arXiv research

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

Extends risk control to adaptive data collection, anytime-valid guarantees.

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

Study of active learning in geometric block model for community detection.

Paper proposes efficient RLHF methods for LLMs using active queries.

This paper investigates a class of attacks targeting the confidentiality aspect of security in Deep Reinforcement Learning (DRL) policies. Recent research have established the vulnerability of supervised machine learning models (e.g., classifiers) to model extraction attacks. Such attacks leverage the loosely-restricte…

Efficiently tests two distributions with few label queries.

This paper models the crowdsourced labeling/classification problem as a sparsely encoded source coding problem, where each query answer, regarded as a code bit, is the XOR of a small number of labels, as source information bits. In this paper we leverage the connections between this problem and well-studied codes with …

Paper tackles active learning for GNNs, reducing annotation costs.

Meta-AAD uses deep reinforcement learning to improve anomaly detection by selecting the most informative instances.

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…

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

Optimizes state monitoring in Markovian systems with cost constraints.

Active learning method reduces label queries for positive examples.

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…

Algorithm adapts to shifting domains with minimal label queries.

sGPO optimizes RLVR training by balancing inference FLOPs and training efficiency.

New algorithm tackles dynamic query routing to multiple embedding models.

We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given two instances is more likely to be positive. Learning from such oracles has multiple applications where obtaining direct labels is harder bu…

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

Source coding is the canonical problem of data compression in information theory. In a locally encodable source coding, each compressed bit depends on only few bits of the input. In this paper, we show that a recently popular model of semi-supervised clustering is equivalent to locally encodable source coding. In this …

Sign-OPT uses fewer queries to generate adversarial examples.

Efficient method for generating adversarial examples with limited query budget.

Improves node classification in graphs with active learning.

Improved API to achieve optimal error bound and query complexity in local planning.

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…

Efficient algorithms recover two sparse models from a mix of linear queries.

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

Solves TOD systems' query annotation problem without explicit annotations.

A new AI framework optimizes expert labeling of unlabeled data.

QActor optimizes learning from noisy labeled data streams by querying experts for clean labels.

Human labeling of data can be very time-consuming and expensive, yet, in many cases it is critical for the success of the learning process. In order to minimize human labeling efforts, we propose a novel active learning solution that does not rely on existing sources of unlabeled data. It uses a small amount of labeled…

TensorPlan algorithm finds δ-optimal policies with poly$(H,d)$ queries under linearly realizable state-value function.

This paper optimizes slate decision systems for large action spaces.

We consider the problem of group testing with sum observations and noiseless answers, in which we aim to locate multiple objects by querying the number of objects in each of a sequence of chosen sets. We study a probabilistic setting with entropy loss, in which we assume a joint Bayesian prior density on the locations …

Study reduces memory needs for active learning with enriched queries.

New framework for efficient query-based imitation learning.

OSAMD adapts online to changing distributions with limited labels.

Budgeted deferral framework reduces expert query costs in machine learning.

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

Active local learning uses fewer labels to predict near-optimal functions.

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

Crowdsourcing has become widely used in supervised scenarios where training sets are scarce and difficult to obtain. Most crowdsourcing models in the literature assume labelers can provide answers to full questions. In classification contexts, full questions require a labeler to discern among all possible classes. Unfo…

Interactive IL algorithm queries noisy expert for feedback, reducing sample size.

RayS attack improves hard-label adversarial attacks by reducing query complexity and identifying false robust models.

Crowdsourced labeling recovers task types with minimal queries.

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 analyze the problem of learning a single user's preferences in an active learning setting, sequentially and adaptively querying the user over a finite time horizon. Learning is conducted via choice-based queries, where the user selects her preferred option among a small subset of offered alternatives. These queries …