
PopSkipJump: DecisionBased Attack for Probabilistic Classifiers
Most current classifiers are vulnerable to adversarial examples, small i...
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JKOnet: Proximal Optimal Transport Modeling of Population Dynamics
Consider a heterogeneous population of points evolving with time. While ...
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Robust Generalization despite Distribution Shift via Minimum Discriminating Information
Training models that perform well under distribution shifts is a central...
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MetaLearning Reliable Priors in the Function Space
MetaLearning promises to enable more dataefficient inference by harnes...
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EnergyBased Learning for Cooperative Games, with Applications to Feature/Data/Model Valuations
Valuation problems, such as attributionbased feature interpretation, da...
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Addressing the Longterm Impact of ML Decisions via Policy Regret
Machine Learning (ML) increasingly informs the allocation of opportuniti...
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CherryPicking Gradients: Learning LowRank Embeddings of Visual Data via Differentiable CrossApproximation
We propose an endtoend trainable framework that processes largescale ...
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NearOptimal MultiPerturbation Experimental Design for Causal Structure Learning
Causal structure learning is a key problem in many domains. Causal struc...
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DiBS: Differentiable Bayesian Structure Learning
Bayesian structure learning allows inferring Bayesian network structure ...
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BiasRobust Bayesian Optimization via Dueling Bandit
We consider Bayesian optimization in settings where observations can be ...
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Regret Bounds for GaussianProcess Optimization in Large Domains
The goal of this paper is to characterize GaussianProcess optimization ...
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Overfitting in Bayesian Optimization: an empirical study and earlystopping solution
Bayesian Optimization (BO) is a successful methodology to tune the hyper...
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Combining Pessimism with Optimism for Robust and Efficient ModelBased Deep Reinforcement Learning
In realworld tasks, reinforcement learning (RL) agents frequently encou...
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Information Directed Reward Learning for Reinforcement Learning
For many reinforcement learning (RL) applications, specifying a reward i...
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RiskAverse Offline Reinforcement Learning
Training Reinforcement Learning (RL) agents in highstakes applications ...
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Efficient Pure Exploration for Combinatorial Bandits with SemiBandit Feedback
Combinatorial bandits with semibandit feedback generalize multiarmed b...
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Safe and Efficient Modelfree Adaptive Control via Bayesian Optimization
Adaptive control approaches yield highperformance controllers when a pr...
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IncentiveCompatible Forecasting Competitions
We initiate the study of incentivecompatible forecasting competitions i...
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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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