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Found 19 papers, 12 papers with code
Evaluating the Effectiveness of Index-Based Treatment Allocation
When resources are scarce, an allocation policy is needed to decide who receives a resource.
A Bayesian Approach to Online Learning for Contextual Restless Bandits with Applications to Public Health
Restless multi-armed bandits (RMABs) are used to model sequential resource allocation in public health intervention programs.
Statistical Inference After Adaptive Sampling for Longitudinal Data
In this work, we focus on longitudinal user data collected by a large class of adaptive sampling algorithms that are designed to optimize treatment decisions online using accruing data from multiple users.
Rate-matching the regret lower-bound in the linear quadratic regulator with unknown dynamics
The linear quadratic regulator with unknown dynamics is a fundamental reinforcement learning setting with significant structure in its dynamics and cost function, yet even in this setting there is a gap between the best known regret lower-bound of $\Omega_p(\sqrt{T})$ and the best known upper-bound of $O_p(\sqrt{T}\,\text{polylog}(T))$.
Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis
We propose a new hypothesis testing approach based on the conditional randomization test to answer the most fundamental question of conjoint analysis: Does a factor of interest matter in any way given the other factors?
A Simple and Efficient Sampling-based Algorithm for General Reachability Analysis
In this work, we analyze an efficient sampling-based algorithm for general-purpose reachability analysis, which remains a notoriously challenging problem with applications ranging from neural network verification to safety analysis of dynamical systems.
The Role of Tactile Sensing in Learning and Deploying Grasp Refinement Algorithms
Our first experiment investigates the need for rich tactile sensing in the rewards of RL-based grasp refinement algorithms for multi-fingered robotic hands.
Statistical Inference with M-Estimators on Adaptively Collected Data
Yet there is a lack of general methods for conducting statistical inference using more complex models on data collected with (contextual) bandit algorithms; for example, current methods cannot be used for valid inference on parameters in a logistic regression model for a binary reward.
Exact Asymptotics for Linear Quadratic Adaptive Control
Recent progress in reinforcement learning has led to remarkable performance in a range of applications, but its deployment in high-stakes settings remains quite rare.
Cross-validation Confidence Intervals for Test Error
This work develops central limit theorems for cross-validation and consistent estimators of its asymptotic variance under weak stability conditions on the learning algorithm.
Floodgate: inference for model-free variable importance
Many modern applications seek to understand the relationship between an outcome variable $Y$ and a covariate $X$ in the presence of a (possibly high-dimensional) confounding variable $Z$.
Methodology
Fast and Powerful Conditional Randomization Testing via Distillation
We propose the distilled CRT, a novel approach to using state-of-the-art machine learning algorithms in the CRT while drastically reducing the number of times those algorithms need to be run, thereby taking advantage of their power and the CRT's statistical guarantees without suffering the usual computational expense.
Methodology
Inference for Batched Bandits
As bandit algorithms are increasingly utilized in scientific studies and industrial applications, there is an associated increasing need for reliable inference methods based on the resulting adaptively-collected data.
Relaxing the Assumptions of Knockoffs by Conditioning
The recent paper Cand\`es et al. (2018) introduced model-X knockoffs, a method for variable selection that provably and non-asymptotically controls the false discovery rate with no restrictions or assumptions on the dimensionality of the data or the conditional distribution of the response given the covariates.
Methodology
Metropolized Knockoff Sampling
Model-X knockoffs is a wrapper that transforms essentially any feature importance measure into a variable selection algorithm, which discovers true effects while rigorously controlling the expected fraction of false positives.
Methodology
Safe Motion Planning in Unknown Environments: Optimality Benchmarks and Tractable Policies
This paper addresses the problem of planning a safe (i. e., collision-free) trajectory from an initial state to a goal region when the obstacle space is a-priori unknown and is incrementally revealed online, e. g., through line-of-sight perception.
Panning for Gold: Model-X Knockoffs for High-dimensional Controlled Variable Selection
Whereas the knockoffs procedure is constrained to homoscedastic linear models with $n\ge p$, the key innovation here is that model-X knockoffs provide valid inference from finite samples in settings in which the conditional distribution of the response is arbitrary and completely unknown.
Methodology Statistics Theory Applications Statistics Theory
Risk-Constrained Reinforcement Learning with Percentile Risk Criteria
In many sequential decision-making problems one is interested in minimizing an expected cumulative cost while taking into account \emph{risk}, i. e., increased awareness of events of small probability and high consequences.
Monte Carlo Motion Planning for Robot Trajectory Optimization Under Uncertainty
MCMP applies this CP estimation procedure to motion planning by iteratively (i) computing an (approximately) optimal path for the deterministic version of the problem (here, using the FMT* algorithm), (ii) computing the CP of this path, and (iii) inflating or deflating the obstacles by a common factor depending on whether the CP is higher or lower than a target value.
Robotics
