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.
In this paper we address cardinality estimation problem which is an important subproblem in query optimization. Query optimization is a part of every relational DBMS responsible for finding the best way of the execution for the given query. These ways are called plans. The execution time of different plans may differ b…
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.
Discrete integration in a high dimensional space of n variables poses fundamental challenges. The WISH algorithm reduces the intractable discrete integration problem into n optimization queries subject to randomized constraints, obtaining a constant approximation guarantee. The optimization queries are expensive, which…
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.
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.
Deep neural networks have recently achieved tremendous success in image classification. Recent studies have however shown that they are easily misled into incorrect classification decisions by adversarial examples. Adversaries can even craft attacks by querying the model in black-box settings, where no information abou…
This paper improves retrieval for LLMs in financial document Q&A.
problem Suboptimal text chunk retrieval by RAG causes inaccuracies in LLM responses.
method Sophisticated chunking techniques, query expansion, metadata annotations, re-ranking algorithms, and embedding fine-tuning.
result Enhanced retrieval quality improves LLM performance and reliability.
The problem of active diagnosis arises in several applications such as disease diagnosis, and fault diagnosis in computer networks, where the goal is to rapidly identify the binary states of a set of objects (e.g., faulty or working) by sequentially selecting, and observing, (noisy) responses to binary valued queries. …
A classical problem in causal inference is that of matching, where treatment units need to be matched to control units based on covariate information. In this work, we propose a method that computes high quality almost-exact matches for high-dimensional categorical datasets. This method, called FLAME (Fast Large-scale …
Federated learning is a distributed form of machine learning where both the training data and model training are decentralized. In this paper, we use federated learning in a commercial, global-scale setting to train, evaluate and deploy a model to improve virtual keyboard search suggestion quality without direct access…
LITE models improve query-document relevance with learnable late interactions.
problem Improving query-document relevance with lower latency and storage.
method Proposes learnable late-interaction models (LITE) that use factorized query and document embeddings followed by a learnable scorer.
result Empirically, LITE outperforms previous late-interaction models in re-ranking tasks.
We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from networks with both high and low quality sensors; and (ii) how to perform query bas…
Recent studies have shown that adversarial examples in state-of-the-art image classifiers trained by deep neural networks (DNN) can be easily generated when the target model is transparent to an attacker, known as the white-box setting. However, when attacking a deployed machine learning service, one can only acquire t…
Active learning selects high-quality examples for text-to-SQL systems.
problem Efficiently annotate large language models for text-to-SQL systems.
method Formalizes example selection as a constrained experimental design problem over semantic query embeddings, proposing a stratified greedy algorithm that maximizes heteroscedastic mutual information.
result Proposed method significantly reduces labeling effort while maintaining high text-to-SQL retrieval accuracy.
Pairwise "same-cluster" queries are one of the most widely used forms of supervision in semi-supervised clustering. However, it is impractical to ask human oracles to answer every query correctly. In this paper, we study the influence of allowing "not-sure" answers from a weak oracle and propose an effective algorithm …
QActor optimizes learning from noisy labeled data streams by querying experts for clean labels.
problem Learning from noisy labeled data in continuous streams with limited oracle queries.
method Combines quality models for filtering and oracle queries for true labels, dynamically adjusting query limits.
result QActor nearly matches optimal accuracy with up to 6% additional ground truth data from experts.
Establishes upper bounds on generalization error in active learning.
problem Improving query algorithms in active learning.
method Derives upper bounds on generalization error using informativeness and representativeness query strategies.
result Validates the use of regularization techniques to ensure bounds' validity.
Optimizes query routing to LLMs under cost and resource constraints.
problem Non-uniform or adversarial batching in per-query routing methods leads to cost inefficiency.
method Batch-level, resource-aware routing framework that jointly optimizes model assignment for each batch.
result Robust routing framework improves accuracy by 1-14% over non-robust methods.
A new attack for probabilistic classifiers adapts to noise levels.
problem Adversarial examples can mislead probabilistic classifiers.
method Adapts HopSkipJump attack for probabilistic classifiers, adjusting queries based on noise levels.
result Decision-based attacks are effective against probabilistic classifiers, even with noise.
Unified framework for deferring queries to top-k experts, improving accuracy-cost trade-offs.
problem Limitation of existing L2D frameworks to single-expert deferral.
method Top-k Learning-to-Defer framework, including adaptive Top-k(x) variant. result Superior accuracy-cost trade-offs with multi-expert deferral.
Machine learning models have been found to be susceptible to adversarial examples that are often indistinguishable from the original inputs. These adversarial examples are created by applying adversarial perturbations to input samples, which would cause them to be misclassified by the target models. Attacks that search…
BMBO-DARN optimizes expensive functions with varying fidelities.
problem Optimizing expensive, multi-fidelity functions efficiently.
method Batch Multi-fidelity Bayesian Optimization with Deep Auto-Regressive Networks.
result BMBO-DARN improves surrogate learning and optimization performance.
Dynamic classifier selection systems aim to select a group of classifiers that is most adequate for a specific query pattern. This is done by defining a region around the query pattern and analyzing the competence of the classifiers in this region. However, the regions are often surrounded by noise which can difficult …
Meta-Router optimizes LLM selection using gold-standard and preference-based data.
problem Training a high-quality LLM router with combined data sources is challenging due to bias and scarcity.
method Developed an integrative causal router training framework to correct bias and improve routing accuracy.
result Our approach delivers more accurate routing and improves the trade-off between cost and quality.
The article presents a method that improves the quality of classification of objects described by a combination of known and unknown features. The method is based on modernized Informational Neurobayesian Approach with consideration of unknown features. The proposed method was developed and trained on 1500 text queries…
Bayesian method helps decision-makers find preferred solutions in multi-objective optimization.
problem Identifying preferred solutions from the Pareto set in multi-objective optimization problems.
method Bayesian model to estimate decision-maker's utility function based on pairwise comparisons, guided by a principled elicitation strategy.
result Superior performance in finding high-utility solutions with a small number of queries.
Estimates neural architecture performance speedily.
problem Accurately evaluating neural architectures' generalization performance.
method Estimates final test performance based on training speed.
result Consistently outperforms other alternatives in correlation with true test performance.
TableQnA answers web queries about lists and superlatives from HTML tables.
problem Answer web queries about lists and superlatives from HTML tables.
method Extract intent from queries, use structure-aware matching, and train models with automatic data generation.
result Significantly higher precision and coverage for list and superlative queries.
Scarcity of training data for task-oriented dialogue systems is a well known problem that is usually tackled with costly and time-consuming manual data annotation. An alternative solution is to rely on automatic text generation which, although less accurate than human supervision, has the advantage of being cheap and f…
Improves retrieval accuracy for hierarchical documents, especially for distant matches.
problem Limited expressive power of dual encoder models in hierarchical retrieval.
method Proves feasibility of DEs for HR, introduces pretrain-finetune recipe to improve long-distance retrieval.
result Pretrain-finetune boosts recall on long-distance pairs from 19% to 76%.
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.
Constraint-based clustering algorithms exploit background knowledge to construct clusterings that are aligned with the interests of a particular user. This background knowledge is often obtained by allowing the clustering system to pose pairwise queries to the user: should these two elements be in the same cluster or n…
Collaborative filtering (CF) allows the preferences of multiple users to be pooled to make recommendations regarding unseen products. We consider in this paper the problem of online and interactive CF: given the current ratings associated with a user, what queries (new ratings) would most improve the quality of the rec…
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.
Study uses LLMs to improve financial forecasting by integrating textual and numerical data.
problem Challenges in fusing multimodal information and measuring qualitative outputs from LLMs.
method Created context sets by segmenting daily securities reports into key factors and combining them with numerical data. Used dynamic updates and crafted prompts to assign scores to qualitative insights.
result LLMs outperform time-series models in market forecasting, though challenges remain.
VMGP extends Gaussian processes for Bayesian meta-learning, improving uncertainty prediction.
problem Bayesian meta-learning for few-shot tasks with non-Gaussian uncertainty.
method VMGP (Variational Meta-Gaussian Processes) extends Gaussian processes to model non-Gaussian predictive posteriors.
result VMGP significantly outperforms existing Bayesian meta-learning methods on complex tasks.
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 O~(k3/2) queries, improving on previous methods. In recent years, deep metric learning has achieved promising results in learning high dimensional semantic feature embeddings where the spatial relationships of the feature vectors match the visual similarities of the images. Similarity search for images is performed by determining the vectors with the smallest distanc…
We study the problem of estimating a set of d linear queries with respect to some unknown distribution p over a domain J=[J] based on a sensitive data set of n individuals under the constraint of local differential privacy. This problem subsumes a wide range of estimation tasks, e.g., distrib…
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.
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.
Proposes a max-utility arm selection strategy for reducing cumulative regret in sequential query recommendations.
problem Reduces cumulative regret in sequential query recommendations for closed loop interactive learning settings.
method Proposes a max-utility arm selection strategy based on the maximum utility of arms.
result Improves cumulative regret substantially compared to baseline algorithms and random selection.
A new method for selecting high quality itemsets from a large collection.
problem Selecting a small, high-quality subset of patterns from a larger collection of itemsets.
method Using decomposable families of itemsets and a junction tree structure.
result The method provides high quality results and is computationally efficient.
C-IP improves LLMs' query selection for interactive tasks by estimating uncertainty robustly.
problem Minimizing the number of queries for interactive LLMs.
method Conformal Information Pursuit (C-IP) using conformal prediction sets.
result C-IP achieves better predictive performance and shorter query-answer chains.
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.
Study efficient sequential evaluation of large language models using historical data.
problem Sequentially evaluate a new large language model (LLM) on a fixed question set.
method Construct a confidence sequence (CS) and design active querying rules to shrink CS width.
result Simple uniform sampling can sometimes outperform adaptive querying rules.
Paper proposes a modified uncertainty sampling method to speed up preference learning from noisy humans.
problem Learning preferences from humans with limited queries and noisy responses.
method Modified uncertainty sampling using expected output value to speed up preference learning.
result The modified method outperforms the baseline uncertainty sampling in preference learning.