broderick"
] | null | null | Coherent uncertainty quantification is a key strength of Bayesian methods. But modern algorithms for approximate Bayesian posterior inference often sacrifice accurate posterior uncertainty estimation in the pursuit of scalability. This work shows that previous Bayesian coreset construction algorithms—which build a smal... | [] | null | 72 | 1802.01737 | title_snapshot | [
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Adversarial Learning with Local Coordinate Coding | https://proceedings.mlr.press/v80/cao18a.html | [
"Jiezhang Cao",
"Yong Guo",
"Qingyao Wu",
"Chunhua Shen",
"Junzhou Huang",
"Mingkui Tan"
] | null | null | Generative adversarial networks (GANs) aim to generate realistic data from some prior distribution (e.g., Gaussian noises). However, such prior distribution is often independent of real data and thus may lose semantic information (e.g., geometric structure or content in images) of data. In practice, the semantic inform... | [] | null | 73 | 1806.04895 | title_snapshot | [
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... |
Fair and Diverse DPP-Based Data Summarization | https://proceedings.mlr.press/v80/celis18a.html | [
"Elisa Celis",
"Vijay Keswani",
"Damian Straszak",
"Amit Deshpande",
"Tarun Kathuria",
"Nisheeth Vishnoi"
] | null | null | Sampling methods that choose a subset of the data proportional to its diversity in the feature space are popular for data summarization. However, recent studies have noted the occurrence of bias {–} e.g., under or over representation of a particular gender or ethnicity {–} in such data summarization methods. In this pa... | [] | null | 74 | 1802.04023 | title_snapshot | [
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Conditional Noise-Contrastive Estimation of Unnormalised Models | https://proceedings.mlr.press/v80/ceylan18a.html | [
"Ciwan Ceylan",
"Michael U. Gutmann"
] | null | null | Many parametric statistical models are not properly normalised and only specified up to an intractable partition function, which renders parameter estimation difficult. Examples of unnormalised models are Gibbs distributions, Markov random fields, and neural network models in unsupervised deep learning. In previous wor... | [] | null | 75 | 1806.03664 | title_snapshot | [
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Adversarial Time-to-Event Modeling | https://proceedings.mlr.press/v80/chapfuwa18a.html | [
"Paidamoyo Chapfuwa",
"Chenyang Tao",
"Chunyuan Li",
"Courtney Page",
"Benjamin Goldstein",
"Lawrence Carin Duke",
"Ricardo Henao"
] | null | null | Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support clinical practice. Time-to-event analysis, also called survival analysis, stands as one of the most representative examples of such statisti... | [] | null | 76 | 1804.03184 | title_snapshot | [
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Stability and Generalization of Learning Algorithms that Converge to Global Optima | https://proceedings.mlr.press/v80/charles18a.html | [
"Zachary Charles",
"Dimitris Papailiopoulos"
] | null | null | We establish novel generalization bounds for learning algorithms that converge to global minima. We derive black-box stability results that only depend on the convergence of a learning algorithm and the geometry around the minimizers of the empirical risk function. The results are shown for non-convex loss functions sa... | [] | null | 77 | 1710.08402 | title_snapshot | [
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Learning and Memorization | https://proceedings.mlr.press/v80/chatterjee18a.html | [
"Satrajit Chatterjee"
] | null | null | In the machine learning research community, it is generally believed that there is a tension between memorization and generalization. In this work we examine to what extent this tension exists by exploring if it is possible to generalize by memorizing alone. Although direct memorization with a lookup table obviously do... | [] | null | 78 | null | null | [
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On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo | https://proceedings.mlr.press/v80/chatterji18a.html | [
"Niladri Chatterji",
"Nicolas Flammarion",
"Yian Ma",
"Peter Bartlett",
"Michael Jordan"
] | null | null | We provide convergence guarantees in Wasserstein distance for a variety of variance-reduction methods: SAGA Langevin diffusion, SVRG Langevin diffusion and control-variate underdamped Langevin diffusion. We analyze these methods under a uniform set of assumptions on the log-posterior distribution, assuming it to be smo... | [] | null | 79 | 1802.05431 | title_snapshot | [
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Hierarchical Clustering with Structural Constraints | https://proceedings.mlr.press/v80/chatziafratis18a.html | [
"Vaggos Chatziafratis",
"Rad Niazadeh",
"Moses Charikar"
] | null | null | Hierarchical clustering is a popular unsupervised data analysis method. For many real-world applications, we would like to exploit prior information about the data that imposes constraints on the clustering hierarchy, and is not captured by the set of features available to the algorithm. This gives rise to the problem ... | [] | null | 80 | 1805.09476 | title_snapshot | [
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Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series | https://proceedings.mlr.press/v80/che18a.html | [
"Zhengping Che",
"Sanjay Purushotham",
"Guangyu Li",
"Bo Jiang",
"Yan Liu"
] | null | null | Multi-Rate Multivariate Time Series (MR-MTS) are the multivariate time series observations which come with various sampling rates and encode multiple temporal dependencies. State-space models such as Kalman filters and deep learning models such as deep Markov models are mainly designed for time series data with the sam... | [] | null | 81 | null | null | [
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GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks | https://proceedings.mlr.press/v80/chen18a.html | [
"Zhao Chen",
"Vijay Badrinarayanan",
"Chen-Yu Lee",
"Andrew Rabinovich"
] | null | null | Deep multitask networks, in which one neural network produces multiple predictive outputs, can offer better speed and performance than their single-task counterparts but are challenging to train properly. We present a gradient normalization (GradNorm) algorithm that automatically balances training in deep multitask mod... | [] | null | 82 | 1711.02257 | title_snapshot | [
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Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy? | https://proceedings.mlr.press/v80/chen18b.html | [
"Lin Chen",
"Moran Feldman",
"Amin Karbasi"
] | null | null | Submodular functions are a broad class of set functions that naturally arise in many machine learning applications. Due to their combinatorial structures, there has been a myriad of algorithms for maximizing such functions under various constraints. Unfortunately, once a function deviates from submodularity (even sligh... | [] | null | 83 | 1707.04347 | title_snapshot | [
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Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity | https://proceedings.mlr.press/v80/chen18c.html | [
"Lin Chen",
"Christopher Harshaw",
"Hamed Hassani",
"Amin Karbasi"
] | null | null | Online optimization has been a successful framework for solving large-scale problems under computational constraints and partial information. Current methods for online convex optimization require either a projection or exact gradient computation at each step, both of which can be prohibitively expensive for large-scal... | [] | null | 84 | 1802.08183 | title_snapshot | [
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Continuous-Time Flows for Efficient Inference and Density Estimation | https://proceedings.mlr.press/v80/chen18d.html | [
"Changyou Chen",
"Chunyuan Li",
"Liqun Chen",
"Wenlin Wang",
"Yunchen Pu",
"Lawrence Carin Duke"
] | null | null | Two fundamental problems in unsupervised learning are efficient inference for latent-variable models and robust density estimation based on large amounts of unlabeled data. Algorithms for the two tasks, such as normalizing flows and generative adversarial networks (GANs), are often developed independently. In this pape... | [] | null | 85 | 1709.01179 | title_snapshot | [
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Scalable Bilinear Pi Learning Using State and Action Features | https://proceedings.mlr.press/v80/chen18e.html | [
"Yichen Chen",
"Lihong Li",
"Mengdi Wang"
] | null | null | Approximate linear programming (ALP) represents one of the major algorithmic families to solve large-scale Markov decision processes (MDP). In this work, we study a primal-dual formulation of the ALP, and develop a scalable, model-free algorithm called bilinear $\pi$ learning for reinforcement learning when a sampling ... | [] | null | 86 | null | null | [
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Stein Points | https://proceedings.mlr.press/v80/chen18f.html | [
"Wilson Ye Chen",
"Lester Mackey",
"Jackson Gorham",
"Francois-Xavier Briol",
"Chris Oates"
] | null | null | An important task in computational statistics and machine learning is to approximate a posterior distribution $p(x)$ with an empirical measure supported on a set of representative points $\{x_i\}_{i=1}^n$. This paper focuses on methods where the selection of points is essentially deterministic, with an emphasis on achi... | [] | null | 87 | 1803.10161 | title_snapshot | [
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Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations | https://proceedings.mlr.press/v80/chen18g.html | [
"Ting Chen",
"Martin Renqiang Min",
"Yizhou Sun"
] | null | null | Conventional embedding methods directly associate each symbol with a continuous embedding vector, which is equivalent to applying a linear transformation based on a “one-hot” encoding of the discrete symbols. Despite its simplicity, such approach yields the number of parameters that grows linearly with the vocabulary s... | [] | null | 88 | 1806.09464 | title_snapshot | [
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PixelSNAIL: An Improved Autoregressive Generative Model | https://proceedings.mlr.press/v80/chen18h.html | [
"XI Chen",
"Nikhil Mishra",
"Mostafa Rohaninejad",
"Pieter Abbeel"
] | null | null | Autoregressive generative models achieve the best results in density estimation tasks involving high dimensional data, such as images or audio. They pose density estimation as a sequence modeling task, where a recurrent neural network (RNN) models the conditional distribution over the next element conditioned on all pr... | [] | null | 89 | 1712.09763 | title_snapshot | [
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Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks | https://proceedings.mlr.press/v80/chen18i.html | [
"Minmin Chen",
"Jeffrey Pennington",
"Samuel Schoenholz"
] | null | null | Recurrent neural networks have gained widespread use in modeling sequence data across various domains. While many successful recurrent architectures employ a notion of gating, the exact mechanism that enables such remarkable performance is not well understood. We develop a theory for signal propagation in recurrent net... | [] | null | 90 | 1806.05394 | title_snapshot | [
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Learning to Explain: An Information-Theoretic Perspective on Model Interpretation | https://proceedings.mlr.press/v80/chen18j.html | [
"Jianbo Chen",
"Le Song",
"Martin Wainwright",
"Michael Jordan"
] | null | null | We introduce instancewise feature selection as a methodology for model interpretation. Our method is based on learning a function to extract a subset of features that are most informative for each given example. This feature selector is trained to maximize the mutual information between selected features and the respon... | [] | null | 91 | 1802.07814 | title_snapshot | [
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Variational Inference and Model Selection with Generalized Evidence Bounds | https://proceedings.mlr.press/v80/chen18k.html | [
"Liqun Chen",
"Chenyang Tao",
"Ruiyi Zhang",
"Ricardo Henao",
"Lawrence Carin Duke"
] | null | null | Recent advances on the scalability and flexibility of variational inference have made it successful at unravelling hidden patterns in complex data. In this work we propose a new variational bound formulation, yielding an estimator that extends beyond the conventional variational bound. It naturally subsumes the importa... | [] | null | 92 | null | null | [
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DRACO: Byzantine-resilient Distributed Training via Redundant Gradients | https://proceedings.mlr.press/v80/chen18l.html | [
"Lingjiao Chen",
"Hongyi Wang",
"Zachary Charles",
"Dimitris Papailiopoulos"
] | null | null | Distributed model training is vulnerable to byzantine system failures and adversarial compute nodes, i.e., nodes that use malicious updates to corrupt the global model stored at a parameter server (PS). To guarantee some form of robustness, recent work suggests using variants of the geometric median as an aggregation r... | [] | null | 93 | 1803.09877 | title_snapshot | [
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SADAGRAD: Strongly Adaptive Stochastic Gradient Methods | https://proceedings.mlr.press/v80/chen18m.html | [
"Zaiyi Chen",
"Yi Xu",
"Enhong Chen",
"Tianbao Yang"
] | null | null | Although the convergence rates of existing variants of ADAGRAD have a better dependence on the number of iterations under the strong convexity condition, their iteration complexities have a explicitly linear dependence on the dimensionality of the problem. To alleviate this bad dependence, we propose a simple yet novel... | [] | null | 94 | null | null | [
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Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization | https://proceedings.mlr.press/v80/chen18n.html | [
"Jinghui Chen",
"Pan Xu",
"Lingxiao Wang",
"Jian Ma",
"Quanquan Gu"
] | null | null | We propose a nonconvex estimator for the covariate adjusted precision matrix estimation problem in the high dimensional regime, under sparsity constraints. To solve this estimator, we propose an alternating gradient descent algorithm with hard thresholding. Compared with existing methods along this line of research, wh... | [] | null | 95 | null | null | [
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End-to-End Learning for the Deep Multivariate Probit Model | https://proceedings.mlr.press/v80/chen18o.html | [
"Di Chen",
"Yexiang Xue",
"Carla Gomes"
] | null | null | The multivariate probit model (MVP) is a popular classic model for studying binary responses of multiple entities. Nevertheless, the computational challenge of learning the MVP model, given that its likelihood involves integrating over a multidimensional constrained space of latent variables, significantly limits its a... | [] | null | 96 | 1803.08591 | title_snapshot | [
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Stochastic Training of Graph Convolutional Networks with Variance Reduction | https://proceedings.mlr.press/v80/chen18p.html | [
"Jianfei Chen",
"Jun Zhu",
"Le Song"
] | null | null | Graph convolutional networks (GCNs) are powerful deep neural networks for graph-structured data. However, GCN computes the representation of a node recursively from its neighbors, making the receptive field size grow exponentially with the number of layers. Previous attempts on reducing the receptive field size by subs... | [] | null | 97 | 1710.10568 | title_snapshot | [
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Extreme Learning to Rank via Low Rank Assumption | https://proceedings.mlr.press/v80/cheng18a.html | [
"Minhao Cheng",
"Ian Davidson",
"Cho-Jui Hsieh"
] | null | null | We consider the setting where we wish to perform ranking for hundreds of thousands of users which is common in recommender systems and web search ranking. Learning a single ranking function is unlikely to capture the variability across all users while learning a ranking function for each person is time-consuming and re... | [] | null | 98 | null | null | [
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Learning a Mixture of Two Multinomial Logits | https://proceedings.mlr.press/v80/chierichetti18a.html | [
"Flavio Chierichetti",
"Ravi Kumar",
"Andrew Tomkins"
] | null | null | The classical Multinomial Logit (MNL) is a behavioral model for user choice. In this model, a user is offered a slate of choices (a subset of a finite universe of $n$ items), and selects exactly one item from the slate, each with probability proportional to its (positive) weight. Given a set of observed slates and choi... | [] | null | 99 | null | null | [
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Structured Evolution with Compact Architectures for Scalable Policy Optimization | https://proceedings.mlr.press/v80/choromanski18a.html | [
"Krzysztof Choromanski",
"Mark Rowland",
"Vikas Sindhwani",
"Richard Turner",
"Adrian Weller"
] | null | null | We present a new method of blackbox optimization via gradient approximation with the use of structured random orthogonal matrices, providing more accurate estimators than baselines and with provable theoretical guarantees. We show that this algorithm can be successfully applied to learn better quality compact policies ... | [] | null | 100 | 1804.02395 | title_snapshot | [
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