ICML
Collection
Accepted papers for ICML (International Conference on Machine Learning), one dataset per year. • 13 items • Updated
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Uncovering Causality from Multivariate Hawkes Integrated Cumulants | https://proceedings.mlr.press/v70/achab17a.html | [
"Massil Achab",
"Emmanuel Bacry",
"Stéphane Gaı̈ffas",
"Iacopo Mastromatteo",
"Jean-François Muzy"
] | null | null | We design a new nonparametric method that allows one to estimate the matrix of integrated kernels of a multivariate Hawkes process. This matrix not only encodes the mutual influences of each node of the process, but also disentangles the causality relationships between them. Our approach is the first that leads to an e... | [] | null | 1 | 1607.06333 | title_snapshot | [
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A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions | https://proceedings.mlr.press/v70/acharya17a.html | [
"Jayadev Acharya",
"Hirakendu Das",
"Alon Orlitsky",
"Ananda Theertha Suresh"
] | null | null | Symmetric distribution properties such as support size, support coverage, entropy, and proximity to uniformity, arise in many applications. Recently, researchers applied different estimators and analysis tools to derive asymptotically sample-optimal approximations for each of these properties. We show that a single, si... | [] | null | 2 | null | null | [
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Constrained Policy Optimization | https://proceedings.mlr.press/v70/achiam17a.html | [
"Joshua Achiam",
"David Held",
"Aviv Tamar",
"Pieter Abbeel"
] | null | null | For many applications of reinforcement learning it can be more convenient to specify both a reward function and constraints, rather than trying to design behavior through the reward function. For example, systems that physically interact with or around humans should satisfy safety constraints. Recent advances in policy... | [] | null | 3 | 1705.10528 | title_snapshot | [
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The Price of Differential Privacy for Online Learning | https://proceedings.mlr.press/v70/agarwal17a.html | [
"Naman Agarwal",
"Karan Singh"
] | null | null | We design differentially private algorithms for the problem of online linear optimization in the full information and bandit settings with optimal $O(T^{0.5})$ regret bounds. In the full-information setting, our results demonstrate that $\epsilon$-differential privacy may be ensured for free – in particular, the regret... | [] | null | 4 | 1701.07953 | title_snapshot | [
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Local Bayesian Optimization of Motor Skills | https://proceedings.mlr.press/v70/akrour17a.html | [
"Riad Akrour",
"Dmitry Sorokin",
"Jan Peters",
"Gerhard Neumann"
] | null | null | Bayesian optimization is renowned for its sample efficiency but its application to higher dimensional tasks is impeded by its focus on global optimization. To scale to higher dimensional problems, we leverage the sample efficiency of Bayesian optimization in a local context. The optimization of the acquisition function... | [] | null | 5 | null | null | [
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Connected Subgraph Detection with Mirror Descent on SDPs | https://proceedings.mlr.press/v70/aksoylar17a.html | [
"Cem Aksoylar",
"Lorenzo Orecchia",
"Venkatesh Saligrama"
] | null | null | We propose a novel, computationally efficient mirror-descent based optimization framework for subgraph detection in graph-structured data. Our aim is to discover anomalous patterns present in a connected subgraph of a given graph. This problem arises in many applications such as detection of network intrusions, communi... | [] | null | 6 | null | null | [
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Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis | https://proceedings.mlr.press/v70/alaa17a.html | [
"Ahmed M. Alaa",
"Scott Hu",
"Mihaela Schaar"
] | null | null | Critically ill patients in regular wards are vulnerable to unanticipated adverse events which require prompt transfer to the intensive care unit (ICU). To allow for accurate prognosis of deteriorating patients, we develop a novel continuous-time probabilistic model for a monitored patient’s temporal sequence of physiol... | [] | null | 7 | 1705.05267 | title_snapshot | [
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A Semismooth Newton Method for Fast, Generic Convex Programming | https://proceedings.mlr.press/v70/ali17a.html | [
"Alnur Ali",
"Eric Wong",
"J. Zico Kolter"
] | null | null | We introduce Newton-ADMM, a method for fast conic optimization. The basic idea is to view the residuals of consecutive iterates generated by the alternating direction method of multipliers (ADMM) as a set of fixed point equations, and then use a nonsmooth Newton method to find a solution; we apply the basic idea to the... | [] | null | 8 | 1705.00772 | title_snapshot | [
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Learning Continuous Semantic Representations of Symbolic Expressions | https://proceedings.mlr.press/v70/allamanis17a.html | [
"Miltiadis Allamanis",
"Pankajan Chanthirasegaran",
"Pushmeet Kohli",
"Charles Sutton"
] | null | null | Combining abstract, symbolic reasoning with continuous neural reasoning is a grand challenge of representation learning. As a step in this direction, we propose a new architecture, called neural equivalence network, for the problem of learning continuous semantic representations of algebraic and logical expressions. Th... | [] | null | 9 | 1611.01423 | title_snapshot | [
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Natasha: Faster Non-Convex Stochastic Optimization via Strongly Non-Convex Parameter | https://proceedings.mlr.press/v70/allen-zhu17a.html | [
"Zeyuan Allen-Zhu"
] | null | null | Given a non-convex function $f(x)$ that is an average of $n$ smooth functions, we design stochastic first-order methods to find its approximate stationary points. The performance of our new methods depend on the smallest (negative) eigenvalue $-\sigma$ of the Hessian. This parameter $\sigma$ captures how strongly non-c... | [] | null | 10 | 1702.00763 | title_snapshot | [
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Doubly Accelerated Methods for Faster CCA and Generalized Eigendecomposition | https://proceedings.mlr.press/v70/allen-zhu17b.html | [
"Zeyuan Allen-Zhu",
"Yuanzhi Li"
] | null | null | We study k-GenEV, the problem of finding the top k generalized eigenvectors, and k-CCA, the problem of finding the top k vectors in canonical-correlation analysis. We propose algorithms LazyEV and LazyCCA to solve the two problems with running times linearly dependent on the input size and on k. Furthermore, our algori... | [] | null | 11 | 1607.06017 | title_snapshot | [
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