CwA optimizes search performance by jointly learning a balanced database partition and a neural probing function.
problem Suboptimal search performance due to mismatched database and query distributions.
method CwA jointly learns a balanced database partition and a neural probing function to optimize search performance directly for the query distribution.
result CwA achieves up to 4.7x throughput over state-of-the-art methods at equal recall.
We consider accurately answering smooth queries while preserving differential privacy. A query is said to be K-smooth if it is specified by a function defined on [−1,1]d whose partial derivatives up to order K are all bounded. We develop an ε-differentially private mechanism for the class of K-smooth queries…
Improves text-to-SQL models by selecting the best SQL query from beam output.
problem Simplifying database query writing for natural language questions.
method Discriminative re-ranker using BERT fine-tuned classifier.
result Achieved top 4 score on Spider leaderboard.
Differentially private data structures for estimating distances between strings.
problem Estimating distances between query strings and database strings while ensuring privacy.
method Proposes differentially private data structures for Hamming and edit distances using randomized response technique.
result Efficient data structures that provide accurate distance estimates with strong privacy guarantees.
Paper proposes a method to predict optimal data partitioning based on query execution costs.
problem Finding optimal data partitioning for improved system performance and scalability.
method Formal model abstraction of workload queries, genetic algorithm for optimization, evaluation using PostgreSQL's query optimizer.
result The approach effectively reduces workload execution cost and improves system performance.
Rk-means clusters relational data without full matrix, speeding up clustering.
problem Clustering relational data without full matrix computation.
method Constructs a grid coreset for clustering, avoiding expensive feature extraction queries.
result Orders-of-magnitude speedup in clustering relational data.
We propose a quantization based approach for fast approximate Maximum Inner Product Search (MIPS). Each database vector is quantized in multiple subspaces via a set of codebooks, learned directly by minimizing the inner product quantization error. Then, the inner product of a query to a database vector is approximated …
ADSH uses asymmetric deep hashing for faster training and better performance.
problem Efficiently utilizing supervised information for large-scale database in deep supervised hashing.
method ADSH learns a deep hash function only for query points and directly learns hash codes for database points.
result ADSH achieves state-of-the-art performance in real applications.
Neural Programmer learns natural language queries for databases.
problem Natural language interface learning for database queries.
method Enhanced Neural Programmer model trained on weak supervision.
result Single Neural Programmer model achieves 34.2% accuracy.
Proposes CRA framework for certifying fair predictive models.
problem Certifying fairness of predictive models trained on biased data.
method Formulates CRA for fairness queries, uses background knowledge and limited target population stats.
result Builds certifiably fair predictive models on target population.
Paper introduces probabilistic search for structured data.
problem Difficult to extract relevant data from databases without domain knowledge.
method Probabilistic programming and nonparametric Bayes for flexible search.
result Users prefer probabilistic search results over standard baselines.
Most content-based image retrieval systems consider either one single query, or multiple queries that include the same object or represent the same semantic information. In this paper we consider the content-based image retrieval problem for multiple query images corresponding to different image semantics. We propose a…
Topology-based information retrieval improves query accuracy.
problem Query accuracy in databases with complex structures.
method Dilation-invariant comparative measures of persistent homology.
result Topology-based retrieval outperforms standard methods.
Amortizes MIPS by training neural networks to predict optimal keys.
problem Efficiently solving Maximum Inner Product Search for repeated queries.
method Regression-based SupportNet and KeyNet models trained on support functions.
result Significant improvement in IVF match rates for document embeddings.
Bayesian entity resolution merges together multiple, noisy databases and returns the minimal collection of unique individuals represented, together with their true, latent record values. Bayesian methods allow flexible generative models that share power across databases as well as principled quantification of uncertain…
Content based image retrieval, a technique which uses visual contents of image to search images from large scale image databases according to users' interests. This paper provides a comprehensive survey on recent technology used in the area of content based face image retrieval. Nowadays digital devices and photo shari…
Private learning model to protect scalar value estimation from an adversary.
problem Protecting scalar value estimation from an adversary in sequential queries.
method Formulated a private learning model involving a learner and an adversary observing queries but not responses.
result Tight upper and lower bounds on the learner's query complexity as a function of privacy and accuracy.
Paper improves text-to-SQL translation by encoding schema relations with self-attention.
problem Improving text-to-SQL translation accuracy across diverse databases.
method Uses relation-aware self-attention to encode schema information.
result Significant gains on Spider dataset (42.94% exact match accuracy).
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…
Deep learning features improve CBIR system performance.
problem Retrieving similar images from a large database.
method Using features from pre-trained deep learning models for similarity retrieval.
result Significantly superior retrieval results compared to traditional methods.
Interactive image retrieval system learns from user feedback and unlabeled data.
problem Efficiently retrieve relevant images with minimal user interaction.
method Combines active learning and graph-based semi-supervised learning (GSSL) to use unlabeled data.
result High F1 scores with few relevance feedback rounds on large datasets.
New vector quantization method reduces relevance of parallel components in database points.
problem Scaling maximum inner product search to massive databases.
method Developed anisotropic vector quantization loss functions.
result Achieves state-of-the-art results on public benchmarks.
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…
Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually departs from some notion of similarity, comparing two enzymes by looking for correspondences in their sequences, structures or surfaces. For a…
UQE uses LLMs to analyze unstructured data efficiently.
problem Efficient analytics on unstructured data.
method Proposes UQE, a query engine that uses LLMs to interpret UQL queries.
result Demonstrates efficient analytics on various unstructured data types.
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.
Improved cover detection using dominant melody embeddings.
problem Challenging cover detection in large audio databases.
method Neural network architecture for track embeddings, focusing on dominant melody.
result Improved accuracy on small and large datasets, scalable to thousands of tracks.
New privacy mechanism reduces error in query results.
problem Achieving privacy while minimizing noise in query results.
method Extended sufficient and necessary condition for (ε,δ)-differential privacy for symmetric and log-concave noise densities. result Significantly lower mean squared errors than Laplace and Gaussian mechanisms.
Graph database outperforms in filtering ESG stocks efficiently.
problem Efficiently filtering ESG stocks from large lists of equities.
method Compared SQL, No-SQL, and graph databases; used Python for database interactions; collected data from stock price and financial news.
result Graph database is more efficient for ESG stock filtering.
Enhances visual localization using graph smoothing.
problem Inferring camera pose from a single image.
method Constructs a graph based on GPS coordinates and temporal information, then smooths feature representations.
result Significantly improves localization accuracy on large datasets.
Improved privacy for heavy-hitters detection.
problem Private detection of heavy-hitters in dynamic data.
method New algorithm for privately identifying heavy-hitters.
result Improved error guarantees over existing techniques.
SANNS secures k-NNS queries and results without revealing them.
problem Securely searching for nearest neighbors without exposing sensitive data.
method Optimized linear scan and clustering-based algorithm with cryptographic primitives.
result SANNS achieves up to 31x faster response times and scales to 10 million entries.
Algorithm approximates target distribution using weight queries.
problem Approximating a distribution without sampling.
method Interactive algorithm using weight queries and UCB approach.
result Approximation bound on total variation distance.
Paper proposes a reinforcement learning framework for distant supervision of question parsing.
problem Lack of gold annotations for parsing in QA datasets.
method Reinforcement learning framework based on formal queries to learn mentions of entities and relations.
result Significant improvement in entity and relation linking compared to state of the art.
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.
NeuroMatch efficiently matches subgraphs in large graphs using neural networks.
problem Determining the presence and location of a query graph in a large target graph.
method NeuroMatch decomposes graphs into subgraphs, embeds them using graph neural networks, and matches them directly in the embedding space.
result NeuroMatch is 100x faster and 18% more accurate than existing methods.
Study of clustering with noisy queries, providing lower bounds and efficient algorithms.
problem Recovering true clustering from noisy oracle queries.
method Information theoretic lower bounds, novel algorithms for adaptive and non-adaptive settings.
result First algorithms matching query complexity lower bound, computationally efficient.
New method embeds DNA sequences for faster, more informative gene comparison.
problem Slow and costly sequence comparison methods for genes without exact matches.
method Recurrent neural networks to embed sequences in a low-dimensional space.
result Embedding allows for better comparison of genes without exact matches.
Smoothly prepares quantum states for robust machine learning.
problem Efficiently preparing quantum states for machine learning.
method Smoothed analysis to prove constant query state preparation.
result State preparation can be achieved with constant queries under realistic noise conditions.
FLAME efficiently matches high-dimensional categorical datasets for causal inference.
problem Matching treatment and control units based on covariate information in causal inference.
method FLAME learns a distance metric using a hold-out training set and uses query processing techniques for large datasets.
result FLAME achieves significantly better performance than other matching methods, scaling to huge datasets.
Paper uses deep learning for accurate, monotonic cardinality estimation.
problem Accurate and monotonic cardinality estimation for similarity selection.
method Feature extraction to Hamming space, followed by deep learning regression.
result Demonstrates improved query optimizer performance.
Paper studies how incomplete data affects machine learning, proposing 'Certain Predictions' for NN classifiers.
problem Impact of incomplete and inconsistent data on machine learning applications.
method Extends Certain Answers concept to machine learning, introduces 'Certain Predictions' for NN classifiers.
result Developed efficient solutions for CP queries in NN classifiers, improving classification accuracy.
We consider the problem of designing locality sensitive hashes (LSH) for inner product similarity, and of the power of asymmetric hashes in this context. Shrivastava and Li argue that there is no symmetric LSH for the problem and propose an asymmetric LSH based on different mappings for query and database points. Howev…
Improved dynamic classifier selection by refining regions of competence.
problem Limited performance of dynamic selection systems due to noisy regions.
method Integrates a filter and an adaptive distance to enhance regions of competence.
result Significant increase in recognition performance and decrease in computational cost.
BUDS balances privacy and utility by shuffling data, achieving strong privacy with minimal loss.
problem Balancing privacy and utility in crowd-sourced statistical databases.
method One-hot encoding, iterative shuffling, loss estimation, risk minimization.
result Achieves ε=0.02 for privacy, maintaining a privacy bound of ε=ln[t/((n1−1)S)]. The results from most machine learning experiments are used for a specific purpose and then discarded. This results in a significant loss of information and requires rerunning experiments to compare learning algorithms. This also requires implementation of another algorithm for comparison, that may not always be correc…
GraphQ system uses GNNs to search for subgraph patterns in graphs.
problem Efficiently identifying and matching subgraph patterns in graph data.
method Graph neural networks (GNNs) for encoding graph data and NeuroAlign for node alignment.
result NeuroAlign improves node-alignment accuracy by 19-29% compared to baseline GNNs.
Differential privacy formalises privacy-preserving mechanisms that provide access to a database. We pose the question of whether Bayesian inference itself can be used directly to provide private access to data, with no modification. The answer is affirmative: under certain conditions on the prior, sampling from the pos…