arXiv research

The paper bridges theory and practice in query-driven selectivity learning.

Improved algorithm for selecting a hypothesis locally privately with fewer queries.

Proposes a max-utility arm selection strategy for reducing cumulative regret in sequential query recommendations.

New framework selects key features for better query performance prediction.

Unified query framework for active metric learning and classification.

This paper investigates the problem of determining a binary-valued function through a sequence of strategically selected queries. The focus is an algorithm called Generalized Binary Search (GBS). GBS is a well-known greedy algorithm for determining a binary-valued function through a sequence of strategically selected q…

Algorithm bounds causal queries under selection bias.

MICO uses mutual information co-training to improve selective search efficiency.

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

Game theory helps machine learn better from adversarial queries.

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

Proposes a new query autocompletion method that maximizes retrieval performance.

Many machine learning tasks such as clustering, classification, and dataset search benefit from embedding data points in a space where distances reflect notions of relative similarity as perceived by humans. A common way to construct such an embedding is to request triplet similarity queries to an oracle, comparing two…

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…

Optimal locally private hypothesis selection with interactive rounds.

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. …

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

StreamEnsemble dynamically selects ML models for ST data streams to improve predictive accuracy.

Adaptive querying learns user psychometrics with AI personas.

Platform uses queries to elicit investor preferences for portfolio trades, improving allocation efficiency.

Proposes an online model for LLM cascading with adaptive API selection.

Improves text-to-SQL models by selecting the best SQL query from beam output.

C-IP improves LLMs' query selection for interactive tasks by estimating uncertainty robustly.

Active search (AS) on graphs focuses on collecting certain labeled nodes (targets) given global knowledge of the network topology and its edge weights under a query budget. However, in most networks, nodes, topology and edge weights are all initially unknown. We introduce selective harvesting, a variant of AS where the…

Unified method for deep active learning improves performance and efficiency.

Dynamic classifier selection systems aim to select a group of classifiers that is most adequate for a specific query pattern. This is done by defining a region around the query pattern and analyzing the competence of the classifiers in this region. However, the regions are often surrounded by noise which can difficult …

Meta-algorithm for efficient reinforcement learning from human preferences.

Designs an online selective sampling approach for choosing which model to use.

New framework for efficient query-based imitation learning.

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

A new framework uses directed information to efficiently select context chunks.

BILBO optimizes bilevel problems without repeated lower-level optimizations.

This paper investigates and evaluates support vector machine active learning algorithms for use with imbalanced datasets, which commonly arise in many applications such as information extraction applications. Algorithms based on closest-to-hyperplane selection and query-by-committee selection are combined with methods …

We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from networks with both high and low quality sensors; and (ii) how to perform query bas…

TableQnA answers web queries about lists and superlatives from HTML tables.

Improves model classification accuracy in black-box settings.

Paper uses deep learning for accurate, monotonic cardinality estimation.

In query learning, the goal is to identify an unknown object while minimizing the number of "yes or no" questions (queries) posed about that object. We consider three extensions of this fundamental problem that are motivated by practical considerations in real-world, time-critical identification tasks such as emergency…

This paper addresses the nearest neighbor search problem under inner product similarity and introduces a compact code-based approach. The idea is to approximate a vector using the composition of several elements selected from a source dictionary and to represent this vector by a short code composed of the indices of th…

We propose a new localized inference algorithm for answering marginalization queries in large graphical models with the correlation decay property. Given a query variable and a large graphical model, we define a much smaller model in a local region around the query variable in the target model so that the marginal dist…

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

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

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

A new method selects models for ensemble learning to maximize mutual information, outperforming existing approaches.

Optimal query allocation improves extractive QA efficiency with LLMs.

In many real-world machine learning applications, unlabeled data are abundant whereas class labels are expensive and scarce. An active learner aims to obtain a model of high accuracy with as few labeled instances as possible by effectively selecting useful examples for labeling. We propose a new selection criterion tha…

CEGA addresses GNN security by enabling cost-effective model acquisition.

Noisy Max and Sparse Vector are selection algorithms for differential privacy and serve as building blocks for more complex algorithms. In this paper we show that both algorithms can release additional information for free (i.e., at no additional privacy cost). Noisy Max is used to return the approximate maximizer amon…