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Found 17 papers, 0 papers with code
Adaptive Sampling for Estimating Probability Distributions
We consider the problem of allocating a fixed budget of samples to a finite set of discrete distributions to learn them uniformly well (minimizing the maximum error) in terms of four common distance measures: $\ell_2^2$, $\ell_1$, $f$-divergence, and separation distance.
Deep anytime-valid hypothesis testing
We propose a general framework for constructing powerful, sequential hypothesis tests for a large class of nonparametric testing problems.
On the near-optimality of betting confidence sets for bounded means
Constructing nonasymptotic confidence intervals (CIs) for the mean of a univariate distribution from independent and identically distributed (i. i. d.)
Reducing sequential change detection to sequential estimation
We consider the problem of sequential change detection, where the goal is to design a scheme for detecting any changes in a parameter or functional $\theta$ of the data stream distribution that has small detection delay, but guarantees control on the frequency of false alarms in the absence of changes.
Risk-limiting Financial Audits via Weighted Sampling without Replacement
Next, we develop methods to improve the quality of CSs by incorporating side information about the unknown values associated with each item.
Sequential change detection via backward confidence sequences
We present a simple reduction from sequential estimation to sequential changepoint detection (SCD).
A Permutation-Free Kernel Independence Test
In nonparametric independence testing, we observe i. i. d.\ data $\{(X_i, Y_i)\}_{i=1}^n$, where $X \in \mathcal{X}, Y \in \mathcal{Y}$ lie in any general spaces, and we wish to test the null that $X$ is independent of $Y$.
A Permutation-free Kernel Two-Sample Test
The usual kernel-MMD test statistic is a degenerate U-statistic under the null, and thus it has an intractable limiting distribution.
Instance-Dependent Regret Analysis of Kernelized Bandits
We study the kernelized bandit problem, that involves designing an adaptive strategy for querying a noisy zeroth-order-oracle to efficiently learn about the optimizer of an unknown function $f$ with a norm bounded by $M<\infty$ in a Reproducing Kernel Hilbert Space~(RKHS) associated with a positive definite kernel $K$.
Adaptive Sampling for Minimax Fair Classification
Machine learning models trained on uncurated datasets can often end up adversely affecting inputs belonging to underrepresented groups.
Multi-Scale Zero-Order Optimization of Smooth Functions in an RKHS
We aim to optimize a black-box function $f:\mathcal{X} \mapsto \mathbb{R}$ under the assumption that $f$ is H\"older smooth and has bounded norm in the RKHS associated with a given kernel $K$.
Active Model Estimation in Markov Decision Processes
Using a number of simple domains with heterogeneous noise in their transitions, we show that our heuristic-based algorithm outperforms both our original algorithm and the maximum entropy algorithm in the small sample regime, while achieving similar asymptotic performance as that of the original algorithm.
Adaptive Sampling for Estimating Multiple Probability Distributions
We consider the problem of allocating samples to a finite set of discrete distributions in order to learn them uniformly well in terms of four common distance measures: $\ell_2^2$, $\ell_1$, $f$-divergence, and separation distance.
Active Learning for Binary Classification with Abstention
We construct and analyze active learning algorithms for the problem of binary classification with abstention.
Binary Classification with Bounded Abstention Rate
We then propose a plug-in classifier that employs unlabeled samples to decide the region of abstention and derive an upper-bound on the excess risk of our classifier under standard \emph{H\"older smoothness} and \emph{margin} assumptions.
Multiscale Gaussian Process Level Set Estimation
In this paper, the problem of estimating the level set of a black-box function from noisy and expensive evaluation queries is considered.
Gaussian Process bandits with adaptive discretization
In this paper, the problem of maximizing a black-box function $f:\mathcal{X} \to \mathbb{R}$ is studied in the Bayesian framework with a Gaussian Process (GP) prior.
