Linear Bandits with Feature Feedback

This paper explores a new form of the linear bandit problem in which the algorithm receives the usual stochastic rewards as well as stochastic feedback about which features are relevant to the rewards, the latter feedback being the novel aspect. The focus of this paper is the development of new theory and algorithms for linear bandits with feature feedback. We show that linear bandits with feature feedback can achieve regret over time horizon $T$ that scales like $k\sqrt{T}$, without prior knowledge of which features are relevant nor the number $k$ of relevant features. In comparison, the regret of traditional linear bandits is $d\sqrt{T}$, where $d$ is the total number of (relevant and irrelevant) features, so the improvement can be dramatic if $k\ll d$. The computational complexity of the new algorithm is proportional to $k$ rather than $d$, making it much more suitable for real-world applications compared to traditional linear bandits. We demonstrate the performance of the new algorithm with synthetic and real human-labeled data

Pessimistic Off-Policy Multi-Objective Optimization

Multi-objective optimization is a type of decision making problems where multiple conflicting objectives are optimized. We study offline optimization of multi-objective policies from data collected by an existing policy. We propose a pessimistic estimator for the multi-objective policy values that can be easily plugged into existing formulas for hypervolume computation and optimized. The estimator is based on inverse propensity scores (IPS), and improves upon a naive IPS estimator in both theory and experiments. Our analysis is general, and applies beyond our IPS estimators and methods for optimizing them. The pessimistic estimator can be optimized by policy gradients and performs well in all of our experiments

IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences

Abstract Background Microbiome studies often involve sequencing a marker gene to identify the microorganisms in samples of interest. Sequence classification is a critical component of this process, whereby sequences are assigned to a reference taxonomy containing known sequence representatives of many microbial groups. Previous studies have shown that existing classification programs often assign sequences to reference groups even if they belong to novel taxonomic groups that are absent from the reference taxonomy. This high rate of “over classification” is particularly detrimental in microbiome studies because reference taxonomies are far from comprehensive. Results Here, we introduce IDTAXA, a novel approach to taxonomic classification that employs principles from machine learning to reduce over classification errors. Using multiple reference taxonomies, we demonstrate that IDTAXA has higher accuracy than popular classifiers such as BLAST, MAPSeq, QIIME, SINTAX, SPINGO, and the RDP Classifier. Similarly, IDTAXA yields far fewer over classifications on Illumina mock microbial community data when the expected taxa are absent from the training set. Furthermore, IDTAXA offers many practical advantages over other classifiers, such as maintaining low error rates across varying input sequence lengths and withholding classifications from input sequences composed of random nucleotides or repeats. Conclusions IDTAXA’s classifications may lead to different conclusions in microbiome studies because of the substantially reduced number of taxa that are incorrectly identified through over classification. Although misclassification error is relatively minor, we believe that many remaining misclassifications are likely caused by errors in the reference taxonomy. We describe how IDTAXA is able to identify many putative mislabeling errors in reference taxonomies, enabling training sets to be automatically corrected by eliminating spurious sequences. IDTAXA is part of the DECIPHER package for the R programming language, available through the Bioconductor repository or accessible online (http://DECIPHER.codes)

Neural Reconstruction with Approximate Message Passing (NeuRAMP)

Many functional descriptions of spiking neurons assume a cascade structure where inputs are passed through an initial linear filtering stage that produces a lowdimensional signal that drives subsequent nonlinear stages. This paper presents a novel and systematic parameter estimation procedure for such models and applies the method to two neural estimation problems: (i) compressed-sensing based neural mapping from multi-neuron excitation, and (ii) estimation of neural receptive fields in sensory neurons. The proposed estimation algorithm models the neurons via a graphical model and then estimates the parameters in the model using a recently-developed generalized approximate message passing (GAMP) method. The GAMP method is based on Gaussian approximations of loopy belief propagation. In the neural connectivity problem, the GAMP-based method is shown to be computational efficient, provides a more exact modeling of the sparsity, can incorporate nonlinearities in the output and significantly outperforms previous compressed-sensing methods. For the receptive field estimation, the GAMP method can also exploit inherent structured sparsity in the linear weights. The method is validated on estimation of linear nonlinear Poisson (LNP) cascade models for receptive fields of salamander retinal ganglion cells.

Perception of college-going girls towards corneal donation in North India: A latent class analysis study

Purpose: To assess the perception of college-going girls toward corneal donation in Northern India. Methods: An online survey with a pre-structured, pre-validated questionnaire was conducted on 1721 college-going girls in Northern India. The knowledge and attitude scores were regressed, and latent class analysis was carried out. Results: The average of scores for all participants was computed individually for the knowledge questions and the attitude questions, and based on this score, total participants were divided into two groups: Better corneal donation behaviors (BCDB) and poor corneal donation behaviors. The binomial logistic regression model of knowledge domain for predicting BCDB, age of the participant, their awareness about corneal donation, and willingness to discuss eye donation among family members were found significant. Similarly, for the attitude domain, awareness about corneal donation, knowledge about hours within which ideal eye donation needs to be undertaken, and knowledge about eye donation during coronavirus disease 2019 (COVID-19) pandemic were found to be significant. Latent class analysis identified one subset of participants having poorer knowledge and attitude scores and that they were more from a rural background, were having more than first order as birth order, were belonging to SC/ST classes, had illiterate or secondary education of father and mother, and were living in rented houses. Conclusion: The findings of the study significantly contribute to devising a mechanism to improve knowledge and influencing the attitude about eye donation among the youth, especially young women, who can act as counselors and motivators for the masses as well as their own families, in the generations to come