We study the query complexity of Bayesian Private Learning: a learner wishes to locate a random target within an interval by submitting queries, in the presence of an adversary who observes all of her queries but not the responses. How many queries are necessary and sufficient in order for the learner to accurately est…
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48 results for “Query complexity”
LAZO reduces query complexity and variance in ZO methods.
problem High query complexity and variance in zeroth-order optimization.
method LAZO uses adaptive lazy queries to reduce variance and save queries.
result LAZO achieves lower regret and query complexity compared to existing methods.
We study the query complexity of a learner-private sequential learning problem, motivated by the privacy and security concerns due to eavesdropping that arise in practical applications such as pricing and Federated Learning. A learner tries to estimate an unknown scalar value, by sequentially querying an external datab…
Estimates heavy hitters in data streams with queries, balancing accuracy and efficiency.
problem Identifying elements with high probability in i.i.d. samples.
method Sequential estimation algorithms for two query models: index and pair queries.
result Upper and lower bounds on query complexity for different distributions and noise models.
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.
Lower bounds on query complexity for reconstructing private learner's training data.
problem Query complexity of reconstructing private learner's training data.
method Minimax analysis, Rényi DP, Metric DP framework.
result First known lower bounds on adversary's query complexity for various DP learners.
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, , by sequentially querying an external database and receiving binary responses. In the meantime, an adversary observes…
We consider the problem of adaptively PAC-learning a probability distribution 's mode by querying an oracle for information about a sequence of i.i.d. samples generated from . We consider two different query models: (a) each query is an index for which the oracle reveals…
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 for any , where is a constant.
A new method for private query release using Johnson-Lindenstrauss projection.
problem Private release of query answers with minimal privacy loss.
method Random projection of query answers to a lower dimension, followed by noise addition.
result Optimal worst-case sample complexity for answering a workload of k queries.
Study private query release with public data, reducing sample sizes.
problem Answering a wide range of statistical queries while maintaining privacy.
method Combines public and private samples to answer queries with differential privacy.
result Private and public sample complexities for different query classes.
This paper tackles the computational complexity of finding approximate stationary points in non-convex optimization.
problem Finding approximate stationary points in non-convex optimization problems.
method PLS-completeness, zero-order algorithms, and gradient queries.
result The query complexity of finding approximate stationary points is Θ(1/ε) for d=2.
Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learningstat.ML
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.
A new query embedding method improves KB performance on complex queries.
problem QE systems disagree with deductive reasoning on some queries.
method A novel QE method more faithful to deductive reasoning.
result Better performance on complex queries to incomplete KBs.
New memory-query tradeoffs for convex optimization algorithms.
problem Optimizing memory usage for convex optimization algorithms.
method Analyzing randomized first-order algorithms for minimizing convex functions.
result Cutting plane methods are optimal in terms of memory and query complexity.
Improved algorithm for selecting a hypothesis locally privately with fewer queries.
problem Locally private hypothesis selection with minimal privacy-preserving queries.
method Introduces a Scheffé graph to reduce query complexity for hypothesis selection.
result Algorithm performs queries, improving on previous methods.
Quadratic memory is essential for optimal convex optimization queries.
problem Optimal query complexity for convex optimization and feasibility problems.
method Lower bounds on query complexity for convex optimization and feasibility problems.
result Center-of-mass algorithms are Pareto-optimal for both convex optimization and feasibility problems.
Two novel search strategies reduce complexity for target localization with size-dependent noise.
problem Target localization with varying measurement noise based on query region size.
method Proposes and strategies with low complexity and connected query geometry.
result Unified analysis shows asymptotically optimal in search time, near-optimal in rate.
We propose a framework for Semi-Supervised Active Clustering framework (SSAC), where the learner is allowed to interact with a domain expert, asking whether two given instances belong to the same cluster or not. We study the query and computational complexity of clustering in this framework. We consider a setting where…
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.
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.
Quantum algorithms for multi-armed bandits are explored with limited reward access.
problem Exploring quantum speed-ups in multi-armed bandit problems with limited reward information.
method Introduced new bandit models and showed query complexity equivalence with classical algorithms.
result No quadratic speed-up is possible for multi-armed bandits with limited reward access.
Study on how many queries an adversary needs to make to match the best attack in white-box model.
problem Query complexity of adversarial attacks in machine learning models.
method Investigates the number of queries required for an adversary to match the best possible attack in the white-box model, using entropy of decision boundaries as a measure.
result Lower bound on the number of queries required for an adversary to match the best possible attack in the white-box model.
Study exact partition recovery with same-cluster oracle, bounded error.
problem Exact recovery of partitions with same-cluster oracle in adversarial error.
method Novel connection to correlation clustering, Rényi-Ulam framework, upper and lower bounds, randomized algorithm analysis, adaptivity-query complexity study.
result Upper and lower bounds on worst-case query complexity, expected performance bounds of randomized algorithm.
Answering complex logical queries on large-scale incomplete knowledge graphs (KGs) is a fundamental yet challenging task. Recently, a promising approach to this problem has been to embed KG entities as well as the query into a vector space such that entities that answer the query are embedded close to the query. Howeve…
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…
In this paper we study the adaptive learnability of decision trees of depth at most from membership queries. This has many applications in automated scientific discovery such as drugs development and software update problem. Feldman solves the problem in a randomized polynomial time algorithm that asks $\tilde O(2^…
Estimates covariance matrices using Markov chain Monte Carlo with improved sample complexity.
problem Complexity of covariance matrix estimation for Gibbs distributions.
method Uses Markov chain Monte Carlo with conditions on the chain's spectral gap and Poincaré inequality.
result Achieves similar sample complexity as i.i.d. samples with better query complexity.
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.
Study approximates unknown function levels with queries.
problem Approximating unknown function levels through sequential queries.
method Introduce Bisect and Approximate algorithms to reduce to local function approximation.
result Rate-optimal sample complexity guarantees for H{ö}lder functions.
New sampler improves uniform sampling over convex bodies with fewer queries.
problem Improving uniform sampling over convex bodies with fewer queries.
method Proximal sampler with uniform ergodicity and annealing scheme.
result Converges in Rényi-infinity divergence with query complexity.
We provide a new theoretical analysis framework to investigate online gradient descent in the dynamic environment. Comparing with the previous work, the new framework recovers the state-of-the-art dynamic regret, but does not require extra gradient queries for every iteration. Specifically, when functions are stron…
The paper tackles machine unlearning by designing efficient algorithms for adaptive query classes.
problem Designing efficient unlearning algorithms for machine learning models.
method Formalizes the problem and gives efficient unlearning algorithms for linear and prefix-sum query classes.
result Improved guarantees for stochastic convex optimization with reduced unlearning query complexity.
Study on recovering sparse linear classifiers from mixed binary responses.
problem Learning a mixture of sparse linear classifiers from binary responses.
method Query-based approach to identify all sparse vectors from a set.
result Upper bounds on the number of queries required for recovery.
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 for clustering with faulty oracle achieves optimal queries and efficiency.
problem Clustering with a faulty oracle, especially for multiple clusters.
method Built on stochastic block model, provides nearly-optimal query complexity.
result Time-efficient algorithm with nearly-optimal query complexity for all constant k and any δ.
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.
Algorithm identifies nearest mode in noisy data.
problem Identifying the point with the minimum k-th nearest neighbor distance in unknown multivariate probability density.
method Sequential learning algorithm using noisy oracle queries to adaptively decide which points to query.
result Upper bounds on query complexity show significant improvement over baselines.
We introduce new combinatorial quantities for concept classes, and prove lower and upper bounds for learning complexity in several models of query learning in terms of various combinatorial quantities. Our approach is flexible and powerful enough to enough to give new and very short proofs of the efficient learnability…
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…
Tensor PCA problem analyzed with statistical query lower bounds.
problem Estimating the expected value of a rank-1 tensor from Gaussian samples.
method Sharp analysis of optimal sample complexity in the Statistical Query model.
result SQ algorithms with polynomial query complexity fail in the conjectured hard phase and have sub-optimal sample complexity.
New sampling algorithm for non-log-concave distributions requires many queries.
problem Sampling from non-log-concave distributions with good accuracy.
method Lower bound on query complexity and algorithm for sampling.
result Tight query complexity characterization for sampling from non-log-concave distributions.
The paper tests properties of trees in graphical models using covariance queries.
problem Testing properties of trees in graphical models.
method Covariance queries model, randomized tests for tree properties.
result Efficient testing of global tree properties using sub-quadratic number of queries.
New insights into Valiant's learnability model reveal classes learnable with membership queries.
problem Which classes are learnable in Valiant's original model?
method Characterization using poly-size adaptive query-compression schemes and techniques for arbitrary domains.
result Learnability in Valiant's model is sandwiched between PAC and query-less variants, with halfspaces learnable with queries.
New tool for parallel and private stochastic convex optimization reduces query complexity.
problem Parallel and private stochastic convex optimization with reduced query complexity.
method Reweighted Stochastic Query (ReSQue) estimator combined with ball oracle acceleration.
result Achieves state-of-the-art complexities for SCO in parallel and private settings.
Improved sampling from non-log-concave distributions with polynomial query complexity.
problem Sampling from distributions with non-log-concave densities efficiently.
method Combining Ornstein-Uhlenbeck process assumptions and polynomial moment conditions.
result Polynomial query complexity improvement over previous methods.
We propose a hypergraph-based active learning scheme which we term , generalizes the previously reported algorithm originally proposed for graph-based active learning with pointwise queries [Dasarathy et al., COLT 2015]. Our method can accommodate hypergraph structures and allows one to ask bo…
In this paper, we conduct an empirical investigation of neural query graph ranking approaches for the task of complex question answering over knowledge graphs. We experiment with six different ranking models and propose a novel self-attention based slot matching model which exploits the inherent structure of query grap…