ICML
Collection
Accepted papers for ICML (International Conference on Machine Learning), one dataset per year. • 13 items • Updated
title string | paper_url string | authors list | type string | primary_area string | abstract large_string | keywords list | TL;DR large_string | submission_number int64 | arxiv_id string | arxiv_id_source string | embedding list |
|---|---|---|---|---|---|---|---|---|---|---|---|
No Oops, You Won’t Do It Again: Mechanisms for Self-correction in Crowdsourcing | https://proceedings.mlr.press/v48/shaha16.html | [
"Nihar Shah",
"Dengyong Zhou"
] | null | null | Crowdsourcing is a very popular means of obtaining the large amounts of labeled data that modern machine learning methods require. Although cheap and fast to obtain, crowdsourced labels suffer from significant amounts of error, thereby degrading the performance of downstream machine learning tasks. With the goal of imp... | [] | null | 1 | null | null | [
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Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues | https://proceedings.mlr.press/v48/shahb16.html | [
"Nihar Shah",
"Sivaraman Balakrishnan",
"Aditya Guntuboyina",
"Martin Wainwright"
] | null | null | There are various parametric models for analyzing pairwise comparison data, including the Bradley-Terry-Luce (BTL) and Thurstone models, but their reliance on strong parametric assumptions is limiting. In this work, we study a flexible model for pairwise comparisons, under which the probabilities of outcomes are requir... | [] | null | 2 | 1510.05610 | title_snapshot | [
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Uprooting and Rerooting Graphical Models | https://proceedings.mlr.press/v48/weller16.html | [
"Adrian Weller"
] | null | null | We show how any binary pairwise model may be “uprooted” to a fully symmetric model, wherein original singleton potentials are transformed to potentials on edges to an added variable, and then “rerooted” to a new model on the original number of variables. The new model is essentially equivalent to the original model, wi... | [] | null | 3 | null | null | [
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A Deep Learning Approach to Unsupervised Ensemble Learning | https://proceedings.mlr.press/v48/shaham16.html | [
"Uri Shaham",
"Xiuyuan Cheng",
"Omer Dror",
"Ariel Jaffe",
"Boaz Nadler",
"Joseph Chang",
"Yuval Kluger"
] | null | null | We show how deep learning methods can be applied in the context of crowdsourcing and unsupervised ensemble learning. First, we prove that the popular model of Dawid and Skene, which assumes that all classifiers are conditionally independent, is \em equivalent to a Restricted Boltzmann Machine (RBM) with a single hidden... | [] | null | 4 | 1602.02285 | title_snapshot | [
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Revisiting Semi-Supervised Learning with Graph Embeddings | https://proceedings.mlr.press/v48/yanga16.html | [
"Zhilin Yang",
"William Cohen",
"Ruslan Salakhudinov"
] | null | null | We present a semi-supervised learning framework based on graph embeddings. Given a graph between instances, we train an embedding for each instance to jointly predict the class label and the neighborhood context in the graph. We develop both transductive and inductive variants of our method. In the transductive variant... | [] | null | 5 | 1603.08861 | title_snapshot | [
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Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization | https://proceedings.mlr.press/v48/finn16.html | [
"Chelsea Finn",
"Sergey Levine",
"Pieter Abbeel"
] | null | null | Reinforcement learning can acquire complex behaviors from high-level specifications. However, defining a cost function that can be optimized effectively and encodes the correct task is challenging in practice. We explore how inverse optimal control (IOC) can be used to learn behaviors from demonstrations, with applicat... | [] | null | 6 | 1603.00448 | title_snapshot | [
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Diversity-Promoting Bayesian Learning of Latent Variable Models | https://proceedings.mlr.press/v48/xiea16.html | [
"Pengtao Xie",
"Jun Zhu",
"Eric Xing"
] | null | null | In learning latent variable models (LVMs), it is important to effectively capture infrequent patterns and shrink model size without sacrificing modeling power. Various studies have been done to “diversify” a LVM, which aim to learn a diverse set of latent components in LVMs. Most existing studies fall into a frequentis... | [] | null | 7 | 1711.08770 | title_snapshot | [
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Additive Approximations in High Dimensional Nonparametric Regression via the SALSA | https://proceedings.mlr.press/v48/kandasamy16.html | [
"Kirthevasan Kandasamy",
"Yaoliang Yu"
] | null | null | High dimensional nonparametric regression is an inherently difficult problem with known lower bounds depending exponentially in dimension. A popular strategy to alleviate this curse of dimensionality has been to use additive models of \emphfirst order, which model the regression function as a sum of independent functio... | [] | null | 8 | 1602.00287 | title_snapshot | [
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Hawkes Processes with Stochastic Excitations | https://proceedings.mlr.press/v48/leea16.html | [
"Young Lee",
"Kar Wai Lim",
"Cheng Soon Ong"
] | null | null | We propose an extension to Hawkes processes by treating the levels of self-excitation as a stochastic differential equation. Our new point process allows better approximation in application domains where events and intensities accelerate each other with correlated levels of contagion. We generalize a recent algorithm f... | [] | null | 9 | 1609.06831 | title_snapshot | [
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Data-driven Rank Breaking for Efficient Rank Aggregation | https://proceedings.mlr.press/v48/khetan16.html | [
"Ashish Khetan",
"Sewoong Oh"
] | null | null | Rank aggregation systems collect ordinal preferences from individuals to produce a global ranking that represents the social preference. To reduce the computational complexity of learning the global ranking, a common practice is to use rank-breaking. Individuals’ preferences are broken into pairwise comparisons and the... | [] | null | 10 | 1601.05495 | title_snapshot | [
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Dropout distillation | https://proceedings.mlr.press/v48/bulo16.html | [
"Samuel Rota Bulò",
"Lorenzo Porzi",
"Peter Kontschieder"
] | null | null | Dropout is a popular stochastic regularization technique for deep neural networks that works by randomly dropping (i.e. zeroing) units from the network during training. This randomization process allows to implicitly train an ensemble of exponentially many networks sharing the same parametrization, which should be aver... | [] | null | 11 | null | null | [
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