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AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs | https://proceedings.mlr.press/v97/abbati19a.html | [
"Gabriele Abbati",
"Philippe Wenk",
"Michael A. Osborne",
"Andreas Krause",
"Bernhard Schölkopf",
"Stefan Bauer"
] | null | null | Stochastic differential equations are an important modeling class in many disciplines. Consequently, there exist many methods relying on various discretization and numerical integration schemes. In this paper, we propose a novel, probabilistic model for estimating the drift and diffusion given noisy observations of the... | [] | null | 1 | 1902.08480 | title_snapshot | [
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Dynamic Weights in Multi-Objective Deep Reinforcement Learning | https://proceedings.mlr.press/v97/abels19a.html | [
"Axel Abels",
"Diederik Roijers",
"Tom Lenaerts",
"Ann Nowé",
"Denis Steckelmacher"
] | null | null | Many real-world decision problems are characterized by multiple conflicting objectives which must be balanced based on their relative importance. In the dynamic weights setting the relative importance changes over time and specialized algorithms that deal with such change, such as a tabular Reinforcement Learning (RL) ... | [] | null | 2 | 1809.07803 | title_snapshot | [
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MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing | https://proceedings.mlr.press/v97/abu-el-haija19a.html | [
"Sami Abu-El-Haija",
"Bryan Perozzi",
"Amol Kapoor",
"Nazanin Alipourfard",
"Kristina Lerman",
"Hrayr Harutyunyan",
"Greg Ver Steeg",
"Aram Galstyan"
] | null | null | Existing popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mixing relationships. To address this weakness, we propose a new model, MixHop, that can learn these relationships, including difference operat... | [] | null | 3 | 1905.00067 | title_snapshot | [
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Communication-Constrained Inference and the Role of Shared Randomness | https://proceedings.mlr.press/v97/acharya19a.html | [
"Jayadev Acharya",
"Clement Canonne",
"Himanshu Tyagi"
] | null | null | A central server needs to perform statistical inference based on samples that are distributed over multiple users who can each send a message of limited length to the center. We study problems of distribution learning and identity testing in this distributed inference setting and examine the role of shared randomness a... | [] | null | 4 | 1905.08302 | title_judge | [
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Distributed Learning with Sublinear Communication | https://proceedings.mlr.press/v97/acharya19b.html | [
"Jayadev Acharya",
"Chris De Sa",
"Dylan Foster",
"Karthik Sridharan"
] | null | null | In distributed statistical learning, $N$ samples are split across $m$ machines and a learner wishes to use minimal communication to learn as well as if the examples were on a single machine. This model has received substantial interest in machine learning due to its scalability and potential for parallel speedup. Howev... | [] | null | 5 | 1902.11259 | title_snapshot | [
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Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters | https://proceedings.mlr.press/v97/acharya19c.html | [
"Jayadev Acharya",
"Ziteng Sun"
] | null | null | We consider the problems of distribution estimation, and heavy hitter (frequency) estimation under privacy, and communication constraints. While the constraints have been studied separately, optimal schemes for one are sub-optimal for the other. We propose a sample-optimal $\eps$-locally differentially private (LDP) sc... | [] | null | 6 | 1905.11888 | title_snapshot | [
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Learning Models from Data with Measurement Error: Tackling Underreporting | https://proceedings.mlr.press/v97/adams19a.html | [
"Roy Adams",
"Yuelong Ji",
"Xiaobin Wang",
"Suchi Saria"
] | null | null | Measurement error in observational datasets can lead to systematic bias in inferences based on these datasets. As studies based on observational data are increasingly used to inform decisions with real-world impact, it is critical that we develop a robust set of techniques for analyzing and adjusting for these biases. ... | [] | null | 7 | 1901.09060 | title_snapshot | [
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TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning | https://proceedings.mlr.press/v97/adel19a.html | [
"Tameem Adel",
"Adrian Weller"
] | null | null | One of the challenges to reinforcement learning (RL) is scalable transferability among complex tasks. Incorporating a graphical model (GM), along with the rich family of related methods, as a basis for RL frameworks provides potential to address issues such as transferability, generalisation and exploration. Here we pr... | [] | null | 8 | null | null | [
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PAC Learnability of Node Functions in Networked Dynamical Systems | https://proceedings.mlr.press/v97/adiga19a.html | [
"Abhijin Adiga",
"Chris J Kuhlman",
"Madhav Marathe",
"S Ravi",
"Anil Vullikanti"
] | null | null | We consider the PAC learnability of the local functions at the vertices of a discrete networked dynamical system, assuming that the underlying network is known. Our focus is on the learnability of threshold functions. We show that several variants of threshold functions are PAC learnable and provide tight bounds on the... | [] | null | 9 | null | null | [
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Static Automatic Batching In TensorFlow | https://proceedings.mlr.press/v97/agarwal19a.html | [
"Ashish Agarwal"
] | null | null | Dynamic neural networks are becoming increasingly common, and yet it is hard to implement them efficiently. On-the-fly operation batching for such models is sub-optimal and suffers from run time overheads, while writing manually batched versions can be hard and error-prone. To address this we extend TensorFlow with pfo... | [] | null | 10 | null | null | [
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Efficient Full-Matrix Adaptive Regularization | https://proceedings.mlr.press/v97/agarwal19b.html | [
"Naman Agarwal",
"Brian Bullins",
"Xinyi Chen",
"Elad Hazan",
"Karan Singh",
"Cyril Zhang",
"Yi Zhang"
] | null | null | Adaptive regularization methods pre-multiply a descent direction by a preconditioning matrix. Due to the large number of parameters of machine learning problems, full-matrix preconditioning methods are prohibitively expensive. We show how to modify full-matrix adaptive regularization in order to make it practical and e... | [] | null | 11 | 1806.02958 | title_snapshot | [
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Online Control with Adversarial Disturbances | https://proceedings.mlr.press/v97/agarwal19c.html | [
"Naman Agarwal",
"Brian Bullins",
"Elad Hazan",
"Sham Kakade",
"Karan Singh"
] | null | null | We study the control of linear dynamical systems with adversarial disturbances, as opposed to statistical noise. We present an efficient algorithm that achieves nearly-tight regret bounds in this setting. Our result generalizes upon previous work in two main aspects: the algorithm can accommodate adversarial noise in t... | [] | null | 12 | 1902.08721 | title_snapshot | [
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Fair Regression: Quantitative Definitions and Reduction-Based Algorithms | https://proceedings.mlr.press/v97/agarwal19d.html | [
"Alekh Agarwal",
"Miroslav Dudik",
"Zhiwei Steven Wu"
] | null | null | In this paper, we study the prediction of a real-valued target, such as a risk score or recidivism rate, while guaranteeing a quantitative notion of fairness with respect to a protected attribute such as gender or race. We call this class of problems fair regression. We propose general schemes for fair regression under... | [] | null | 13 | 1905.12843 | title_snapshot | [
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Learning to Generalize from Sparse and Underspecified Rewards | https://proceedings.mlr.press/v97/agarwal19e.html | [
"Rishabh Agarwal",
"Chen Liang",
"Dale Schuurmans",
"Mohammad Norouzi"
] | null | null | We consider the problem of learning from sparse and underspecified rewards, where an agent receives a complex input, such as a natural language instruction, and needs to generate a complex response, such as an action sequence, while only receiving binary success-failure feedback. Such success-failure rewards are often ... | [] | null | 14 | 1902.07198 | title_snapshot | [
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The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions | https://proceedings.mlr.press/v97/agrawal19a.html | [
"Raj Agrawal",
"Brian Trippe",
"Jonathan Huggins",
"Tamara Broderick"
] | null | null | Discovering interaction effects on a response of interest is a fundamental problem faced in biology, medicine, economics, and many other scientific disciplines. In theory, Bayesian methods for discovering pairwise interactions enjoy many benefits such as coherent uncertainty quantification, the ability to incorporate b... | [] | null | 15 | 1905.06501 | title_snapshot | [
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Understanding the Impact of Entropy on Policy Optimization | https://proceedings.mlr.press/v97/ahmed19a.html | [
"Zafarali Ahmed",
"Nicolas Le Roux",
"Mohammad Norouzi",
"Dale Schuurmans"
] | null | null | Entropy regularization is commonly used to improve policy optimization in reinforcement learning. It is believed to help with exploration by encouraging the selection of more stochastic policies. In this work, we analyze this claim using new visualizations of the optimization landscape based on randomly perturbing the ... | [] | null | 16 | 1811.11214 | title_snapshot | [
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Fairwashing: the risk of rationalization | https://proceedings.mlr.press/v97/aivodji19a.html | [
"Ulrich Aivodji",
"Hiromi Arai",
"Olivier Fortineau",
"Sébastien Gambs",
"Satoshi Hara",
"Alain Tapp"
] | null | null | Black-box explanation is the problem of explaining how a machine learning model – whose internal logic is hidden to the auditor and generally complex – produces its outcomes. Current approaches for solving this problem include model explanation, outcome explanation as well as model inspection. While these techniques ca... | [] | null | 17 | 1901.09749 | title_snapshot | [
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Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search | https://proceedings.mlr.press/v97/akimoto19a.html | [
"Youhei Akimoto",
"Shinichi Shirakawa",
"Nozomu Yoshinari",
"Kento Uchida",
"Shota Saito",
"Kouhei Nishida"
] | null | null | High sensitivity of neural architecture search (NAS) methods against their input such as step-size (i.e., learning rate) and search space prevents practitioners from applying them out-of-the-box to their own problems, albeit its purpose is to automate a part of tuning process. Aiming at a fast, robust, and widely-appli... | [] | null | 18 | 1905.08537 | title_snapshot | [
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Projections for Approximate Policy Iteration Algorithms | https://proceedings.mlr.press/v97/akrour19a.html | [
"Riad Akrour",
"Joni Pajarinen",
"Jan Peters",
"Gerhard Neumann"
] | null | null | Approximate policy iteration is a class of reinforcement learning (RL) algorithms where the policy is encoded using a function approximator and which has been especially prominent in RL with continuous action spaces. In this class of RL algorithms, ensuring increase of the policy return during policy update often requi... | [] | null | 19 | null | null | [
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Validating Causal Inference Models via Influence Functions | https://proceedings.mlr.press/v97/alaa19a.html | [
"Ahmed Alaa",
"Mihaela Van Der Schaar"
] | null | null | The problem of estimating causal effects of treatments from observational data falls beyond the realm of supervised learning {—} because counterfactual data is inaccessible, we can never observe the true causal effects. In the absence of "supervision", how can we evaluate the performance of causal inference methods? In... | [] | null | 20 | null | null | [
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Multi-objective training of Generative Adversarial Networks with multiple discriminators | https://proceedings.mlr.press/v97/albuquerque19a.html | [
"Isabela Albuquerque",
"Joao Monteiro",
"Thang Doan",
"Breandan Considine",
"Tiago Falk",
"Ioannis Mitliagkas"
] | null | null | Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in contrast to the traditional game involving one generator against a single adversary. Such methods perform single-objective optimization on some simple consolidation of the losses, e... | [] | null | 21 | 1901.08680 | title_snapshot | [
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Graph Element Networks: adaptive, structured computation and memory | https://proceedings.mlr.press/v97/alet19a.html | [
"Ferran Alet",
"Adarsh Keshav Jeewajee",
"Maria Bauza Villalonga",
"Alberto Rodriguez",
"Tomas Lozano-Perez",
"Leslie Kaelbling"
] | null | null | We explore the use of graph neural networks (GNNs) to model spatial processes in which there is no a priori graphical structure. Similar to finite element analysis, we assign nodes of a GNN to spatial locations and use a computational process defined on the graph to model the relationship between an initial function de... | [] | null | 22 | 1904.09019 | title_snapshot | [
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Analogies Explained: Towards Understanding Word Embeddings | https://proceedings.mlr.press/v97/allen19a.html | [
"Carl Allen",
"Timothy Hospedales"
] | null | null | Word embeddings generated by neural network methods such as word2vec (W2V) are well known to exhibit seemingly linear behaviour, e.g. the embeddings of analogy “woman is to queen as man is to king” approximately describe a parallelogram. This property is particularly intriguing since the embeddings are not trained to a... | [] | null | 23 | 1901.09813 | title_snapshot | [
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Infinite Mixture Prototypes for Few-shot Learning | https://proceedings.mlr.press/v97/allen19b.html | [
"Kelsey Allen",
"Evan Shelhamer",
"Hanul Shin",
"Joshua Tenenbaum"
] | null | null | We propose infinite mixture prototypes to adaptively represent both simple and complex data distributions for few-shot learning. Infinite mixture prototypes combine deep representation learning with Bayesian nonparametrics, representing each class by a set of clusters, unlike existing prototypical methods that represen... | [] | null | 24 | 1902.04552 | title_snapshot | [
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A Convergence Theory for Deep Learning via Over-Parameterization | https://proceedings.mlr.press/v97/allen-zhu19a.html | [
"Zeyuan Allen-Zhu",
"Yuanzhi Li",
"Zhao Song"
] | null | null | Deep neural networks (DNNs) have demonstrated dominating performance in many fields; since AlexNet, networks used in practice are going wider and deeper. On the theoretical side, a long line of works have been focusing on why we can train neural networks when there is only one hidden layer. The theory of multi-layer ne... | [] | null | 25 | 1811.03962 | title_snapshot | [
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Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation | https://proceedings.mlr.press/v97/alvi19a.html | [
"Ahsan Alvi",
"Binxin Ru",
"Jan-Peter Calliess",
"Stephen Roberts",
"Michael A. Osborne"
] | null | null | Batch Bayesian optimisation (BO) has been successfully applied to hyperparameter tuning using parallel computing, but it is wasteful of resources: workers that complete jobs ahead of others are left idle. We address this problem by developing an approach, Penalising Locally for Asynchronous Bayesian Optimisation on K W... | [] | null | 26 | 1901.10452 | title_snapshot | [
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Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy | https://proceedings.mlr.press/v97/amin19a.html | [
"Kareem Amin",
"Alex Kulesza",
"Andres Munoz",
"Sergei Vassilvtiskii"
] | null | null | Differentially private learning algorithms protect individual participants in the training dataset by guaranteeing that their presence does not significantly change the resulting model. In order to make this promise, such algorithms need to know the maximum contribution that can be made by a single user: the more data ... | [] | null | 27 | null | null | [
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Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation | https://proceedings.mlr.press/v97/ancona19a.html | [
"Marco Ancona",
"Cengiz Oztireli",
"Markus Gross"
] | null | null | The problem of explaining the behavior of deep neural networks has recently gained a lot of attention. While several attribution methods have been proposed, most come without strong theoretical foundations, which raises questions about their reliability. On the other hand, the literature on cooperative game theory sugg... | [] | null | 28 | 1903.10992 | title_judge | [
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Scaling Up Ordinal Embedding: A Landmark Approach | https://proceedings.mlr.press/v97/anderton19a.html | [
"Jesse Anderton",
"Javed Aslam"
] | null | null | Ordinal Embedding is the problem of placing n objects into R^d to satisfy constraints like "object a is closer to b than to c." It can accommodate data that embeddings from features or distances cannot, but is a more difficult problem. We propose a novel landmark-based method as a partial solution. At small to medium s... | [] | null | 29 | null | null | [
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Sorting Out Lipschitz Function Approximation | https://proceedings.mlr.press/v97/anil19a.html | [
"Cem Anil",
"James Lucas",
"Roger Grosse"
] | null | null | Training neural networks under a strict Lipschitz constraint is useful for provable adversarial robustness, generalization bounds, interpretable gradients, and Wasserstein distance estimation. By the composition property of Lipschitz functions, it suffices to ensure that each individual affine transformation or nonline... | [] | null | 30 | 1811.05381 | title_snapshot | [
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Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data | https://proceedings.mlr.press/v97/antelmi19a.html | [
"Luigi Antelmi",
"Nicholas Ayache",
"Philippe Robert",
"Marco Lorenzi"
] | null | null | Interpretable modeling of heterogeneous data channels is essential in medical applications, for example when jointly analyzing clinical scores and medical images. Variational Autoencoders (VAE) are powerful generative models that learn representations of complex data. The flexibility of VAE may come at the expense of l... | [] | null | 31 | null | null | [
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Unsupervised Label Noise Modeling and Loss Correction | https://proceedings.mlr.press/v97/arazo19a.html | [
"Eric Arazo",
"Diego Ortego",
"Paul Albert",
"Noel O’Connor",
"Kevin Mcguinness"
] | null | null | Despite being robust to small amounts of label noise, convolutional neural networks trained with stochastic gradient methods have been shown to easily fit random labels. When there are a mixture of correct and mislabelled targets, networks tend to fit the former before the latter. This suggests using a suitable two-com... | [] | null | 32 | 1904.11238 | title_snapshot | [
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Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks | https://proceedings.mlr.press/v97/arora19a.html | [
"Sanjeev Arora",
"Simon Du",
"Wei Hu",
"Zhiyuan Li",
"Ruosong Wang"
] | null | null | Recent works have cast some light on the mystery of why deep nets fit any data and generalize despite being very overparametrized. This paper analyzes training and generalization for a simple 2-layer ReLU net with random initialization, and provides the following improvements over recent works: (i) Using a tighter char... | [] | null | 33 | 1901.08584 | title_snapshot | [
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Distributed Weighted Matching via Randomized Composable Coresets | https://proceedings.mlr.press/v97/assadi19a.html | [
"Sepehr Assadi",
"Mohammadhossein Bateni",
"Vahab Mirrokni"
] | null | null | Maximum weight matching is one of the most fundamental combinatorial optimization problems with a wide range of applications in data mining and bioinformatics. Developing distributed weighted matching algorithms has been challenging due to the sequential nature of efficient algorithms for this problem. In this paper, w... | [] | null | 34 | 1906.01993 | title_snapshot | [
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Stochastic Gradient Push for Distributed Deep Learning | https://proceedings.mlr.press/v97/assran19a.html | [
"Mahmoud Assran",
"Nicolas Loizou",
"Nicolas Ballas",
"Mike Rabbat"
] | null | null | Distributed data-parallel algorithms aim to accelerate the training of deep neural networks by parallelizing the computation of large mini-batch gradient updates across multiple nodes. Approaches that synchronize nodes using exact distributed averaging (e.g., via AllReduce) are sensitive to stragglers and communication... | [] | null | 35 | 1811.10792 | title_snapshot | [
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Bayesian Optimization of Composite Functions | https://proceedings.mlr.press/v97/astudillo19a.html | [
"Raul Astudillo",
"Peter Frazier"
] | null | null | We consider optimization of composite objective functions, i.e., of the form $f(x)=g(h(x))$, where $h$ is a black-box derivative-free expensive-to-evaluate function with vector-valued outputs, and $g$ is a cheap-to-evaluate real-valued function. While these problems can be solved with standard Bayesian optimization, we... | [] | null | 36 | 1906.01537 | title_snapshot | [
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Linear-Complexity Data-Parallel Earth Mover’s Distance Approximations | https://proceedings.mlr.press/v97/atasu19a.html | [
"Kubilay Atasu",
"Thomas Mittelholzer"
] | null | null | The Earth Mover’s Distance (EMD) is a state-of-the art metric for comparing discrete probability distributions, but its high distinguishability comes at a high cost in computational complexity. Even though linear-complexity approximation algorithms have been proposed to improve its scalability, these algorithms are eit... | [] | null | 37 | 1812.02091 | title_judge | [
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Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA | https://proceedings.mlr.press/v97/awan19a.html | [
"Jordan Awan",
"Ana Kenney",
"Matthew Reimherr",
"Aleksandra Slavković"
] | null | null | The exponential mechanism is a fundamental tool of Differential Privacy (DP) due to its strong privacy guarantees and flexibility. We study its extension to settings with summaries based on infinite dimensional outputs such as with functional data analysis, shape analysis, and nonparametric statistics. We show that the... | [] | null | 38 | 1901.10864 | title_snapshot | [
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Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data | https://proceedings.mlr.press/v97/aydore19a.html | [
"Sergul Aydore",
"Bertrand Thirion",
"Gael Varoquaux"
] | null | null | In many applications where collecting data is expensive, for example neuroscience or medical imaging, the sample size is typically small compared to the feature dimension. These datasets call for intelligent regularization that exploits known structure, such as correlations between the features arising from the measure... | [] | null | 39 | 1807.11718 | title_snapshot | [
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Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior | https://proceedings.mlr.press/v97/ayed19a.html | [
"Fadhel Ayed",
"Juho Lee",
"Francois Caron"
] | null | null | Bayesian nonparametric approaches, in particular the Pitman-Yor process and the associated two-parameter Chinese Restaurant process, have been successfully used in applications where the data exhibit a power-law behavior. Examples include natural language processing, natural images or networks. There is also growing em... | [] | null | 40 | 1902.04714 | title_snapshot | [
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Scalable Fair Clustering | https://proceedings.mlr.press/v97/backurs19a.html | [
"Arturs Backurs",
"Piotr Indyk",
"Krzysztof Onak",
"Baruch Schieber",
"Ali Vakilian",
"Tal Wagner"
] | null | null | We study the fair variant of the classic k-median problem introduced by (Chierichetti et al., NeurIPS 2017) in which the points are colored, and the goal is to minimize the same average distance objective as in the standard $k$-median problem while ensuring that all clusters have an “approximately equal” number of poin... | [] | null | 41 | 1902.03519 | title_snapshot | [
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Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs | https://proceedings.mlr.press/v97/balaji19a.html | [
"Yogesh Balaji",
"Hamed Hassani",
"Rama Chellappa",
"Soheil Feizi"
] | null | null | Building on the success of deep learning, two modern approaches to learn a probability model from the data are Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs). VAEs consider an explicit probability model for the data and compute a generative distribution by maximizing a variational lower-boun... | [] | null | 42 | 1810.04147 | title_snapshot | [
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Provable Guarantees for Gradient-Based Meta-Learning | https://proceedings.mlr.press/v97/balcan19a.html | [
"Maria-Florina Balcan",
"Mikhail Khodak",
"Ameet Talwalkar"
] | null | null | We study the problem of meta-learning through the lens of online convex optimization, developing a meta-algorithm bridging the gap between popular gradient-based meta-learning and classical regularization-based multi-task transfer methods. Our method is the first to simultaneously satisfy good sample efficiency guarant... | [] | null | 43 | 1902.10644 | title_snapshot | [
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Open-ended learning in symmetric zero-sum games | https://proceedings.mlr.press/v97/balduzzi19a.html | [
"David Balduzzi",
"Marta Garnelo",
"Yoram Bachrach",
"Wojciech Czarnecki",
"Julien Perolat",
"Max Jaderberg",
"Thore Graepel"
] | null | null | Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of agents, for example labeling them ‘winner’ and ‘loser’. If the game is approximately transitive, then self-play generates sequences of agents of increasing strength. However, nontransitive games, such as rock-paper-scissors, can ex... | [] | null | 44 | 1901.08106 | title_snapshot | [
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Concrete Autoencoders: Differentiable Feature Selection and Reconstruction | https://proceedings.mlr.press/v97/balin19a.html | [
"Muhammed Fatih Balın",
"Abubakar Abid",
"James Zou"
] | null | null | We introduce the concrete autoencoder, an end-to-end differentiable method for global feature selection, which efficiently identifies a subset of the most informative features and simultaneously learns a neural network to reconstruct the input data from the selected features. Our method is unsupervised, and is based on... | [] | null | 45 | 1901.09346 | title_judge | [
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HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving | https://proceedings.mlr.press/v97/bansal19a.html | [
"Kshitij Bansal",
"Sarah Loos",
"Markus Rabe",
"Christian Szegedy",
"Stewart Wilcox"
] | null | null | We present an environment, benchmark, and deep learning driven automated theorem prover for higher-order logic. Higher-order interactive theorem provers enable the formalization of arbitrary mathematical theories and thereby present an interesting challenge for deep learning. We provide an open-source framework based o... | [] | null | 46 | 1904.03241 | title_judge | [
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Structured agents for physical construction | https://proceedings.mlr.press/v97/bapst19a.html | [
"Victor Bapst",
"Alvaro Sanchez-Gonzalez",
"Carl Doersch",
"Kimberly Stachenfeld",
"Pushmeet Kohli",
"Peter Battaglia",
"Jessica Hamrick"
] | null | null | Physical construction—the ability to compose objects, subject to physical dynamics, to serve some function—is fundamental to human intelligence. We introduce a suite of challenging physical construction tasks inspired by how children play with blocks, such as matching a target configuration, stacking blocks to connect ... | [] | null | 47 | 1904.03177 | title_snapshot | [
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Learning to Route in Similarity Graphs | https://proceedings.mlr.press/v97/baranchuk19a.html | [
"Dmitry Baranchuk",
"Dmitry Persiyanov",
"Anton Sinitsin",
"Artem Babenko"
] | null | null | Recently similarity graphs became the leading paradigm for efficient nearest neighbor search, outperforming traditional tree-based and LSH-based methods. Similarity graphs perform the search via greedy routing: a query traverses the graph and in each vertex moves to the adjacent vertex that is the closest to this query... | [] | null | 48 | 1905.10987 | title_snapshot | [
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A Personalized Affective Memory Model for Improving Emotion Recognition | https://proceedings.mlr.press/v97/barros19a.html | [
"Pablo Barros",
"German Parisi",
"Stefan Wermter"
] | null | null | Recent models of emotion recognition strongly rely on supervised deep learning solutions for the distinction of general emotion expressions. However, they are not reliable when recognizing online and personalized facial expressions, e.g., for person-specific affective understanding. In this paper, we present a neural m... | [] | null | 49 | 1904.12632 | title_judge | [
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Scale-free adaptive planning for deterministic dynamics & discounted rewards | https://proceedings.mlr.press/v97/bartlett19a.html | [
"Peter Bartlett",
"Victor Gabillon",
"Jennifer Healey",
"Michal Valko"
] | null | null | We address the problem of planning in an environment with deterministic dynamics and stochastic discounted rewards under a limited numerical budget where the ranges of both rewards and noise are unknown. We introduce PlaTypOOS, an adaptive, robust, and efficient alternative to the OLOP (open-loop optimistic planning) a... | [] | null | 50 | 2604.18312 | title_snapshot | [
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Pareto Optimal Streaming Unsupervised Classification | https://proceedings.mlr.press/v97/basu19a.html | [
"Soumya Basu",
"Steven Gutstein",
"Brent Lance",
"Sanjay Shakkottai"
] | null | null | We study an online and streaming unsupervised classification system. Our setting consists of a collection of classifiers (with unknown confusion matrices) each of which can classify one sample per unit time, and which are accessed by a stream of unlabeled samples. Each sample is dispatched to one or more classifiers, a... | [] | null | 51 | null | null | [
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Categorical Feature Compression via Submodular Optimization | https://proceedings.mlr.press/v97/bateni19a.html | [
"Mohammadhossein Bateni",
"Lin Chen",
"Hossein Esfandiari",
"Thomas Fu",
"Vahab Mirrokni",
"Afshin Rostamizadeh"
] | null | null | In the era of big data, learning from categorical features with very large vocabularies (e.g., 28 million for the Criteo click prediction dataset) has become a practical challenge for machine learning researchers and practitioners. We design a highly-scalable vocabulary compression algorithm that seeks to maximize the ... | [] | null | 52 | 1904.13389 | title_snapshot | [
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Noise2Self: Blind Denoising by Self-Supervision | https://proceedings.mlr.press/v97/batson19a.html | [
"Joshua Batson",
"Loic Royer"
] | null | null | We propose a general framework for denoising high-dimensional measurements which requires no prior on the signal, no estimate of the noise, and no clean training data. The only assumption is that the noise exhibits statistical independence across different dimensions of the measurement, while the true signal exhibits s... | [] | null | 53 | 1901.11365 | title_snapshot | [
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Efficient optimization of loops and limits with randomized telescoping sums | https://proceedings.mlr.press/v97/beatson19a.html | [
"Alex Beatson",
"Ryan P Adams"
] | null | null | We consider optimization problems in which the objective requires an inner loop with many steps or is the limit of a sequence of increasingly costly approximations. Meta-learning, training recurrent neural networks, and optimization of the solutions to differential equations are all examples of optimization problems wi... | [] | null | 54 | 1905.07006 | title_snapshot | [
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Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces | https://proceedings.mlr.press/v97/becker19a.html | [
"Philipp Becker",
"Harit Pandya",
"Gregor Gebhardt",
"Cheng Zhao",
"C. James Taylor",
"Gerhard Neumann"
] | null | null | In order to integrate uncertainty estimates into deep time-series modelling, Kalman Filters (KFs) (Kalman et al., 1960) have been integrated with deep learning models, however, such approaches typically rely on approximate inference tech- niques such as variational inference which makes learning more complex and often ... | [] | null | 55 | 1905.07357 | title_snapshot | [
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Switching Linear Dynamics for Variational Bayes Filtering | https://proceedings.mlr.press/v97/becker-ehmck19a.html | [
"Philip Becker-Ehmck",
"Jan Peters",
"Patrick Van Der Smagt"
] | null | null | System identification of complex and nonlinear systems is a central problem for model predictive control and model-based reinforcement learning. Despite their complexity, such systems can often be approximated well by a set of linear dynamical systems if broken into appropriate subsequences. This mechanism not only hel... | [] | null | 56 | 1905.12434 | title_snapshot | [
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Active Learning for Probabilistic Structured Prediction of Cuts and Matchings | https://proceedings.mlr.press/v97/behpour19a.html | [
"Sima Behpour",
"Anqi Liu",
"Brian Ziebart"
] | null | null | Active learning methods, like uncertainty sampling, combined with probabilistic prediction techniques have achieved success in various problems like image classification and text classification. For more complex multivariate prediction tasks, the relationships between labels play an important role in designing structur... | [] | null | 57 | null | null | [
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Invertible Residual Networks | https://proceedings.mlr.press/v97/behrmann19a.html | [
"Jens Behrmann",
"Will Grathwohl",
"Ricky T. Q. Chen",
"David Duvenaud",
"Joern-Henrik Jacobsen"
] | null | null | We show that standard ResNet architectures can be made invertible, allowing the same model to be used for classification, density estimation, and generation. Typically, enforcing invertibility requires partitioning dimensions or restricting network architectures. In contrast, our approach only requires adding a simple ... | [] | null | 58 | 1811.00995 | title_snapshot | [
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Greedy Layerwise Learning Can Scale To ImageNet | https://proceedings.mlr.press/v97/belilovsky19a.html | [
"Eugene Belilovsky",
"Michael Eickenberg",
"Edouard Oyallon"
] | null | null | Shallow supervised 1-hidden layer neural networks have a number of favorable properties that make them easier to interpret, analyze, and optimize than their deep counterparts, but lack their representational power. Here we use 1-hidden layer learning problems to sequentially build deep networks layer by layer, which ca... | [] | null | 59 | 1812.11446 | title_snapshot | [
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Overcoming Multi-model Forgetting | https://proceedings.mlr.press/v97/benyahia19a.html | [
"Yassine Benyahia",
"Kaicheng Yu",
"Kamil Bennani Smires",
"Martin Jaggi",
"Anthony C. Davison",
"Mathieu Salzmann",
"Claudiu Musat"
] | null | null | We identify a phenomenon, which we refer to as multi-model forgetting, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performance of previously-trained models degrades as one optimizes a subsequent one, due to the overwriting of shared parameters. To overcome this, w... | [] | null | 60 | 1902.08232 | title_snapshot | [
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Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning | https://proceedings.mlr.press/v97/benzing19a.html | [
"Frederik Benzing",
"Marcelo Matheus Gauy",
"Asier Mujika",
"Anders Martinsson",
"Angelika Steger"
] | null | null | One of the central goals of Recurrent Neural Networks (RNNs) is to learn long-term dependencies in sequential data. Nevertheless, the most popular training method, Truncated Backpropagation through Time (TBPTT), categorically forbids learning dependencies beyond the truncation horizon. In contrast, the online training ... | [] | null | 61 | 1902.03993 | title_snapshot | [
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Adversarially Learned Representations for Information Obfuscation and Inference | https://proceedings.mlr.press/v97/bertran19a.html | [
"Martin Bertran",
"Natalia Martinez",
"Afroditi Papadaki",
"Qiang Qiu",
"Miguel Rodrigues",
"Galen Reeves",
"Guillermo Sapiro"
] | null | null | Data collection and sharing are pervasive aspects of modern society. This process can either be voluntary, as in the case of a person taking a facial image to unlock his/her phone, or incidental, such as traffic cameras collecting videos on pedestrians. An undesirable side effect of these processes is that shared data ... | [] | null | 62 | null | null | [
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Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case | https://proceedings.mlr.press/v97/beygelzimer19a.html | [
"Alina Beygelzimer",
"David Pal",
"Balazs Szorenyi",
"Devanathan Thiruvenkatachari",
"Chen-Yu Wei",
"Chicheng Zhang"
] | null | null | We study the problem of efficient online multiclass linear classification with bandit feedback, where all examples belong to one of $K$ classes and lie in the $d$-dimensional Euclidean space. Previous works have left open the challenge of designing efficient algorithms with finite mistake bounds when the data is linear... | [] | null | 63 | 1902.02244 | title_snapshot | [
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Analyzing Federated Learning through an Adversarial Lens | https://proceedings.mlr.press/v97/bhagoji19a.html | [
"Arjun Nitin Bhagoji",
"Supriyo Chakraborty",
"Prateek Mittal",
"Seraphin Calo"
] | null | null | Federated learning distributes model training among a multitude of agents, who, guided by privacy concerns, perform training using their local data but share only model parameter updates, for iterative aggregation at the server to train an overall global model. In this work, we explore how the federated learning settin... | [] | null | 64 | 1811.12470 | title_snapshot | [
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Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference | https://proceedings.mlr.press/v97/bian19a.html | [
"Yatao Bian",
"Joachim Buhmann",
"Andreas Krause"
] | null | null | Mean field inference for discrete graphical models is generally a highly nonconvex problem, which also holds for the class of probabilistic log-submodular models. Existing optimization methods, e.g., coordinate ascent algorithms, typically only find local optima. In this work we propose provable mean filed methods for ... | [] | null | 65 | 1805.07482 | title_judge | [
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More Efficient Off-Policy Evaluation through Regularized Targeted Learning | https://proceedings.mlr.press/v97/bibaut19a.html | [
"Aurelien Bibaut",
"Ivana Malenica",
"Nikos Vlassis",
"Mark Van Der Laan"
] | null | null | We study the problem of off-policy evaluation (OPE) in Reinforcement Learning (RL), where the aim is to estimate the performance of a new policy given historical data that may have been generated by a different policy, or policies. In particular, we introduce a novel doubly-robust estimator for the OPE problem in RL, b... | [] | null | 66 | 1912.06292 | title_snapshot | [
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A Kernel Perspective for Regularizing Deep Neural Networks | https://proceedings.mlr.press/v97/bietti19a.html | [
"Alberto Bietti",
"Grégoire Mialon",
"Dexiong Chen",
"Julien Mairal"
] | null | null | We propose a new point of view for regularizing deep neural networks by using the norm of a reproducing kernel Hilbert space (RKHS). Even though this norm cannot be computed, it admits upper and lower approximations leading to various practical strategies. Specifically, this perspective (i) provides a common umbrella f... | [] | null | 67 | 1810.00363 | title_snapshot | [
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Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff | https://proceedings.mlr.press/v97/blau19a.html | [
"Yochai Blau",
"Tomer Michaeli"
] | null | null | Lossy compression algorithms are typically designed and analyzed through the lens of Shannon’s rate-distortion theory, where the goal is to achieve the lowest possible distortion (e.g., low MSE or high SSIM) at any given bit rate. However, in recent years, it has become increasingly accepted that "low distortion" is no... | [] | null | 68 | 1901.07821 | title_snapshot | [
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Correlated bandits or: How to minimize mean-squared error online | https://proceedings.mlr.press/v97/boda19a.html | [
"Vinay Praneeth Boda",
"Prashanth L.A."
] | null | null | While the objective in traditional multi-armed bandit problems is to find the arm with the highest mean, in many settings, finding an arm that best captures information about other arms is of interest. This objective, however, requires learning the underlying correlation structure and not just the means. Sensors placem... | [] | null | 69 | 1902.02953 | title_snapshot | [
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Adversarial Attacks on Node Embeddings via Graph Poisoning | https://proceedings.mlr.press/v97/bojchevski19a.html | [
"Aleksandar Bojchevski",
"Stephan Günnemann"
] | null | null | The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks. However, despite the proliferation of such methods, there is currently no study of their robustness to adversarial attacks. We provide the first adversari... | [] | null | 70 | 1809.01093 | title_snapshot | [
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Online Variance Reduction with Mixtures | https://proceedings.mlr.press/v97/borsos19a.html | [
"Zalán Borsos",
"Sebastian Curi",
"Kfir Yehuda Levy",
"Andreas Krause"
] | null | null | Adaptive importance sampling for stochastic optimization is a promising approach that offers improved convergence through variance reduction. In this work, we propose a new framework for variance reduction that enables the use of mixtures over predefined sampling distributions, which can naturally encode prior knowledg... | [] | null | 71 | 1903.12416 | title_snapshot | [
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Compositional Fairness Constraints for Graph Embeddings | https://proceedings.mlr.press/v97/bose19a.html | [
"Avishek Bose",
"William Hamilton"
] | null | null | Learning high-quality node embeddings is a key building block for machine learning models that operate on graph data, such as social networks and recommender systems. However, existing graph embedding techniques are unable to cope with fairness constraints, e.g., ensuring that the learned representations do not correla... | [] | null | 72 | 1905.10674 | title_snapshot | [
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Unreproducible Research is Reproducible | https://proceedings.mlr.press/v97/bouthillier19a.html | [
"Xavier Bouthillier",
"César Laurent",
"Pascal Vincent"
] | null | null | The apparent contradiction in the title is a wordplay on the different meanings attributed to the word reproducible across different scientific fields. What we imply is that unreproducible findings can be built upon reproducible methods. Without denying the importance of facilitating the reproduction of methods, we dee... | [] | null | 73 | null | null | [
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Blended Conditonal Gradients | https://proceedings.mlr.press/v97/braun19a.html | [
"Gábor Braun",
"Sebastian Pokutta",
"Dan Tu",
"Stephen Wright"
] | null | null | We present a blended conditional gradient approach for minimizing a smooth convex function over a polytope P, combining the Frank{–}Wolfe algorithm (also called conditional gradient) with gradient-based steps, different from away steps and pairwise steps, but still achieving linear convergence for strongly convex funct... | [] | null | 74 | null | null | [
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Coresets for Ordered Weighted Clustering | https://proceedings.mlr.press/v97/braverman19a.html | [
"Vladimir Braverman",
"Shaofeng H.-C. Jiang",
"Robert Krauthgamer",
"Xuan Wu"
] | null | null | We design coresets for Ordered k-Median, a generalization of classical clustering problems such as k-Median and k-Center. Its objective function is defined via the Ordered Weighted Averaging (OWA) paradigm of Yager (1988), where data points are weighted according to a predefined weight vector, but in order of their con... | [] | null | 75 | 1903.04351 | title_snapshot | [
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Target Tracking for Contextual Bandits: Application to Demand Side Management | https://proceedings.mlr.press/v97/bregere19a.html | [
"Margaux Brégère",
"Pierre Gaillard",
"Yannig Goude",
"Gilles Stoltz"
] | null | null | We propose a contextual-bandit approach for demand side management by offering price incentives. More precisely, a target mean consumption is set at each round and the mean consumption is modeled as a complex function of the distribution of prices sent and of some contextual variables such as the temperature, weather, ... | [] | null | 76 | 1901.09532 | title_snapshot | [
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Active Manifolds: A non-linear analogue to Active Subspaces | https://proceedings.mlr.press/v97/bridges19a.html | [
"Robert Bridges",
"Anthony Gruber",
"Christopher Felder",
"Miki Verma",
"Chelsey Hoff"
] | null | null | We present an approach to analyze $C^1(\mathbb{R}^m)$ functions that addresses limitations present in the Active Subspaces (AS) method of Constantine et al. (2014; 2015). Under appropriate hypotheses, our Active Manifolds (AM) method identifies a 1-D curve in the domain (the active manifold) on which nearly all values ... | [] | null | 77 | 1904.13386 | title_snapshot | [
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Conditioning by adaptive sampling for robust design | https://proceedings.mlr.press/v97/brookes19a.html | [
"David Brookes",
"Hahnbeom Park",
"Jennifer Listgarten"
] | null | null | We present a method for design problems wherein the goal is to maximize or specify the value of one or more properties of interest (e.g. maximizing the fluorescence of a protein). We assume access to black box, stochastic “oracle" predictive functions, each of which maps from design space to a distribution over propert... | [] | null | 78 | 1901.10060 | title_snapshot | [
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Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations | https://proceedings.mlr.press/v97/brown19a.html | [
"Daniel Brown",
"Wonjoon Goo",
"Prabhat Nagarajan",
"Scott Niekum"
] | null | null | A critical flaw of existing inverse reinforcement learning (IRL) methods is their inability to significantly outperform the demonstrator. This is because IRL typically seeks a reward function that makes the demonstrator appear near-optimal, rather than inferring the underlying intentions of the demonstrator that may ha... | [] | null | 79 | 1904.06387 | title_snapshot | [
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Deep Counterfactual Regret Minimization | https://proceedings.mlr.press/v97/brown19b.html | [
"Noam Brown",
"Adam Lerer",
"Sam Gross",
"Tuomas Sandholm"
] | null | null | Counterfactual Regret Minimization (CFR) is the leading algorithm for solving large imperfect-information games. It converges to an equilibrium by iteratively traversing the game tree. In order to deal with extremely large games, abstraction is typically applied before running CFR. The abstracted game is solved with ta... | [] | null | 80 | 1811.00164 | title_snapshot | [
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Understanding the Origins of Bias in Word Embeddings | https://proceedings.mlr.press/v97/brunet19a.html | [
"Marc-Etienne Brunet",
"Colleen Alkalay-Houlihan",
"Ashton Anderson",
"Richard Zemel"
] | null | null | Popular word embedding algorithms exhibit stereotypical biases, such as gender bias. The widespread use of these algorithms in machine learning systems can amplify stereotypes in important contexts. Although some methods have been developed to mitigate this problem, how word embedding biases arise during training is po... | [] | null | 81 | 1810.03611 | title_snapshot | [
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Low Latency Privacy Preserving Inference | https://proceedings.mlr.press/v97/brutzkus19a.html | [
"Alon Brutzkus",
"Ran Gilad-Bachrach",
"Oren Elisha"
] | null | null | When applying machine learning to sensitive data, one has to find a balance between accuracy, information security, and computational-complexity. Recent studies combined Homomorphic Encryption with neural networks to make inferences while protecting against information leakage. However, these methods are limited by the... | [] | null | 82 | 1812.10659 | title_snapshot | [
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Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem | https://proceedings.mlr.press/v97/brutzkus19b.html | [
"Alon Brutzkus",
"Amir Globerson"
] | null | null | Empirical evidence suggests that neural networks with ReLU activations generalize better with over-parameterization. However, there is currently no theoretical analysis that explains this observation. In this work, we provide theoretical and empirical evidence that, in certain cases, overparameterized convolutional net... | [] | null | 83 | 1810.03037 | title_snapshot | [
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Adversarial examples from computational constraints | https://proceedings.mlr.press/v97/bubeck19a.html | [
"Sebastien Bubeck",
"Yin Tat Lee",
"Eric Price",
"Ilya Razenshteyn"
] | null | null | Why are classifiers in high dimension vulnerable to “adversarial” perturbations? We show that it is likely not due to information theoretic limitations, but rather it could be due to computational constraints. First we prove that, for a broad set of classification tasks, the mere existence of a robust classifier implie... | [] | null | 84 | 1805.10204 | title_snapshot | [
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Self-similar Epochs: Value in arrangement | https://proceedings.mlr.press/v97/buchnik19a.html | [
"Eliav Buchnik",
"Edith Cohen",
"Avinatan Hasidim",
"Yossi Matias"
] | null | null | Optimization of machine learning models is commonly performed through stochastic gradient updates on randomly ordered training examples. This practice means that each fraction of an epoch comprises an independent random sample of the training data that may not preserve informative structure present in the full data. We... | [] | null | 85 | 1803.05389 | title_snapshot | [
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Learning Generative Models across Incomparable Spaces | https://proceedings.mlr.press/v97/bunne19a.html | [
"Charlotte Bunne",
"David Alvarez-Melis",
"Andreas Krause",
"Stefanie Jegelka"
] | null | null | Generative Adversarial Networks have shown remarkable success in learning a distribution that faithfully recovers a reference distribution in its entirety. However, in some cases, we may want to only learn some aspects (e.g., cluster or manifold structure), while modifying others (e.g., style, orientation or dimension)... | [] | null | 86 | 1905.05461 | title_snapshot | [
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Rates of Convergence for Sparse Variational Gaussian Process Regression | https://proceedings.mlr.press/v97/burt19a.html | [
"David Burt",
"Carl Edward Rasmussen",
"Mark Van Der Wilk"
] | null | null | Excellent variational approximations to Gaussian process posteriors have been developed which avoid the $\mathcal{O}\left(N^3\right)$ scaling with dataset size $N$. They reduce the computational cost to $\mathcal{O}\left(NM^2\right)$, with $M\ll N$ the number ofinducing variables, which summarise the process. While the... | [] | null | 87 | 1903.03571 | title_snapshot | [
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What is the Effect of Importance Weighting in Deep Learning? | https://proceedings.mlr.press/v97/byrd19a.html | [
"Jonathon Byrd",
"Zachary Lipton"
] | null | null | Importance-weighted risk minimization is a key ingredient in many machine learning algorithms for causal inference, domain adaptation, class imbalance, and off-policy reinforcement learning. While the effect of importance weighting is well-characterized for low-capacity misspecified models, little is known about how it... | [] | null | 88 | 1812.03372 | title_snapshot | [
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A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent | https://proceedings.mlr.press/v97/cai19a.html | [
"Yongqiang Cai",
"Qianxiao Li",
"Zuowei Shen"
] | null | null | Despite its empirical success and recent theoretical progress, there generally lacks a quantitative analysis of the effect of batch normalization (BN) on the convergence and stability of gradient descent. In this paper, we provide such an analysis on the simple problem of ordinary least squares (OLS), where the precise... | [] | null | 89 | 1810.00122 | title_snapshot | [
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Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances | https://proceedings.mlr.press/v97/can19a.html | [
"Bugra Can",
"Mert Gurbuzbalaban",
"Lingjiong Zhu"
] | null | null | Momentum methods such as Polyak’s heavy ball (HB) method, Nesterov’s accelerated gradient (AG) as well as accelerated projected gradient (APG) method have been commonly used in machine learning practice, but their performance is quite sensitive to noise in the gradients. We study these methods under a first-order stoch... | [] | null | 90 | 1901.07445 | title_snapshot | [
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Active Embedding Search via Noisy Paired Comparisons | https://proceedings.mlr.press/v97/canal19a.html | [
"Gregory Canal",
"Andy Massimino",
"Mark Davenport",
"Christopher Rozell"
] | null | null | Suppose that we wish to estimate a user’s preference vector $w$ from paired comparisons of the form “does user $w$ prefer item $p$ or item $q$?,” where both the user and items are embedded in a low-dimensional Euclidean space with distances that reflect user and item similarities. Such observations arise in numerous se... | [] | null | 91 | 1905.04363 | title_snapshot | [
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Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem | https://proceedings.mlr.press/v97/cao19a.html | [
"Junyu Cao",
"Wei Sun"
] | null | null | Motivated by the phenomenon that companies introduce new products to keep abreast with customers’ rapidly changing tastes, we consider a novel online learning setting where a profit-maximizing seller needs to learn customers’ preferences through offering recommendations, which may contain existing products and new prod... | [] | null | 92 | 1904.12445 | title_snapshot | [
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Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games | https://proceedings.mlr.press/v97/cardoso19a.html | [
"Adrian Rivera Cardoso",
"Jacob Abernethy",
"He Wang",
"Huan Xu"
] | null | null | We study the problem of repeated play in a zero-sum game in which the payoff matrix may change, in a possibly adversarial fashion, on each round; we call these Online Matrix Games. Finding the Nash Equilibrium (NE) of a two player zero-sum game is core to many problems in statistics, optimization, and economics, and fo... | [] | null | 93 | 1907.07723 | title_judge | [
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Automated Model Selection with Bayesian Quadrature | https://proceedings.mlr.press/v97/chai19a.html | [
"Henry Chai",
"Jean-Francois Ton",
"Michael A. Osborne",
"Roman Garnett"
] | null | null | We present a novel technique for tailoring Bayesian quadrature (BQ) to model selection. The state-of-the-art for comparing the evidence of multiple models relies on Monte Carlo methods, which converge slowly and are unreliable for computationally expensive models. Although previous research has shown that BQ offers sam... | [] | null | 94 | 1902.09724 | title_snapshot | [
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Learning Action Representations for Reinforcement Learning | https://proceedings.mlr.press/v97/chandak19a.html | [
"Yash Chandak",
"Georgios Theocharous",
"James Kostas",
"Scott Jordan",
"Philip Thomas"
] | null | null | Most model-free reinforcement learning methods leverage state representations (embeddings) for generalization, but either ignore structure in the space of actions or assume the structure is provided a priori. We show how a policy can be decomposed into a component that acts in a low-dimensional space of action represen... | [] | null | 95 | 1902.00183 | title_snapshot | [
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Dynamic Measurement Scheduling for Event Forecasting using Deep RL | https://proceedings.mlr.press/v97/chang19a.html | [
"Chun-Hao Chang",
"Mingjie Mai",
"Anna Goldenberg"
] | null | null | Imagine a patient in critical condition. What and when should be measured to forecast detrimental events, especially under the budget constraints? We answer this question by deep reinforcement learning (RL) that jointly minimizes the measurement cost and maximizes predictive gain, by scheduling strategically-timed meas... | [] | null | 96 | 1901.09699 | title_snapshot | [
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On Symmetric Losses for Learning from Corrupted Labels | https://proceedings.mlr.press/v97/charoenphakdee19a.html | [
"Nontawat Charoenphakdee",
"Jongyeong Lee",
"Masashi Sugiyama"
] | null | null | This paper aims to provide a better understanding of a symmetric loss. First, we emphasize that using a symmetric loss is advantageous in the balanced error rate (BER) minimization and area under the receiver operating characteristic curve (AUC) maximization from corrupted labels. Second, we prove general theoretical p... | [] | null | 97 | 1901.09314 | title_snapshot | [
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Online learning with kernel losses | https://proceedings.mlr.press/v97/chatterji19a.html | [
"Niladri Chatterji",
"Aldo Pacchiano",
"Peter Bartlett"
] | null | null | We present a generalization of the adversarial linear bandits framework, where the underlying losses are kernel functions (with an associated reproducing kernel Hilbert space) rather than linear functions. We study a version of the exponential weights algorithm and bound its regret in this setting. Under conditions on ... | [] | null | 98 | 1802.09732 | title_snapshot | [
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Neural Network Attributions: A Causal Perspective | https://proceedings.mlr.press/v97/chattopadhyay19a.html | [
"Aditya Chattopadhyay",
"Piyushi Manupriya",
"Anirban Sarkar",
"Vineeth N Balasubramanian"
] | null | null | We propose a new attribution method for neural networks developed using first principles of causality (to the best of our knowledge, the first such). The neural network architecture is viewed as a Structural Causal Model, and a methodology to compute the causal effect of each feature on the output is presented. With reas... | [] | null | 99 | 1902.02302 | title_snapshot | [
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PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits | https://proceedings.mlr.press/v97/chaudhuri19a.html | [
"Arghya Roy Chaudhuri",
"Shivaram Kalyanakrishnan"
] | null | null | We consider the problem of identifying any k out of the best m arms in an n-armed stochastic multi-armed bandit; framed in the PAC setting, this particular problem generalises both the problem of “best subset selection” (Kalyanakrishnan & Stone, 2010) and that of selecting “one out of the best m” arms (Roy Chaudhuri & ... | [] | null | 100 | 1901.08386 | title_snapshot | [
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... |
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