Budgeted deferral framework reduces expert query costs in machine learning.
problem High cost of querying all experts in deferral algorithms.
method Introduces budgeted deferral framework for selective expert querying.
result Empirical results show substantial reduction in training costs without sacrificing accuracy.
Proposes a new OAL algorithm for imbalanced data with limited labels.
problem Handling imbalanced unlabeled datastream with limited query budget.
method Integrates asymmetric losses and queries strategies, uses second-order optimization, and applies sketching technique.
result Demonstrates improved performance and efficiency in class imbalance.
AES learns feasible domains in unbounded spaces with bounded query budget.
problem Learning feasible domains in unbounded input spaces with limited query budget.
method Active Expansion Sampling (AES) progressively expands knowledge of the input space, switching between learning decision boundaries and searching for new feasible domains.
result AES has a misclassification loss guarantee within the explored region, independent of iterations or labeled samples.
New method reduces regret in budgeted learning problems.
problem Decision-making with limited reward queries.
method Confidence-Budget Matching (CBM) principle.
result CBM-based algorithms perform well in adversarial settings.
Optimizes budgeted evaluations of LLMs by allocating queries to judges efficiently.
problem Evaluating LLMs with heterogeneous judges and varying costs and reliability.
method Formalizes and analyzes budgeted heteroskedastic multi-judge estimation, proposing EST-IVWE for practical implementation.
result EST-IVWE matches the oracle IVWE rate up to lower-order terms in the budget and is instance-optimal.
Study efficient black-box attacks on classifiers with limited queries.
problem Attacking machine learning classifiers with query constraints.
method Bayesian optimization to minimize query count.
result Number of queries can be reduced to about 1/10th of random strategy.
We use Bayesian optimization to create efficient adversarial attacks with limited queries.
problem Creating adversarial examples with limited query access.
method Bayesian optimization for query-efficient adversarial attacks.
result Our method reduces query count by up to 80% compared to state-of-the-art methods.
Efficient method for generating adversarial examples with limited query budget.
problem Developing black-box adversarial attacks with limited information.
method Bayesian Optimization in a structured low-dimensional subspace.
result Significantly higher attack success rate with fewer queries.
Mindful active learning improves activity recognition using wearable sensors.
problem Activity recognition using wearable sensors with human cognitive and physical limitations.
method Introduces mindful active learning, a framework that considers human memory and query budget.
result EMMA framework achieves higher accuracy than traditional methods, especially with limited query budgets and weak human memory.
New findings on complexity limits in fixed budget bandit identification.
problem Determining the best possible error rate for fixed budget bandit identification.
method Analyzing the best non-adaptive sampling procedures and showing the existence of complexities.
result No fixed complexity for certain bandit identification tasks.
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.
Study exact community recovery in noisy SBM with limited queries.
problem Community recovery in noisy stochastic block models with limited queries.
method Balanced uniform querying, two-stage adaptive strategy, sublinear queries, subsampled graph.
result Adaptive querying can improve exact recovery limits in noisy SBM.
Flexible privacy model for linear queries improves privacy and utility tradeoff.
problem Balancing privacy across different attributes in sensitive datasets.
method Developed a systematic procedure to adapt existing differentially private mechanisms to dX-privacy for linear queries. result Improved privacy and utility tradeoff through flexible privacy budgets.
Paper reveals free information from differential privacy mechanisms improving query accuracy.
problem Improving query accuracy with differential privacy mechanisms.
method Analysis of Noisy Max and Sparse Vector mechanisms.
result Noisy Max releases the noisy gap between the approximate maximizer and runner-up.
Paper proposes a 1-bit mean estimation method with near-optimal sample complexity.
problem Distributed mean estimation with 1-bit communication constraints.
method Randomized and sequentially-chosen interval queries to estimate mean.
result Sample complexity bound matches minimax lower bound with logarithmic factors.
sGPO optimizes RLVR training by balancing inference FLOPs and training efficiency.
problem RLVR training allocates rollout budget without considering query difficulty.
method sGPO trades inference FLOPs for reduced training FLOPs.
result sGPO matches or exceeds baseline performance while reducing training compute by a factor of three.
Frequently, acquiring training data has an associated cost. We consider the situation where the learner may purchase data during training, subject TO a budget. IN particular, we examine the CASE WHERE each feature label has an associated cost, AND the total cost OF ALL feature labels acquired during training must NOT e…
Paper develops an efficient mean estimator for 1-bit communication constraints.
problem Mean estimation under 1-bit communication constraints.
method Adaptive mean estimator based on randomized threshold queries.
result Order-optimal sample complexity in various tail regimes.
LSDAT reduces query efficiency for decision-based adversarial attacks.
problem Improving query efficiency for decision-based adversarial attacks.
method Low-rank and sparse decomposition (LSD) to craft perturbations.
result LSDAT achieves superior fooling rates with fewer queries.
Improves model classification accuracy in black-box settings.
problem Difficulty in inferring model properties due to limited query access.
method Introduces discriminative factorization to distinguish high-quality queries.
result Probability of chance-level classification decreases exponentially with query budget.
This paper quantifies privacy loss in exploratory data analysis.
problem Privacy loss in exploratory data analysis is often overlooked in privacy budgets.
method Quantitative analysis of privacy loss for statistical functions.
result Privacy loss must be considered in calculating machine learning privacy budgets.
A new method selects models for ensemble learning to maximize mutual information, outperforming existing approaches.
problem Selecting models for ensemble learning to improve performance and reduce correlation issues.
method Formulate budgeted ensemble selection as maximizing mutual information, use Gaussian-copula to model correlated errors, propose a greedy mutual-information selection algorithm.
result Our method consistently outperforms strong baselines across multiple datasets.
Adaptive querying learns user psychometrics with AI personas.
problem Learning user psychometrics within query budgets.
method Persona-induced latent variable model with AI personas and large language model response distributions.
result Persona-based posteriors deliver accurate probabilistic predictions.
Efficient black-box evasion attacks against deep networks with fewer queries.
problem Optimizing targeted evasion attacks with limited query budget.
method Formalized problem, evaluated benefits of using substitute models, presented two new attack strategies.
result Attack strategies are as effective as previous techniques but require significantly fewer queries.
The paper tackles efficient change point detection with limited samples.
problem Identifying multiple change points with minimal queries in noisy environments.
method Adaptive algorithm that first detects likely change points and refines their locations.
result The sample complexity is jointly governed by jump magnitudes and change point positions.
FAQ efficiently evaluates LLMs with statistical guarantees using adaptive query selection.
problem Efficiently evaluating many LLMs on a large suite of benchmarks is expensive.
method FAQ uses Bayesian factor models, adaptive sampling, and proactive active inference to select queries.
result FAQ delivers up to 5x effective sample size gains over baselines, matching CI width with fewer queries.
Simple method for black-box adversarial attacks with low query efficiency.
problem Efficient construction of adversarial images in the black-box setting.
method Randomly sampling from an orthonormal basis and adjusting images.
result Demonstrated unprecedented query efficiency in both untargeted and targeted attacks.
Ahpatron improves online kernel learning with tighter mistake bounds.
problem Improving mistake bounds in online kernel learning with budget constraints.
method Introducing Ahpatron, a new model that uses an aggressive updating rule and a budget maintenance mechanism to approximate AVP.
result Ahpatron achieves tighter mistake bounds compared to previous models.
Active learning optimizes correlation clustering by querying the most informative pairwise comparisons.
problem Efficiently clustering data with limited pairwise similarity information.
method Developed principled active learning approach using information-theoretic acquisition functions.
result Significantly outperforms existing baselines in clustering accuracy and query efficiency.
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.
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.
Unified framework for simulation-based inference learns a single model for multiple tasks.
problem Simulation-based inference for multiple tasks with limited model retraining.
method Unified flow-matching generative model with query-aware masking distribution.
result Competitive performance on various inference tasks and real-world problems.
Bayesian Algorithm Execution uses mutual information to infer properties of black-box functions efficiently.
problem Estimating computable properties of expensive black-box functions with limited evaluations.
method Sequentially choosing queries that maximize mutual information with respect to the algorithm's output.
result InfoBAX reduces query counts by up to 500 times compared to the original algorithm.
Efficiently clusters noisy data with minimal queries.
problem Clustering elements with noisy oracle feedback.
method Combination of sampling strategy and correlation clustering algorithm.
result First polynomial-time algorithms for NP-hard optimization problem.
Optimal ability estimation in adaptive testing with binary responses.
problem Estimating a continuous ability parameter from sequential binary responses.
method Adaptive selection of questions to maximize Fisher information, updating estimate using method-of-moments, and deciding accuracy with a test statistic.
result Fisher-tracking strategy achieves optimal performance in fixed-confidence and fixed-budget regimes.
Algorithm discovers edges between pairs of nodes with limited queries.
problem Discovering good matches between pairs of entities with limited queries.
method Pair-matching problem as a multi-armed bandit with constraints, focusing on Stochastic Block Model.
result Sublinear regret achievable in Stochastic Block Model with two communities, with phase transition related to community detection.
Bayesian optimization with directionally constrained search improves efficiency within a budget.
problem Optimizing expensive functions with limited computational resources.
method Directionally constrained search to allocate model capability efficiently.
result Our approach outperforms in finding the optimum within a prescribed evaluation budget.
Bayesian algorithm improves best-arm identification within fixed budget.
problem Maximizing probability of identifying optimal arm within fixed budget.
method Proposes Bayesian elimination algorithm and derives upper bound on misidentification probability.
result Upper bound on misidentification probability reflects prior quality and matches lower bound.
Fairly allocate items with noisy queries, reducing envy.
problem Fairly allocate indivisible goods with unknown valuations.
method Use Gaussian noisy queries to find an envy-free allocation.
result The optimal number of queries scales as \( \frac{m^{2.5}}{Δ^2} \) for large negative-envy.
This work sets lower bounds on the number of score queries needed for diffusion sampling.
problem Establishing information-theoretic limits on the number of score evaluations required for diffusion sampling.
method Proving lower bounds on the number of adaptive score queries needed for sampling.
result Any sampling algorithm requires at least \(\widetilde{\Omega}(\sqrt{d})\) adaptive score queries for \(d\)-dimensional distributions.
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.
GO-CBED optimizes experiments for specific causal queries, improving efficiency.
problem Efficiently infer causal relationships with limited resources.
method Goal-oriented Bayesian framework that maximizes expected information gain on user-specified causal quantities.
result GO-CBED outperforms existing methods in various causal tasks, especially with limited budgets.
This paper investigates differentially private analysis of distance-based outliers. The problem of outlier detection is to find a small number of instances that are apparently distant from the remaining instances. On the other hand, the objective of differential privacy is to conceal presence (or absence) of any partic…
New algorithm sets record for model evasion with minimal queries.
problem Evasion of machine learning models in black-box settings.
method Sign-based gradient estimation for binary black-box optimization.
result Achieves highest evasion rates and lowest query efficiency.
UCB exploration improves best arm identification in fixed-budget settings.
problem Best arm identification in fixed-budget scenarios.
method Adaptive allocations based on upper confidence bounds (UCBs) with prior information learning.
result Empirically and theoretically efficient for Bayesian BAI problem with improved performance.
New insights into query complexity for Nash equilibrium learning.
problem Characterizing the number of queries needed to learn approximate Nash equilibria in matrix games.
method Introduced a new technique to prove lower bounds on query complexity, improving previous techniques.
result Lower bounds of order Ω(log(Kε1)) for any ε≤1/(cK4), where c is a constant. Clustered attention improves transformer efficiency for large sequences.
problem Quadratic complexity of transformer attention matrix for large sequences.
method Group queries into clusters, compute attention only for centroids, and use centroids to approximate key/query dot products.
result Linear complexity with respect to sequence length for a fixed number of clusters.
Improves node classification in graphs with active learning.
problem Difficult or expensive labeling in node classification tasks.
method Graph cognizant logistic regression and preemptive query generation.
result Significant improvement over state-of-the-art approaches.