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Found 15 papers, 0 papers with code
Overlapping Batch Confidence Intervals on Statistical Functionals Constructed from Time Series: Application to Quantiles, Optimization, and Estimation
The message from extensive numerical experimentation is that in settings where a functional CLT on the point estimator is in effect, using \emph{large overlapping batches} alongside OB-x critical values yields confidence intervals that are often of significantly higher quality than those obtained from more generic methods like subsampling or the bootstrap.
Minimax Optimal Estimation of Stability Under Distribution Shift
The performance of decision policies and prediction models often deteriorates when applied to environments different from the ones seen during training.
Risk-Sensitive Markov Decision Processes with Long-Run CVaR Criterion
CVaR (Conditional Value at Risk) is a risk metric widely used in finance.
The Typical Behavior of Bandit Algorithms
The tail characterizations there are associated with atypical bandit behavior on trajectories where the optimal arm mean is under-estimated, leading to mis-identification of the optimal arm and large regret.
Modified Frank Wolfe in Probability Space
We propose a novel Frank-Wolfe (FW) procedure for the optimization of infinite-dimensional functionals of probability measures - a task which arises naturally in a wide range of areas including statistical learning (e. g. variational inference) and artificial intelligence (e. g. generative adversarial networks).
The Fragility of Optimized Bandit Algorithms
It is well known that designs that are optimal over certain exponential families can achieve expected regret that grows logarithmically in the number of arm plays, at a rate governed by the Lai-Robbins lower bound.
Distributed stochastic optimization with large delays
One of the most widely used methods for solving large-scale stochastic optimization problems is distributed asynchronous stochastic gradient descent (DASGD), a family of algorithms that result from parallelizing stochastic gradient descent on distributed computing architectures (possibly) asychronously.
Diffusion Approximations for Thompson Sampling
In the regime where the gaps between arm means scale as $1/\sqrt{n}$ with the time horizon $n$, we show that the dynamics of Thompson sampling evolve according to discrete versions of SDEs and random ODEs.
On Incorporating Forecasts into Linear State Space Model Markov Decision Processes
Weather forecast information will very likely find increasing application in the control of future energy systems.
Sequential Batch Learning in Finite-Action Linear Contextual Bandits
We study the sequential batch learning problem in linear contextual bandits with finite action sets, where the decision maker is constrained to split incoming individuals into (at most) a fixed number of batches and can only observe outcomes for the individuals within a batch at the batch's end.
Learning in Games with Lossy Feedback
We consider a game-theoretical multi-agent learning problem where the feedback information can be lost during the learning process and rewards are given by a broad class of games known as variationally stable games.
Change-Point Testing for Risk Measures in Time Series
We propose novel methods for change-point testing for nonparametric estimators of expected shortfall and related risk measures in weakly dependent time series.
Countering Feedback Delays in Multi-Agent Learning
We consider a model of game-theoretic learning based on online mirror descent (OMD) with asynchronous and delayed feedback information.
Stochastic Mirror Descent in Variationally Coherent Optimization Problems
In this paper, we examine a class of non-convex stochastic optimization problems which we call variationally coherent, and which properly includes pseudo-/quasiconvex and star-convex optimization problems.
Shape-constrained Estimation of Value Functions
We present a fully nonparametric method to estimate the value function, via simulation, in the context of expected infinite-horizon discounted rewards for Markov chains.
