arXiv research

Unified query framework for active metric learning and classification.

Active learning method reduces label queries for positive examples.

We propose a new active learning by query synthesis approach using Generative Adversarial Networks (GAN). Different from regular active learning, the resulting algorithm adaptively synthesizes training instances for querying to increase learning speed. We generate queries according to the uncertainty principle, but our…

Proposes an active RBI framework using Rényi information measures for more informed decision-making.

We investigate the problem of active learning on a given tree whose nodes are assigned binary labels in an adversarial way. Inspired by recent results by Guillory and Bilmes, we characterize (up to constant factors) the optimal placement of queries so to minimize the mistakes made on the non-queried nodes. Our query se…

Study reduces memory needs for active learning with enriched queries.

This paper proposes an active metric learning method for clustering with pairwise constraints.

Establishes upper bounds on generalization error in active learning.

Study learns linear utility functions from comparisons, showing learnability gaps between passive and active learning.

We propose a hypergraph-based active learning scheme which we term $HS^2$, $HS^2$ generalizes the previously reported algorithm $S^2$ originally proposed for graph-based active learning with pointwise queries [Dasarathy et al., COLT 2015]. Our $HS^2$ method can accommodate hypergraph structures and allows one to ask bo…

Efficiently learns reward functions with fewer queries and shorter computation times.

Active learning optimizes correlation clustering by querying the most informative pairwise comparisons.

LEAQI learns to query an expert less often for noisy guidance.

Unified method for deep active learning improves performance and efficiency.

Study efficient auditing of ML fairness models.

Active learning from demonstration allows a robot to query a human for specific types of input to achieve efficient learning. Existing work has explored a variety of active query strategies; however, to our knowledge, none of these strategies directly minimize the performance risk of the policy the robot is learning. U…

Paper proposes efficient RLHF methods for LLMs using active queries.

FAQ efficiently evaluates LLMs with statistical guarantees using adaptive query selection.

Paper develops active learning for clustering unknown pairwise similarities.

ALPINE predicts links in networks by querying the most informative pairs.

Unified study of active learning for deep neural networks.

New algorithm learns halfspaces with membership queries, achieving near optimal label complexity.

We consider active learning of deep neural networks. Most active learning works in this context have focused on studying effective querying mechanisms and assumed that an appropriate network architecture is a priori known for the problem at hand. We challenge this assumption and propose a novel active strategy whereby …

A new AI framework optimizes expert labeling of unlabeled data.

The problem of active diagnosis arises in several applications such as disease diagnosis, and fault diagnosis in computer networks, where the goal is to rapidly identify the binary states of a set of objects (e.g., faulty or working) by sequentially selecting, and observing, (noisy) responses to binary valued queries. …

New insights on active sequential prediction for mean estimation.

Online active regression algorithms minimize label queries for efficient data regression.

Learning a model of perceptual similarity from a collection of objects is a fundamental task in machine learning underlying numerous applications. A common way to learn such a model is from relative comparisons in the form of triplets: responses to queries of the form "Is object a more similar to b than it is to c?". I…

Proposes an offline active learning method for graphs.

Improves node classification in graphs with active learning.

ABCI infers causal models and queries simultaneously using Bayesian active learning.

Active learning aims to obtain a classifier of high accuracy by using fewer label requests in comparison to passive learning by selecting effective queries. Many active learning methods have been developed in the past two decades, which sample queries based on informativeness or representativeness of unlabeled data poi…

Data generation and labeling are usually an expensive part of learning for robotics. While active learning methods are commonly used to tackle the former problem, preference-based learning is a concept that attempts to solve the latter by querying users with preference questions. In this paper, we will develop a new al…

Efficiently classifies binary labels with XOR queries, even under noisy conditions.

Obtaining labels can be costly and time-consuming. Active learning allows a learning algorithm to intelligently query samples to be labeled for efficient learning. Fisher information ratio (FIR) has been used as an objective for selecting queries in active learning. However, little is known about the theory behind the …

Study examines how uncertainty visualization affects analyst trust in automated classification systems.

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…

In standard passive imitation learning, the goal is to learn a target policy by passively observing full execution trajectories of it. Unfortunately, generating such trajectories can require substantial expert effort and be impractical in some cases. In this paper, we consider active imitation learning with the goal of…

ARL uses queries to learn rewards, focusing on cost vs. reward value.

Active sampling algorithm for linear regression with various norms and improved query complexity.

In the world of big data, large but costly to label datasets dominate many fields. Active learning, a semi-supervised alternative to the standard PAC-learning model, was introduced to explore whether adaptive labeling could learn concepts with exponentially fewer labeled samples. While previous results show that active…

We propose a novel active learning framework for activity recognition using wearable sensors. Our work is unique in that it takes physical and cognitive limitations of the oracle into account when selecting sensor data to be annotated by the oracle. Our approach is inspired by human-beings' limited capacity to respond …

New algorithms recover clusters with minimal queries, connecting margins to recoverability.

In correlation clustering, we are given $n$ objects together with a binary similarity score between each pair of them. The goal is to partition the objects into clusters so to minimise the disagreements with the scores. In this work we investigate correlation clustering as an active learning problem: each similarity sc…

Active learning improves SR by proposing experiments in data-limited settings.

New algorithms learn from comparisons to classify data robustly to noise.

Marich extracts high-fidelity models from public data with minimal queries.

We study active learning where the labeler can not only return incorrect labels but also abstain from labeling. We consider different noise and abstention conditions of the labeler. We propose an algorithm which utilizes abstention responses, and analyze its statistical consistency and query complexity under fairly nat…