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Found 27 papers, 1 papers with code
Distribution Learnability and Robustness
We examine the relationship between learnability and robust (or agnostic) learnability for the problem of distribution learning.
Continual Learning: Applications and the Road Forward
Continual learning is a subfield of machine learning, which aims to allow machine learning models to continuously learn on new data, by accumulating knowledge without forgetting what was learned in the past.
On Computable Online Learning
Finally, we consider online learning with no requirements for optimality, and show, under a weaker notion of computability, that the finiteness of the Littlestone dimension no longer characterizes whether a class is c-online learnable with finite mistake bound.
Impossibility results for fair representations
The goal of such representations is that a model trained on data under the representation (e. g., a classifier) will be guaranteed to respect some fairness constraints.
Impossibility results for fair representation
With the growing awareness to fairness in machine learning and the realization of the central role that data representation has in data processing tasks, there is an obvious interest in notions of fair data representations.
Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability
We then model the act of enforcing interpretability as that of performing empirical risk minimization over the set of interpretable hypotheses.
On Learnability with Computable Learners
We initiate a study of learning with computable learners and computable output predictors.
When can unlabeled data improve the learning rate?
In semi-supervised classification, one is given access both to labeled and unlabeled data.
Learnability can be undecidable
We show that, in some cases, a solution to the ‘estimating the maximum’ problem is equivalent to the continuum hypothesis.
Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes
We prove that ϴ(k d^2 / ε^2) samples are necessary and sufficient for learning a mixture of k Gaussians in R^d, up to error ε in total variation distance.
Semi-supervised clustering for de-duplication
In this work, we view de-duplication as a clustering problem where the goal is to put records corresponding to the same physical entity in the same cluster and putting records corresponding to different physical entities into different clusters.
Clustering - What Both Theoreticians and Practitioners are Doing Wrong
Unsupervised learning is widely recognized as one of the most important challenges facing machine learning nowa- days.
Empirical Risk Minimization under Fairness Constraints
It encourages the conditional risk of the learned classifier to be approximately constant with respect to the sensitive variable.
Provably noise-robust, regularised $k$-means clustering
We focus on the $k$-means objective and we prove that the regularised version of $k$-means is NP-Hard even for $k=1$.
A learning problem that is independent of the set theory ZFC axioms
We consider the following statistical estimation problem: given a family F of real valued functions over some domain X and an i. i. d.
Near-optimal Sample Complexity Bounds for Robust Learning of Gaussians Mixtures via Compression Schemes
We prove that $\tilde{\Theta}(k d^2 / \varepsilon^2)$ samples are necessary and sufficient for learning a mixture of $k$ Gaussians in $\mathbb{R}^d$, up to error $\varepsilon$ in total variation distance.
Sample-Efficient Learning of Mixtures
Let $\mathcal F$ be an arbitrary class of probability distributions, and let $\mathcal{F}^k$ denote the class of $k$-mixtures of elements of $\mathcal F$.
Clustering with Same-Cluster Queries
We show that there is a trade off between computational complexity and query complexity; We prove that for the case of $k$-means clustering (i. e., when the expert conforms to a solution of $k$-means), having access to relatively few such queries allows efficient solutions to otherwise NP hard problems.
Multi-task and Lifelong Learning of Kernels
We consider a problem of learning kernels for use in SVM classification in the multi-task and lifelong scenarios and provide generalization bounds on the error of a large margin classifier.
Clustering is Easy When ....What?
I wish to present a a critical bird's eye overview of the results published on this issue so far and to call attention to the gap between available and desirable results on this issue.
2 Notes on Classes with Vapnik-Chervonenkis Dimension 1
Arguably the most influential consequence of the VC analysis is the fundamental theorem of statistical machine learning, stating that a concept class is learnable (in some precise sense) if and only if its VC-dimension is finite.
Representation Learning for Clustering: A Statistical Framework
The algorithm designer then uses that sample to come up with a data representation under which $k$-means clustering results in a clustering (of the full data set) that is aligned with the user's clustering.
Computational Feasibility of Clustering under Clusterability Assumptions
In particular, we list some implied requirements for notions of clusterability.
Multiclass learnability and the ERM principle
We study the sample complexity of multiclass prediction in several learning settings.
Weighted Clustering
One of the most prominent challenges in clustering is "the user's dilemma," which is the problem of selecting an appropriate clustering algorithm for a specific task.
Towards Property-Based Classification of Clustering Paradigms
We propose to address this problem by distilling abstract properties of the input-output behavior of different clustering paradigms.
Measures of Clustering Quality: A Working Set of Axioms for Clustering
In this respect, we follow up on the work of Kelinberg, (Kleinberg) that showed an impossibility result for such axiomatization.
