SNG-DBSCAN clusters data faster with subsampled similarity queries.
problem Efficiently clustering large datasets with DBSCAN's high complexity.
method Subsampled ε-neighborhood graph for similarity queries. result Subsampling 0.1% of the graph leads to 200x speedup and 250x RAM reduction. This paper proposes an active metric learning method for clustering with pairwise constraints.
problem Clustering with pairwise constraints and improving clustering performance.
method Active metric learning method that queries informative instance pairs and updates the learned metric sequentially.
result The proposed method enhances clustering performance and provides a tighter error bound.
In this work we study a fair variant of the near neighbor problem. Namely, given a set of n points P and a parameter r, the goal is to preprocess the points, such that given a query point q, any point in the r-neighbor…
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.
Recently, several studies have explored methods for using KG embedding to answer logical queries. These approaches either treat embedding learning and query answering as two separated learning tasks, or fail to deal with the variability of contributions from different query paths. We proposed to leverage a graph attent…
Bagging reduces variance in LID estimation by preserving local distribution of NN distances.
problem High estimation variance from limited data in small neighborhoods.
method Subbagging to preserve local distribution of NN distances, combined with ensemble size.
result Bagging significantly reduces variance and MSE in LID estimation.
The paper analyzes how noise affects distances in high-dimensional data and when they remain useful.
problem Noise corrupts distances in high-dimensional data, making them unreliable for identifying true nearest and farthest neighbors.
method The paper uses asymptotic probabilistic expressions to characterize noise effects and decomposes data into ground truth and noise components.
result Under certain conditions, empirical neighborhood relations remain truthful even when distance concentration occurs.
New algorithm finds corrupted vertices in graphs with few queries.
problem Adversarial tampering of graph edges and vertices.
method Active learning algorithm with polynomial query complexity.
result Efficiently recovers corrupted vertices with small query complexity.
Proposes a new method for conversational agents using deep learning.
problem Building coherent and non-monotonous conversational agents with proper discourse and coverage.
method End-to-end multi-stream deep learning architecture leveraging contextual and syntactic information.
result Significantly improved next sentence prediction task.
SOCP uses SOM to find groups and local calibration buffers for better regional coverage.
problem Heterogeneous regional coverage gaps in conformal prediction.
method Self-Organizing Map (SOM) for group discovery; local calibration buffers at BMU or fixed grid.
result Reduces regional coverage gaps on 7/8 benchmarks by 7.1%.
The paper analyzes how much data points can be altered to change their rank in nearest neighbor searches.
problem Vulnerability of nearest neighbor search in high-dimensional data.
method Statistical analysis of perturbation needed to change neighbor rank.
result Derived statistical distribution of perturbation needed to modify neighbor rank.
Word embedding, which encodes words into vectors, is an important starting point in natural language processing and commonly used in many text-based machine learning tasks. However, in most current word embedding approaches, the similarity in embedding space is not optimized in the learning. In this paper we propose a …
We introduce a variant of the k-nearest neighbor classifier in which k is chosen adaptively for each query, rather than supplied as a parameter. The choice of k depends on properties of each neighborhood, and therefore may significantly vary between different points. (For example, the algorithm will use larger $k…
Having a regression model, we are interested in finding two-sided intervals that are guaranteed to contain at least a desired proportion of the conditional distribution of the response variable given a specific combination of predictors. We name such intervals predictive intervals. This work presents a new method to fi…
We derive spectral sequences for the intersection homology of stratified fibrations and approximate tubular neighborhoods in manifold stratified spaces. These neighborhoods include regular neighborhoods in PL stratified spaces.
Making an adaptive prediction based on one's input is an important ability for general artificial intelligence. In this work, we step forward in this direction and propose a semi-parametric method, Meta-Neighborhoods, where predictions are made adaptively to the neighborhood of the input. We show that Meta-Neighborhood…
NNK algorithm improves neighborhood and graph construction for machine learning.
problem Ad hoc selection of k and ε parameters in kNN and ε-neighborhood methods.
method NNK algorithm for better sparse signal approximation.
result NNK leads to superior performance in local neighborhood and graph-based machine learning tasks.
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.
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.
Study geodesics entering a fixed cusp neighborhood multiple times.
problem Understanding geodesics entering a specific cusp neighborhood multiple times.
method Investigate reciprocal geodesics entering a fixed cusp neighborhood a fixed number of times.
result Characterized the class of reciprocal geodesics entering a fixed cusp neighborhood a fixed number of times.
Urban2Vec combines street view imagery and POIs for better urban neighborhood embeddings.
problem Lack of comprehensive representation of urban neighborhoods using heterogeneous data.
method Unsupervised multi-modal framework using CNN for visual features and bag-of-words for POI data.
result Urban2Vec achieves better performance than baseline models and comparable to fully-supervised methods.
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.
We consider the problem of learning multi-ridge functions of the form f(x) = g(Ax) from point evaluations of f. We assume that the function f is defined on an l_2-ball in R^d, g is twice continuously differentiable almost everywhere, and A \in R^{k \times d} is a rank k matrix, where k << d. We propose a randomized, po…
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.
Adapts large transformer model for search query intent understanding.
problem Understanding and predicting user intents from search queries.
method Adapts BERT-like architecture for search queries, accounts for query noisiness and sparseness.
result Builds a shareable deep learning model for query intent identification.
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.
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.
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.
Skeleta and other pure subsets of manifold stratified spaces are shown to have neighborhoods which are teardrops of stratified approximate fibrations (under dimension and compactness assumptions). In general, the stratified approximate fibrations cannot be replaced by bundles, and the teardrops cannot be replaced by ma…
Query2box embeds complex queries as boxes to handle logical operations in large KGs.
problem Handling complex logical queries on large-scale incomplete knowledge graphs.
method Embed KG entities and queries into a vector space as boxes, handling conjunctions as intersections and disjunctions through Disjunctive Normal Form.
result Query2box achieves up to 25% relative improvement over state-of-the-art methods.
New framework distinguishes knots via neighborhood invariants.
problem Distinguishing knots and knotoids.
method Study of knotoid spectra and neighborhood invariants.
result Neighborhood invariants can distinguish knots of higher Gordian distance.
This paper tackles selection bias in recommender systems by considering the neighborhood effect.
problem Selection bias in recommender systems due to filtering and user selection.
method Formalizes neighborhood effect as interference problem, introduces treatment representation, and proposes ideal loss.
result Proposed methods achieve unbiased learning when both selection bias and neighborhood effect are present.
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.
Survey of statistical queries and their applications.
problem Understanding statistical queries and their applications.
method Exploration of statistical queries model, definitions, and connections to learnability.
result Connections to learnability and applications in optimization, evolvability, and differential privacy.
Maximally hyperbolic solutions contain future neighborhoods of intersecting hypersurfaces.
problem Maximally globally hyperbolic solutions of higher-dimensional vacuum Einstein equations.
method Analyzing intersections of characteristic hypersurfaces.
result Contains a future neighborhood of intersecting hypersurfaces.
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…
Many prediction problems can be phrased as inferences over local neighborhoods of graphs. The graph represents the interaction between entities, and the neighborhood of each entity contains information that allows the inferences or predictions. We present an approach for applying machine learning directly to such graph…
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 …
Proposes a new NMF method incorporating neighborhood structure for better anomaly detection.
problem NMF's inability to incorporate neighborhood structure information limits its performance in nonlinear manifold structures.
method Integrates neighborhood structure information using Minimum Spanning Tree (MST) within NMF framework.
result Empirical results show superior performance in anomaly detection using the proposed method.
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 Llog(1/ε) for accurate estimation. 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.
AdaWISH improves efficiency of discrete integration queries.
problem Efficiently solving discrete integration in high-dimensional spaces.
method Adaptive quantile queries to reduce query count.
result AdaWISH achieves the same approximation guarantee with fewer queries.
Study on transverse knots and their neighborhoods, proving unique standard neighborhoods and destabilization results.
problem Understanding the structure and properties of transverse knots and their neighborhoods.
method Proves unique standard neighborhoods and structure theorems for non-loose Legendrian knots through destabilization results.
result Finds a manifold with infinite tight contact structures, up to contactomorphism, without Giroux torsion.
In this paper we study the adaptive learnability of decision trees of depth at most d 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^…
We study black-box attacks on machine learning classifiers where each query to the model incurs some cost or risk of detection to the adversary. We focus explicitly on minimizing the number of queries as a major objective. Specifically, we consider the problem of attacking machine learning classifiers subject to a budg…
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 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 …
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…