
Logistic QLearning
We propose a new reinforcement learning algorithm derived from a regular...
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Online Active Model Selection for Pretrained Classifiers
Given k pretrained classifiers and a stream of unlabeled data examples,...
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Semisupervised Batch Active Learning via Bilevel Optimization
Active learning is an effective technique for reducing the labeling cost...
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RaoBlackwellizing the StraightThrough GumbelSoftmax Gradient Estimator
Gradient estimation in models with discrete latent variables is a challe...
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Learning Set Functions that are Sparse in NonOrthogonal Fourier Bases
Many applications of machine learning on discrete domains, such as learn...
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Learning to Play Sequential Games versus Unknown Opponents
We consider a repeated sequential game between a learner, who plays firs...
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Stochastic Linear Bandits Robust to Adversarial Attacks
We consider a stochastic linear bandit problem in which the rewards are ...
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Continuous Submodular Function Maximization
Continuous submodular functions are a category of generally nonconvex/n...
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Learning Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory
We present the first approach for learning – from a single trajectory – ...
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Efficient ModelBased Reinforcement Learning through Optimistic Policy Search and Planning
Modelbased reinforcement learning algorithms with probabilistic dynamic...
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Gradient Estimation with Stochastic Softmax Tricks
The GumbelMax trick is the basis of many relaxed gradient estimators. T...
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Learning Graph Models for TemplateFree Retrosynthesis
Retrosynthesis prediction is a fundamental problem in organic synthesis,...
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Coresets via Bilevel Optimization for Continual Learning and Streaming
Coresets are small data summaries that are sufficient for model training...
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From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models
Submodular functions have been studied extensively in machine learning a...
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Hierarchical Image Classification using Entailment Cone Embeddings
Image classification has been studied extensively, but there has been li...
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SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives
Gaussian processes are an important regression tool with excellent analy...
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CorruptionTolerant Gaussian Process Bandit Optimization
We consider the problem of optimizing an unknown (typically nonconvex) ...
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Mixed Strategies for Robust Optimization of Unknown Objectives
We consider robust optimization problems, where the goal is to optimize ...
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Information Directed Sampling for Linear Partial Monitoring
Partial monitoring is a rich framework for sequential decision making un...
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Distributionally Robust Bayesian Optimization
Robustness to distributional shift is one of the key challenges of conte...
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PACOH: BayesOptimal MetaLearning with PACGuarantees
Metalearning can successfully acquire useful inductive biases from data...
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Log Barriers for Safe Nonconvex Blackbox Optimization
We address the problem of minimizing a smooth function f^0(x) over a com...
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Safe nonsmooth blackbox optimization with application to policy search
For safetycritical blackbox optimization tasks, observations of the co...
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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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Safe Exploration for Interactive Machine Learning
In Interactive Machine Learning (IML), we iteratively make decisions and...
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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 Sampling for Stochastic RiskAverse Learning
We consider the problem of training machine learning models in a riskav...
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Convergence Analysis of the Randomized Newton Method with Determinantal Sampling
We analyze the convergence rate of the Randomized Newton Method (RNM) in...
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NoRegret Learning in Unknown Games with Correlated Payoffs
We consider the problem of learning to play a repeated multiagent game ...
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Noise Regularization for Conditional Density Estimation
Modelling statistical relationships beyond the conditional mean is cruci...
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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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MixedVariable Bayesian Optimization
The optimization of expensive to evaluate, blackbox, mixedvariable fun...
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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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Stochastic Bandits with Context Distributions
We introduce a novel stochastic contextual bandit model, where at each s...
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Learning Generative Models across Incomparable Spaces
Generative Adversarial Networks have shown remarkable success in learnin...
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Online Variance Reduction with Mixtures
Adaptive importance sampling for stochastic optimization is a promising ...
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Bounding Inefficiency of Equilibria in Continuous Actions Games using Submodularity and Curvature
Games with continuous strategy sets arise in several machine learning pr...
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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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MultiPlayer Bandits: The Adversarial Case
We consider a setting where multiple players sequentially choose among a...
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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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Adaptive Sequence Submodularity
In many machine learning applications, one needs to interactively select...
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Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning
Fairness for Machine Learning has received considerable attention, recen...
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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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InformationDirected Exploration for Deep Reinforcement Learning
Efficient exploration remains a major challenge for reinforcement learni...
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Evaluating GANs via Duality
Generative Adversarial Networks (GANs) have shown great results in accur...
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Learning to Compensate Photovoltaic Power Fluctuations from Images of the Sky by Imitating an Optimal Policy
The energy output of photovoltaic (PV) power plants depends on the envir...
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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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The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamic Systems
Learning algorithms have shown considerable prowess in simulation by all...
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Discrete Sampling using Semigradientbased Product Mixtures
We consider the problem of inference in discrete probabilistic models, t...
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