In this paper, we make an important step towards the black-box machine teaching by considering the cross-space machine teaching, where the teacher and the learner use different feature representations and the teacher can not fully observe the learner's model. In such scenario, we study how the teacher is still able to …
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48 results for “active learners”
Improves learning efficiency for active sequential learners.
problem Optimizing training data for sequential learners who actively choose their queries.
method Formulated as a Markov decision process, addressing both teaching and learning from a teacher.
result Planning teaching and learner's model of the teacher improve learning outcomes.
Teaches an active learner with contrastive examples to speed up learning.
problem Active learning with a helpful teacher and contrastive examples.
method Designs an adaptive teaching algorithm to provide informative contrastive examples.
result Strong performance guarantees and approximation guarantees for specific learners.
Discriminative learning machines often need a large set of labeled samples for training. Active learning (AL) settings assume that the learner has the freedom to ask an oracle to label its desired samples. Traditional AL algorithms heuristically choose query samples about which the current learner is uncertain. This st…
Bayesian active learning tackles nuisance parameters, leading to bias and dilemmas.
problem Bayesian active learning with nuisance parameters leads to bias and dilemmas.
method Characterizes and mitigates negative interference by accurately estimating nuisance parameters.
result The extent of negative interference can be extremely large, and accurate estimation of nuisance parameters is critical.
Active learning (AL) aims to enable training high performance classifiers with low annotation cost by predicting which subset of unlabelled instances would be most beneficial to label. The importance of AL has motivated extensive research, proposing a wide variety of manually designed AL algorithms with diverse theoret…
New model considers varying costs in learning, outperforming existing methods.
problem Learning with varying costs in machine learning models.
method Introduces ε-frugal learning that considers both known and unknown costs.
result ε-frugal learners outperform learners with known costs and random sampling.
In this paper we address the problem of pool based active learning, and provide an algorithm, called UPAL, that works by minimizing the unbiased estimator of the risk of a hypothesis in a given hypothesis space. For the space of linear classifiers and the squared loss we show that UPAL is equivalent to an exponentially…
We propose a new active learning algorithm for parametric linear regression with random design. We provide finite sample convergence guarantees for general distributions in the misspecified model. This is the first active learner for this setting that provably can improve over passive learning. Unlike other learning se…
Active learning can't improve over passive in certain settings.
problem Active learning vs. passive learning in nonparametric settings.
method Analyzing margin conditions and their effects on active learning performance.
result Nuances in margin conditions determine whether active learning can outperform passive learning.
Variational Temporal IRT: Fast, Accurate, and Explainable Inference of Dynamic Learner Proficiencycs.LG
VTIRT speeds up IRT inference for dynamic learner proficiency.
problem Expensive and slow inference algorithms for dynamic IRT models.
method Variational Temporal IRT (VTIRT) for fast, accurate inference.
result Orders of magnitude speedup in inference runtime with accurate results.
Gradient-based meta-learning methods leverage gradient descent to learn the commonalities among various tasks. While previous such methods have been successful in meta-learning tasks, they resort to simple gradient descent during meta-testing. Our primary contribution is the {\em MT-net}, which enables the meta-learner…
Dynamic ensemble active learning tackles non-stationary criteria in active learning.
problem Active learning's effectiveness varies across datasets and sessions, leading to suboptimal results.
method Developed a dynamic ensemble active learner based on a non-stationary multi-armed bandit with expert advice.
result Dynamic ensemble selects the best criteria at each step, improving overall performance.
Active learning algorithms propose which unlabeled objects should be queried for their labels to improve a predictive model the most. We study active learners that minimize generalization bounds and uncover relationships between these bounds that lead to an improved approach to active learning. In particular we show th…
Indirect Active Learningmath.ST
New method for learning indirectly through control variables.
problem Learning relationships when direct manipulation of variables is impossible.
method Study of indirect active learning under nonparametric models with fixed budget.
result Minimax rates for estimating relationships between variables.
Proposes a method to imitate active learning heuristics for better performance.
problem The performance of active learning heuristics depends on the classifier model and data structure.
method Imitates the selection of the best active learning heuristic using DAGGER.
result Outperforms state-of-the-art imitation learners and heuristics on well-known datasets.
Active Coveringcs.LG
Active learning method reduces label queries for positive examples.
problem Minimizing label queries for identifying all positive examples.
method Repurposed support estimator as offline algorithm, and a simple active learning method.
result Improved excess query cost of compared to offline methods.
Conventional learning with expert advice methods assumes a learner is always receiving the outcome (e.g., class labels) of every incoming training instance at the end of each trial. In real applications, acquiring the outcome from oracle can be costly or time consuming. In this paper, we address a new problem of active…
Active learning method improves AI performance by balancing exploration and exploitation.
problem Efficiently acquiring samples for supervised learning in streaming data.
method Ensemble active learning by contextual bandits.
result Improved AI modeling performance through better sample acquisition.
Agent learns from an expert, adapting to constraints in concept learning.
problem Insufficient query selection in active learning for realistic human domains.
method Imitation learning to reason about both internal goals and external constraints.
result Agent outperforms other active learners under most constrained conditions.
New algorithm reduces clustering cost in bandit feedback.
problem Discovering hidden groupings in stochastic bandit feedback.
method ACB algorithm for active clustering with non-asymptotic lower bound.
result ACB algorithm's budget matches lower bound in most regimes.
Meta-learners improve causal effect estimation in small samples.
problem Estimating causal effects using machine learning methods.
method Sample-splitting and cross-fitting to reduce overfitting bias.
result Meta-learners' performance depends on sample size and estimation procedure.
New algorithm for active learning in multiple matrix completion problems.
problem Simultaneous matrix completion with unknown ranks and varying sizes.
method MAlocate algorithm that adapts to unknown ranks and sizes.
result Minimax-optimal performance demonstrated with lower-bound analysis.
Online active regression algorithms minimize label queries for efficient data regression.
problem Efficiently predict data points with minimal label queries in an online setting.
method Proposed online algorithms for active regression under ℓ_p loss, achieving (1+ε)-approximation with minimal label queries.
result Achieves (1+ε)-approximation with only ε^(-1) d log(nκ) label queries, matching offline methods in performance.
Classical learning assumes the learner is given a labeled data sample, from which it learns a model. The field of Active Learning deals with the situation where the learner begins not with a training sample, but instead with resources that it can use to obtain information to help identify the optimal model. To better u…
A new approach for learning from expert demonstrations using multiple perspectives.
problem Learning from expert demonstrations with limited information from different perspectives.
method Generative adversarial network-based active learning approach.
result Our approach effectively learns from expert demonstrations and highlights the importance of architectural choices.
"Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Usersstat.ML
Although interactive learning puts the user into the loop, the learner remains mostly a black box for the user. Understanding the reasons behind queries and predictions is important when assessing how the learner works and, in turn, trust. Consequently, we propose the novel framework of explanatory interactive learning…
An active learner is given a hypothesis class, a large set of unlabeled examples and the ability to interactively query labels to an oracle of a subset of these examples; the goal of the learner is to learn a hypothesis in the class that fits the data well by making as few label queries as possible. This work addresses…
Study active learning of PTFs with derivative access.
problem Active learning of polynomial threshold functions (PTFs).
method Algorithm for active learning degree- univariate PTFs with derivative access.
result Computational efficient algorithm for active learning degree- univariate PTFs.
Active learning (AL) is a learning paradigm where an active learner has to train a model (e.g., a classifier) which is in principal trained in a supervised way, but in AL it has to be done by means of a data set with initially unlabeled samples. To get labels for these samples, the active learner has to ask an oracle (…
A new active learning method that improves model diversity and performance.
problem Efficiently selecting examples to label for training multiple models.
method Trains multiple models on bootstrap samples and selects examples based on minimum margin.
result Min-margin outperforms other methods, especially with larger batch sizes.
An active learner is given a class of models, a large set of unlabeled examples, and the ability to interactively query labels of a subset of these examples; the goal of the learner is to learn a model in the class that fits the data well. Previous theoretical work has rigorously characterized label complexity of activ…
Gradient descent learns a neuron in noisy data.
problem Learning a single neuron with adversarial label noise.
method Gradient descent on the -loss.
result Efficient approximate learners for various distributions and activations.
DAL uses disentanglement for automatic labeling in GAN-based active learning.
problem Reducing human labeling in GAN-based active learning.
method DAL leverages disentanglement in InfoGAN to automatically label datapoints, deciding human labeling based on disagreement with InfoGAN labels and label correction.
result DAL achieves better performance than existing GAN-based active learning approaches on image classification tasks.
Develops a real-time exercise recommendation system using deep learning.
problem Improving accuracy in exercise recommendation systems without user feedback.
method Deep recurrent neural network with attention mechanisms, real-time expert feedback.
result Improved accuracy in exercise recommendation system after real-time active learning.
OSAMD adapts online to changing distributions with limited labels.
problem Models struggle with continual distribution shifts and expensive labeling in changing environments.
method Online Active Continual Adaptation with OSAMD, an online teacher-student structure and margin-based criterion.
result OSAMD achieves favorable dynamic regret bounds under changing environments with limited labels.
This paper optimizes the number of comparisons needed to find the best k items from pairwise comparisons.
problem Finding the best k items from pairwise comparisons with limited comparisons.
method Developed algorithms for finding probably approximately correct and exact best k items under stochastic conditions.
result Upper and lower bounds on the number of comparisons for finding the best k items, with matching upper bounds for PAC best k items.
Efficient algorithms identify true hypothesis from many options with minimal actions.
problem Identifying true hypothesis from a large set of options with minimal actions.
method Greedy approximation algorithms for active sequential hypothesis testing.
result First approximation guarantees for ASHT, independent of the number of hypotheses.
New method predicts model performance change in text classification.
problem Data annotation bottleneck in text classification systems.
method Predicted Change of F Measure for stopping active learning.
result Estimates how much model performance changes at each iteration.
In this paper, we tackle the question of discovering an effective set of spatial filters to solve hyperspectral classification problems. Instead of fixing a priori the filters and their parameters using expert knowledge, we let the model find them within random draws in the (possibly infinite) space of possible filters…
In multi-task learning, a learner is given a collection of prediction tasks and needs to solve all of them. In contrast to previous work, which required that annotated training data is available for all tasks, we consider a new setting, in which for some tasks, potentially most of them, only unlabeled training data is …
Active learning has shown to reduce the number of experiments needed to obtain high-confidence drug-target predictions. However, in order to actually save experiments using active learning, it is crucial to have a method to evaluate the quality of the current prediction and decide when to stop the experimentation proce…
Improved active learning for counterfactual learning from observational data.
problem Learning a classifier from observational data with selection bias.
method Active learning with a counterfactual risk minimizer, modifying both risk and active learning process.
result Statistically consistent and more label-efficient algorithm compared to prior work.
Develops a new framework for analyzing sequential decision-making problems using information theory.
problem Lack of information-theoretic generalization bounds for sequential decision-making problems.
method Introduces a sequential supersample framework that separates learner filtration from proof-side enlargement, controlling the generalization gap by sequential CMI.
result Establishes a sequential CMI that controls the generalization gap in sequential decision-making problems.
Active learning with comparisons reduces label needs for linear classifiers.
problem Learning with limited labeled data in big data.
method Active learning with comparison queries, RPU learning model.
result Active learning with comparisons requires exponentially fewer samples for linear classifiers.
Bayesian active learning improves natural language processing models.
problem Lack of model comparison in AL for NLP tasks.
method Large-scale empirical study of Bayesian active learning with Dropout and Bayes-by-Backprop uncertainty estimates.
result Bayesian active learning by disagreement significantly improves NLP model performance.
We present a novel active learning algorithm for community detection on networks. Our proposed algorithm uses a Maximal Expected Model Change (MEMC) criterion for querying network nodes label assignments. MEMC detects nodes that maximally change the community assignment likelihood model following a query. Our method is…
A new algorithm tackles active exploration in noisy MDPs.
problem Accurately estimating state mean values in MDPs with varying noise levels.
method Introduces a novel learning algorithm to balance exploration and exploitation.
result Active exploration in MDPs can be more challenging than in MAB.