285 citations · 409 across the 28 of their papers we have counts for
8 papers · 1 filter
The Utility of Abstaining in Binary Classification
Akshay Balsubramani
We explore the problem of binary classification in machine learning, with a twist - the classifier is allowed to abstain on any datum, professing ignorance about the true class lab…
Optimal Binary Classifier Aggregation for General Losses
Akshay Balsubramani, Yoav Freund
We address the problem of aggregating an ensemble of predictors with known loss bounds in a semi-supervised binary classification setting, to minimize prediction loss incurred on t…
PAC-Bayes Iterated Logarithm Bounds for Martingale Mixtures
Akshay Balsubramani
We give tight concentration bounds for mixtures of martingales that are simultaneously uniform over (a) mixture distributions, in a PAC-Bayes sense; and (b) all finite times. These…
Scalable Semi-Supervised Aggregation of Classifiers
Akshay Balsubramani, Yoav Freund
We present and empirically evaluate an efficient algorithm that learns to aggregate the predictions of an ensemble of binary classifiers. The algorithm uses the structure of the en…
Sequential Nonparametric Testing with the Law of the Iterated Logarithm
Akshay Balsubramani, Aaditya Ramdas
We propose a new algorithmic framework for sequential hypothesis testing with i.i.d. data, which includes A/B testing, nonparametric two-sample testing, and independence testing as…
Optimally Combining Classifiers Using Unlabeled Data
Akshay Balsubramani, Yoav Freund
We develop a worst-case analysis of aggregation of classifier ensembles for binary classification. The task of predicting to minimize error is formulated as a game played over a gi…
PAC-Bayes with Minimax for Confidence-Rated Transduction
Akshay Balsubramani, Yoav Freund
We consider using an ensemble of binary classifiers for transductive prediction, when unlabeled test data are known in advance. We derive minimax optimal rules for confidence-rated…
The Fast Convergence of Incremental PCA
Akshay Balsubramani, Sanjoy Dasgupta, Yoav Freund
We consider a situation in which we see samples in drawn i.i.d. from some distribution with mean zero and unknown covariance A. We wish to compute the top eigenvecto…