ICML
Collection
Accepted papers for ICML (International Conference on Machine Learning), one dataset per year. • 13 items • Updated
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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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