
NoRegret Learning in Unknown Games with Correlated Payoffs
We consider the problem of learning to play a repeated multiagent game ...
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Adaptive and Safe Bayesian Optimization in High Dimensions via OneDimensional Subspaces
Bayesian optimization is known to be difficult to scale to high dimensio...
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Noregret Bayesian Optimization with Unknown Hyperparameters
Bayesian optimization (BO) based on Gaussian process models is a powerfu...
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Adaptive Sampling for Stochastic RiskAverse Learning
We consider the problem of training machine learning models in a riskav...
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Online Variance Reduction with Mixtures
Adaptive importance sampling for stochastic optimization is a promising ...
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Safe Exploration for Interactive Machine Learning
In Interactive Machine Learning (IML), we iteratively make decisions and...
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InformationDirected Exploration for Deep Reinforcement Learning
Efficient exploration remains a major challenge for reinforcement learni...
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A Humanintheloop Framework to Construct Contextdependent Mathematical Formulations of Fairness
Despite the recent surge of interest in designing and guaranteeing mathe...
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AReS and MaRS  Adversarial and MMDMinimizing Regression for SDEs
Stochastic differential equations are an important modeling class in man...
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MixedVariable Bayesian Optimization
The optimization of expensive to evaluate, blackbox, mixedvariable fun...
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ODIN: ODEInformed Regression for Parameter and State Inference in TimeContinuous Dynamical Systems
Parameter inference in ordinary differential equations is an important p...
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Robust Modelfree Reinforcement Learning with Multiobjective Bayesian Optimization
In reinforcement learning (RL), an autonomous agent learns to perform co...
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Adaptive Sequence Submodularity
In many machine learning applications, one needs to interactively select...
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Evaluating GANs via Duality
Generative Adversarial Networks (GANs) have shown great results in accur...
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Stochastic Bandits with Context Distributions
We introduce a novel stochastic contextual bandit model, where at each s...
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Distributionally Robust Bayesian Optimization
Robustness to distributional shift is one of the key challenges of conte...
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A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity
Equality of opportunity (EOP) is an extensively studied conception of fa...
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Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuning
We tune one of the most common heating, ventilation, and air conditionin...
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Noise Regularization for Conditional Density Estimation
Modelling statistical relationships beyond the conditional mean is cruci...
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PACOH: BayesOptimal MetaLearning with PACGuarantees
Metalearning can successfully acquire useful inductive biases from data...
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Unsupervised Imitation Learning
We introduce a novel method to learn a policy from unsupervised demonstr...
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Structured Variational Inference in Unstable Gaussian Process State Space Models
Gaussian processes are expressive, nonparametric statistical models tha...
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Learning Generative Models across Incomparable Spaces
Generative Adversarial Networks have shown remarkable success in learnin...
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Learning User Preferences to Incentivize Exploration in the Sharing Economy
We study platforms in the sharing economy and discuss the need for incen...
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Nonmonotone Continuous DRsubmodular Maximization: Structure and Algorithms
DRsubmodular continuous functions are important objectives with wide re...
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Learning Implicit Generative Models Using Differentiable Graph Tests
Recently, there has been a growing interest in the problem of learning r...
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An Online Learning Approach to Generative Adversarial Networks
We consider the problem of training generative models with a Generative ...
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Safe Modelbased Reinforcement Learning with Stability Guarantees
Reinforcement learning is a powerful paradigm for learning optimal polic...
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Training Mixture Models at Scale via Coresets
How can we train a statistical mixture model on a massive data set? In t...
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Practical Coreset Constructions for Machine Learning
We investigate coresets  succinct, small summaries of large data sets ...
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Efficient Online Learning for Optimizing Value of Information: Theory and Application to Interactive Troubleshooting
We consider the optimal value of information (VoI) problem, where the go...
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Uniform Deviation Bounds for Unbounded Loss Functions like kMeans
Uniform deviation bounds limit the difference between a model's expected...
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Scalable and Distributed Clustering via Lightweight Coresets
Coresets are compact representations of data sets such that models train...
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Coordinated Online Learning With Applications to Learning User Preferences
We study an online multitask learning setting, in which instances of re...
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Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and LevelSet Estimation
We present a new algorithm, truncated variance reduction (TruVaR), that ...
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Discovering Valuable Items from Massive Data
Suppose there is a large collection of items, each with an associated co...
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Building Hierarchies of Concepts via Crowdsourcing
Hierarchies of concepts are useful in many applications from navigation ...
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Information Gathering in Networks via Active Exploration
How should we gather information in a network, where each node's visibil...
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Safe Exploration in Finite Markov Decision Processes with Gaussian Processes
In classical reinforcement learning, when exploring an environment, agen...
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Horizontally Scalable Submodular Maximization
A variety of largescale machine learning problems can be cast as instan...
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Actively Learning Hemimetrics with Applications to Eliciting User Preferences
Motivated by an application of eliciting users' preferences, we investig...
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Distributed Submodular Maximization
Many largescale machine learning problemsclustering, nonparametric l...
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Algorithms for Learning Sparse Additive Models with Interactions in High Dimensions
A function f: R^d →R is a Sparse Additive Model (SPAM), if it is of the ...
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Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning
Faced with massive data, is it possible to trade off (statistical) risk,...
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Lineartime Outlier Detection via Sensitivity
Outliers are ubiquitous in modern data sets. Distancebased techniques a...
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Learning Sparse Additive Models with Interactions in High Dimensions
A function f: R^d →R is referred to as a Sparse Additive Model (SPAM), i...
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Better safe than sorry: Risky function exploitation through safe optimization
Explorationexploitation of functions, that is learning and optimizing a...
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Near Optimal Bayesian Active Learning for Decision Making
How should we gather information to make effective decisions? We address...
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A UtilityTheoretic Approach to Privacy in Online Services
Online offerings such as web search, news portals, and ecommerce applic...
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Optimal Value of Information in Graphical Models
Many realworld decision making tasks require us to choose among several...
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