arXiv research

BestChanID identifies the channel with maximal capacity using training sequences.

Submodular function maximization finds application in a variety of real-world decision-making problems. However, most existing methods, based on greedy maximization, assume it is computationally feasible to evaluate F, the function being maximized. Unfortunately, in many realistic settings F is too expensive to evaluat…

Hypothesis testing is an important problem with applications in target localization, clinical trials etc. Many active hypothesis testing strategies operate in two phases: an exploration phase and a verification phase. In the exploration phase, selection of experiments is such that a moderate level of confidence on the …

Algorithm maximizes rewards with a budget and giving up option.

This paper improves test-time adaptation for distribution shifts using confidence maximization and input transformation.

StoSOO optimistically maximizes noisy, locally smooth functions.

Inexact acquisition solutions in BO lead to sublinear cumulative regret.

Adversarial training yields robust models against a specific threat model, e.g., $L_\infty$ adversarial examples. Typically robustness does not generalize to previously unseen threat models, e.g., other $L_p$ norms, or larger perturbations. Our confidence-calibrated adversarial training (CCAT) tackles this problem by b…

The paper shows over-confidence in models isn't just due to over-parametrization.

The paper identifies the best treatment to maximize NDPO, a key outcome in causal mediation analysis.

Typical dimensionality reduction methods focus on directly reducing the number of random variables while retaining maximal variations in the data. In this paper, we consider the dimensionality reduction in parameter spaces of binary multivariate distributions. We propose a general Confident-Information-First (CIF) prin…

Typical dimensionality reduction (DR) methods are often data-oriented, focusing on directly reducing the number of random variables (features) while retaining the maximal variations in the high-dimensional data. In unsupervised situations, one of the main limitations of these methods lies in their dependency on the sca…

Discriminatively trained neural classifiers can be trusted, only when the input data comes from the training distribution (in-distribution). Therefore, detecting out-of-distribution (OOD) samples is very important to avoid classification errors. In the context of OOD detection for image classification, one of the recen…

We describe Information Forests, an approach to classification that generalizes Random Forests by replacing the splitting criterion of non-leaf nodes from a discriminative one -- based on the entropy of the label distribution -- to a generative one -- based on maximizing the information divergence between the class-con…

New method improves auto-labeling accuracy by optimizing confidence functions.

This paper improves multi-class calibration methods using mutual information maximization-based binning.

Optimizes energy efficiency in wireless sensor networks with limited information.

Unified theory for UCB policies in total and max bandit problems.

The ultimate goal of a supervised learning algorithm is to produce models constructed on the training data that can generalize well to new examples. In classification, functional margin maximization -- correctly classifying as many training examples as possible with maximal confidence --has been known to construct mode…

Unified technique for sequential estimation of convex divergences.

A new UCB policy improves reward-cost ratio estimation in budgeted MAB.

New approach to multi-armed bandit problem aims to maximize highest total reward.

We study the problem of online influence maximization in social networks. In this problem, a learner aims to identify the set of "best influencers" in a network by interacting with it, i.e., repeatedly selecting seed nodes and observing activation feedback in the network. We capitalize on an important property of the i…

Study optimizes dynamic product selection and pricing using censored preference feedback.

We discuss the problem of risk estimation in the classification problem, with specific focus on finding distributions that maximize the confidence intervals of risk estimation. We derived simple analytic approximations for the maximum bias of empirical risk for histogram classifier. We carry out a detailed study on usi…

NoMoPy models noise as HMM/FHMM in Python.

A new method for semi-supervised learning with missing data using GMM and margin confidence.

SMURF-THP improves Transformer Hawkes process models by providing uncertainty quantification.

New method estimates animal density using acoustic data, accounting for unknown call identities.

Measuring mutual information from finite data is difficult. Recent work has considered variational methods maximizing a lower bound. In this paper, we prove that serious statistical limitations are inherent to any method of measuring mutual information. More specifically, we show that any distribution-free high-confide…

Extended Ville's inequality for nonintegrable supermartingales.

Deep learning models are known to be overconfident in their predictions on out of distribution inputs. There have been several pieces of work to address this issue, including a number of approaches for building Bayesian neural networks, as well as closely related work on detection of out of distribution samples. Recent…

A new online learning problem, CAB, tackles matching platforms to maximize user satisfaction.

Much recent research has been conducted in the area of Bayesian learning, particularly with regard to the optimization of hyper-parameters via Gaussian process regression. The methodologies rely chiefly on the method of maximizing the expected improvement of a score function with respect to adjustments in the hyper-par…

Proposes a new sampling method for online learning with cumulative oversampling.

The paper addresses uncertainty in demand prediction for dynamic pricing.

In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework that allows a general nonlinear reward function, whose expected value may not depend only on the means of the input random variables but possibly on the entire distributions of these variables. Our framework enables a much larger c…

We provide a general theory of the expectation-maximization (EM) algorithm for inferring high dimensional latent variable models. In particular, we make two contributions: (i) For parameter estimation, we propose a novel high dimensional EM algorithm which naturally incorporates sparsity structure into parameter estima…

Paper aims to ensure reliable detection of out-of-distribution data with certifiable worst-case guarantees.

The paper tackles multi-armed bandits with vector losses, focusing on minimizing the $\ell^\infty$-norm of relative losses.

New method balances performance and cost in identifying best arm.

Proposes a novel graph self-training method with EM regularization for semi-supervised node classification.

In this paper we describe a new algorithm called Fast Adaptive Sequencing Technique (FAST) for maximizing a monotone submodular function under a cardinality constraint $k$ whose approximation ratio is arbitrarily close to $1-1/e$, is $O(\log(n) \log^2(\log k))$ adaptive, and uses a total of $O(n \log\log(k))$ queries. …

New pricing algorithm learns demand curves and optimizes prices in dynamic markets.

Maximin UCB algorithm optimizes energy harvesting for sensor networks.

We study an extension of the classic stochastic multi-armed bandit problem which involves multiple plays and Markovian rewards in the rested bandits setting. In order to tackle this problem we consider an adaptive allocation rule which at each stage combines the information from the sample means of all the arms, with t…

Optimal simple regret bound for Gaussian Process bandits.

We propose and address a novel few-shot RL problem, where a task is characterized by a subtask graph which describes a set of subtasks and their dependencies that are unknown to the agent. The agent needs to quickly adapt to the task over few episodes during adaptation phase to maximize the return in the test phase. In…