ICML
Collection
Accepted papers for ICML (International Conference on Machine Learning), one dataset per year. • 13 items • Updated
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values | abstract large_stringlengths 233 1.97k | keywords listlengths 0 0 | TL;DR large_stringclasses 0
values | submission_number int64 1 621 | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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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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