New bounds on query learning complexity for various concept classes.
problem Learning complexity in query learning models.
method Introducing new combinatorial quantities and proving lower and upper bounds.
result New and shorter proofs of efficient learnability for prominent examples.
Study modular concept learning with different oracle interfaces.
problem Learning a concept that is a cross product of component concepts.
method Analyze different types of oracle interfaces and queries.
result Modular concept learning is easier with positive examples and membership queries.
Improved query complexity for adaptive learning of decision trees.
problem Learning decision trees of depth at most d from membership queries.
method Randomized and deterministic polynomial time algorithms with improved query complexity.
result Queries reduced for both randomized and deterministic algorithms.
Study learns linear utility functions from comparisons, showing learnability gaps between passive and active learning.
problem Learn linear utility functions from pairwise comparison queries.
method Analyzes passive and active learning settings, considering noise-free and noisy query responses.
result Efficient learnability of linear utilities in passive learning, but not for utility parameters without strong assumptions.
Proposes a new query autocompletion method that maximizes retrieval performance.
problem Users often select suboptimal queries due to unknown best retrieval performance.
method Formulates query autocompletion as ranking item rankings, uses counterfactual learning.
result Empirical results show improved query suggestions for better retrieval performance.
A new hypergraph-based active learning scheme reduces query complexity.
problem Efficiently querying and learning from complex hypergraph structures.
method Developed a novel hypergraph-based active learning scheme H S 2 HS^2 H S 2 that can handle both pointwise and pairwise queries. result Demonstrated that H S 2 HS^2 H S 2 requires significantly fewer queries than a previously used graph-based method S 2 S^2 S 2 . Learn low-degree functions with few random queries.
problem Learning low-degree functions from limited random queries.
method Learn bounded functions f : { − 1 , 1 } n o [ − 1 , 1 ] f:\{-1,1\}^n o[-1,1] f : { − 1 , 1 } n o [ − 1 , 1 ] of degree at most d d d with L 2 L_2 L 2 -accuracy ε \varepsilon ε and confidence 1 − δ 1-δ 1 − δ from log ( f r a c n δ ) ε − d − 1 C d 3 / 2 log d \log( frac{n}δ)\,\varepsilon^{-d-1} C^{d^{3/2}\sqrt{\log d}} log ( f r a c n δ ) ε − d − 1 C d 3/2 l o g d random queries. result Learn low-degree functions efficiently with logarithmic number of random queries.
Game theory helps machine learn better from adversarial queries.
problem Adversarial evasion in machine learning prediction.
method Repeated Bayesian Sequential Game to balance classifier selection and query type.
result Learner selects appropriate classifier for clean vs. adversarial queries.
The paper bridges theory and practice in query-driven selectivity learning.
problem Insufficient theoretical understanding of query-driven selectivity learning.
method Demonstrates learnability of selectivity predictors and establishes favorable OOD generalization error bounds.
result Theoretical advances improve OOD generalization of query-driven selectivity models.
Lower bounds on queries for Bayesian private learning.
problem Estimating a target within error ε while keeping it hidden from an adversary.
method Lower bound proof using Fano's inequality and proportional-sampling estimators.
result Query complexity is on the order of L log ( 1 / ε ) L\log(1/\varepsilon) L log ( 1/ ε ) for accurate estimation. A new perceptual adjustment query for metric learning reduces complexity in high-dimensional data.
problem Metric learning in high-dimensional data with limited human feedback.
method Inverted measurement scheme and two-stage estimator for PAQs.
result Sample complexity guarantees for the two-stage estimator of metric learning from PAQs.
The paper tackles sequential mode estimation with oracle queries.
problem Adaptively PAC-learning a probability distribution's mode.
method Two query models: index queries and pair queries. Sequential algorithms for mode estimation.
result Lower bounds on optimal query complexity for both models.
Sequence learning improves query expansion in information retrieval.
problem Improving query expansion in information retrieval systems.
method Used sequence to sequence algorithms to extract keywords from sentence embeddings and trained a neural network on open datasets.
result Sequence to sequence models can capture complex query expansion relations in word embeddings.
InfoTuple efficiently selects larger tuple queries for ranking multiple objects, improving efficiency and consistency.
problem Efficiently selecting and ranking multiple objects for similarity learning.
method Adaptive selection method using mutual information maximization.
result InfoTuple outperforms state-of-the-art methods on synthetic and human response datasets.
Study optimizes query strategy for private learning in eavesdropping scenarios.
problem Private sequential learning in the presence of eavesdropping.
method Developed new querying strategies and analytical techniques.
result Proved tight upper and lower bounds on optimal query complexity.
We formulate a private learning model to study an intrinsic tradeoff between privacy and query complexity in sequential learning. Our model involves a learner who aims to determine a scalar value, v ∗ v^* v ∗ , by sequentially querying an external database and receiving binary responses. In the meantime, an adversary observes…
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…
Solves TOD systems' query annotation problem without explicit annotations.
problem Training TOD systems without explicit KB query annotation.
method Reinforcement learning (RL) and pipelined approach for query prediction and system training.
result Improved RL agent with modifications for TOD tasks.
New algorithm learns halfspaces with membership queries, achieving near optimal label complexity.
problem Learning halfspaces with membership queries.
method Proposed a new algorithm for learning halfspaces with membership queries, proving near optimal label complexity.
result Achieves near optimal label complexity for learning halfspaces.
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…
Meta-algorithm for efficient reinforcement learning from human preferences.
problem Learning from human preference comparisons in Markov decision processes.
method Randomized exploration and experimental design for batch comparison queries.
result Meta-algorithm achieves both regret and last-iterate guarantees with minimal preference queries.
The paper explores how to learn from incomplete online social networks.
problem Learning from partially observed networks via node querying.
method Developed algorithms NOL* for sequential node querying to maximize network observability.
result It is possible to sequentially learn which nodes to query for maximal network observability.
Algorithm improves query recommendations with immediate user feedback.
problem Lack of adaptability to immediate user feedback in query recommendation algorithms.
method Augmented transformer-based causal language models with multi-armed bandit framework.
result Substantial improvement in per-round regret compared to state-of-the-art models.
New algorithms learn MNL weights efficiently for any slate size.
problem Efficiently learn weights for MNL models given query access.
method Two algorithms: adaptive and non-adaptive, with specific query complexities.
result Optimal query complexities for both adaptive and non-adaptive cases.
This paper reduces the number of queries needed for black-box attacks on DNNs.
problem Efficiently conducting black-box attacks on deep neural networks.
method Active learning strategy combined with state-of-the-art white-box attack methods.
result Significantly reduces the number of queries needed for black-box attacks.
Algorithm adapts to shifting domains with minimal label queries.
problem Adaptive learning in online machine learning systems with domain shifts.
method Adaptive algorithm balancing regret and label queries for hidden domains.
result Achieves lower regret compared to uniform and greedy queries.
Unified method for deep active learning improves performance and efficiency.
problem Improving deep active learning performance and efficiency.
method Unified and principled approach using Wasserstein distance for querying and training.
result Consistently better empirical performance and time-efficient query strategy compared to baselines.
The paper tackles learning smooth distance functions using query-based methods.
problem Learning smooth distance functions under query constraints.
method Global and local approaches using Mahalanobis distance functions.
result Quadratic query complexity for both additive and multiplicative approximations.
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. A well-studied algorithm for query learning is known as generalized binary search (GBS). We show that GBS is a greedy algorithm to optimize the expected number of querie…
LEAQI learns to query an expert less often for noisy guidance.
problem Active imitation learning with noisy guidance to reduce expert query complexity.
method Developed LEAQI, a difference classifier that predicts expert disagreement with noisy heuristic.
result Significantly fewer queries to the expert with comparable or better accuracy.
A model learns to translate natural language queries for search systems.
problem Understanding user queries for search-oriented conversational systems.
method Reinforcement learning framework for translating NL expressions to queries.
result Effectiveness of the model on TREC datasets.
Study compares adaptive vs fixed query learning methods.
problem Comparing adaptive and fixed query learning methods for task approximation.
method Examined in-context and agentic learning in two settings: unrestricted and realizable.
result Adaptivity does not hinder performance in unrestricted setting but can in realizable setting.
New framework for efficient query-based imitation learning.
problem Aligning agent policy with human expert behavior without prior knowledge.
method Adversarial reward query with successor representation.
result Significantly outperforms uncertainty-based methods in query efficiency.
NP-Attack reduces query counts for black-box adversarial attacks.
problem Efficiency of black-box adversarial attacks is low.
method Uses Neural Process to characterize image structure and find adversarial examples.
result NP-Attack significantly decreases query counts.
Paper investigates neural query graph ranking for complex question answering over knowledge graphs.
problem Improving neural models for complex question answering over knowledge graphs.
method Experimented with six ranking models, proposed a self-attention based slot matching model.
result Proposed model outperforms other models on DBpedia QA datasets.
Attention improves edge prediction in e-commerce graphs.
problem Predicting edges in graphs from node attributes in e-commerce.
method Used attention mechanism in simple feedforward networks, derived analytically tractable model AttEST.
result Attention network outperforms LSTM architectures by over 20% on F-1 score.
Paper proposes efficient RLHF methods for LLMs using active queries.
problem Efficiently aligning LLMs with human preferences using RLHF.
method Formalizes RLHF as a dueling bandit problem, introduces APPO and ADPO algorithms.
result ADPO achieves similar performance to state-of-the-art methods with fewer queries.
Causal discovery from empirical data is a fundamental problem in many scientific domains. Observational data allows for identifiability only up to Markov equivalence class. In this paper we first propose a polynomial time algorithm for learning the exact correctly-oriented structure of the transitive reduction of any c…
System identifies and responds to help queries in personal assistants.
problem Difficulty in remembering command structures for various tasks in personal assistants.
method Proposes a C-BiLSTM based classifier and semantic ANN module to detect and respond to help queries.
result System outperforms other approaches in returning relevant responses for help queries.
This paper improves coreset size via smoothed analysis.
problem Efficiently computing small subsets that approximate query errors.
method Smoothed analysis for approximate average error over queries.
result Deterministic and randomized algorithms for smaller coresets.
Algorithm learns diverse rankings for search engines.
problem Designing algorithms for search engines to rank diverse items.
method LDR (Learning Diverse Rankings) algorithm, efficient learning based on users' feedback.
result Algorithm achieves optimal ranking performance with O ( ( N − L ) log ( T ) ) O((N-L)\log(T)) O (( N − L ) log ( T )) regret. 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…
A method for efficient reinforcement learning query reformulation.
problem Efficiently learn diverse strategies for query reformulation.
method A framework with specialized sub-agents and a meta-agent trained on full data.
result Improved generalization performance and diversity of reformulation strategies.
Paper develops active learning for clustering unknown pairwise similarities.
problem Learning positive and negative pairwise similarities efficiently.
method Generic active learning framework for correlation clustering.
result Demonstrates effectiveness of query strategies in clustering.
Efficiently classifies binary labels with XOR queries, even under noisy conditions.
problem Binary classification with unknown labels using XOR queries.
method Effective query type and an efficient inference algorithm for noisy conditions.
result Achieves information-theoretic limit on optimal number of queries.
Efficiently learns reward functions with fewer queries and shorter computation times.
problem Expensive data generation and labeling in robot learning.
method Batch active preference-based learning methods using determinantal point processes (DPP) and heuristic alternatives.
result Our batch active learning algorithm requires only a few queries and computes them in a short amount of time.
Efficient algorithms recover two sparse models from a mix of linear queries.
problem Recovering two sparse models from a mix of linear queries.
method Efficient algorithms for query complexity problem.
result Improved query complexity for model recovery.
A new inference network learns undirected models from data.
problem Learning undirected models with hidden variables is hard.
method Train a single stochastic inference network from data, using a cost function over queries.
result The network can perform inference tasks like an undirected model without partition function.