In the 'Big Data' era, many real-world applications like search involve the ranking problem for a large number of items. It is important to obtain effective ranking results and at the same time obtain the results efficiently in a timely manner for providing good user experience and saving computational costs. Valuable …
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48 results for “Search Ranking”
A ranking network improves neural architecture search efficiency.
problem Efficiently search for neural network architectures without extensive training.
method Pairwise ranking loss for a performance predictor trained on task meta-features.
result The ranking network outperforms the performance predictor in architecture search.
Ancestry improves genealogy search results by ranking diverse record types.
problem Ranking diverse genealogy records equitably from various sources.
method Customized Coordinate Ascent, Stochastic Search, Normalized Cumulative Entropy.
result Demonstrated effectiveness of algorithms in improving relevance and diversity.
EENMF improves e-commerce sponsored search efficiency and effectiveness.
problem Improving efficiency and effectiveness of e-commerce sponsored search.
method End-to-end neural matching framework (EENMF) for vector-based ad retrieval and neural pre-ranking.
result Significantly outperforms baseline in real e-commerce traffic.
New approach to handle ranking function variation in zero-shot NAS.
problem Variation in ranking function outputs due to randomness.
method Viewing ranking function output as a random variable and constructing a stochastic ordering.
result Stochastic ordering boosts performance in neural architecture search.
Paper addresses bias in search intent affecting click behavior.
problem Bias in user search intent affects click behavior and relevance.
method Proposes a search intent bias hypothesis to improve click models.
result Click models can better interpret user clicks and improve retrieval performance.
Paper explores methods to improve web search ranking.
problem Improving web search ranking accuracy with varied test data.
method Model Interpolation and Boosting Algorithm.
result Model Interpolation achieves best results on open test sets.
Improves deep learning for Airbnb search ranking.
problem Challenges in ranking inventory and handling new listings.
method New ranking neural network architecture, positional bias handling, and cold start solutions.
result Significant improvements in inventory ranking and new listing treatment.
GroSS enables efficient search for grouped convolutional architectures.
problem Training grouped convolutional architectures efficiently and effectively.
method GroSS: Group-Size Series Decomposition for Grouped Architecture Search.
result Simultaneous training of differing numbers of groups within a single layer and all possible combinations between layers.
This paper explores LETOR for E-Com search, addressing practical challenges and reporting key findings.
problem Applying LETOR to E-Com search presents unique challenges.
method Investigates practical challenges in LETOR for E-Com search, including feature representation, relevance judgments, and feedback signal exploitation.
result LETOR methods can effectively optimize combinations of popularity-based and relevance-based features, and order rate is the most robust training objective.
Traditional approaches to ranking in web search follow the paradigm of rank-by-score: a learned function gives each query-URL combination an absolute score and URLs are ranked according to this score. This paradigm ensures that if the score of one URL is better than another then one will always be ranked higher than th…
Solves a new bandit problem with duels and pulls for crowdsourcing.
problem Finding the best arms with mean rewards above a threshold.
method Alternates between ranking and binary search to solve TBP-DC.
result Proves optimality of the Rank-Search algorithm.
Framework for optimizing search engine rankings using observational data.
problem Optimizing ranking policies for search engines using limited observational data.
method Formulated expected reward optimization problem, estimated context value distribution, trained ranking policy via Bayesian inference.
result Demonstrated trade-offs in ranking policies trained on empirical reward estimates.
Stochastic gradient descent on manifolds improves low-rank approximation.
problem Efficiently approximate large matrices with lower rank.
method Stochastic gradient descent on a manifold.
result Algorithm outperforms Euclidean space methods on Netflix Prize data.
Paper proposes HCDC to improve hyperparameter search efficiency.
problem Poor generalizability of dataset condensation across different hyperparameters.
method HCDC algorithm that matches hyperparameter gradients for synthetic validation dataset.
result HCDC effectively maintains validation-performance rankings of models.
Extends linear classification framework to nonlinear SVM-based ranking problems.
problem Maximizing performance on relevant samples in ranking problems.
method Dualization, kernel addition, componentwise dual ascent method.
result General framework for nonlinear classifiers in ranking problems.
Study online ranking in non-stationary environments with abrupt preference changes.
problem Online learning to rank in a non-stationary cascade model where user preferences change abruptly.
method Cascading non-stationary bandits, proposing CascadeDUCB and CascadeSWUCB algorithms.
result Upper and lower bounds on regret for cascading non-stationary bandits algorithms.
MixPath unifies multi-path neural architecture search with one-shot training.
problem Efficiently searching multi-path neural architectures.
method One-shot multi-path supernet with Shadow Batch Normalization (SBN).
result SBN stabilizes optimization and improves ranking performance.
A data proxy reduces NAS computation cost by preserving key examples.
problem High computation cost in Neural Architecture Search (NAS).
method Measuring example importance based on relative accuracy ranking and creating a data proxy.
result Preserves almost same relative accuracy ranking with 10-20x smaller data proxy.
FairNAS improves fairness in neural architecture search evaluation.
problem Improving accuracy of model ranking in neural architecture search.
method Proposes expectation and strict fairness constraints to ensure equal optimization opportunities for all blocks.
result Demonstrates improved model ranking and accuracy with FairNAS-A.
We analyze the generalized Mallows model, a popular exponential model over rankings. Estimating the central (or consensus) ranking from data is NP-hard. We obtain the following new results: (1) We show that search methods can estimate both the central ranking pi0 and the model parameters theta exactly. The search is n!…
GANs improve missing data imputation for ranking tasks.
problem Missing data in ranking datasets violates standard assumptions.
method Conditional Imputation GAN for Extended Missing At Random and Extended Always Missing At Random mechanisms.
result Optimal GAN imputation for EMAR and EAMAR mechanisms.
In this paper, we propose a convergent parallel best-response algorithm with the exact line search for the nondifferentiable nonconvex sparsity-regularized rank minimization problem. On the one hand, it exhibits a faster convergence than subgradient algorithms and block coordinate descent algorithms. On the other hand,…
This paper explores using neural networks to improve Airbnb search performance.
problem Plateaued gains from gradient boosted decision tree model in search ranking.
method Applied neural networks to improve search performance.
result Shows elements useful in applying neural networks to a real-life product.
Paper builds LETOR models for e-commerce, segmenting queries and optimizing search results.
problem Optimizing search results for e-commerce platforms.
method Segmenting queries into broad and narrow, using denoising auto-encoders and skip-gram embeddings, employing various feature types.
result Specialized models for broad and narrow queries outperform a combined model.
SCARLET-NAS improves neural architecture search by stabilizing feature inconsistencies.
problem Difficulty in ranking subnetworks due to feature inconsistency in skip connections.
method Introduced an equivariant learnable stabilizer to homogenize feature disparities.
result SCARLET-A achieves 76.9% top-1 accuracy on ImageNet.
New method saves computational budget by ranking and transferring learning curves.
problem Expensive automated machine learning methods for hyperparameter and neural architecture optimization.
method Tackles as a ranking and transfer learning problem, optimizing a pairwise ranking loss and leveraging learning curves from other datasets.
result Accelerates neural architecture search by a factor of up to 100 without significant performance degradation.
Paper introduces MPES for top-k ranking BO with preferential observations.
problem Handling top-k ranking and tie/indifference observations in Bayesian optimization.
method Designs a surrogate model and introduces MPES acquisition function.
result MPES outperforms existing acquisition functions in handling preferential observations.
Paper proposes a method to aggregate customer engagement data for better ranking of e-commerce results.
problem Cold start problem and under-representation of new or under-impressed products in e-commerce search results.
method Aggregates customer engagements within a day for the same query as input training data for machine learning models.
result Training models on aggregated data leads to better ranking of new and under-impressed products.
Algorithm learns diverse rankings for search engines.
problem Designing algorithms for search engines to rank diverse items.
method LDR (Learning Diverse Rankings) algorithm, efficient learning based on users' feedback.
result Algorithm achieves optimal ranking performance with regret.
Improved neural architecture search through balanced training.
problem Inconsistent ranking of architectures under one-shot training.
method Balanced NAO: balanced training of supernet during search.
result Significant improvements in architecture discovery and performance.
Improved unsupervised probing for ranking tasks using Contrast-Consistent Ranking.
problem Improving self-consistency in language model rankings.
method Adapting Contrast-Consistent Search (CCS) to Contrast-Consistent Ranking (CCR) for ranking tasks.
result CCR probing outperforms prompting techniques across different models and datasets.
Paper tackles underranking in group-fair ranking systems, proving a trade-off and presenting an algorithm.
problem Underranking in group-fair ranking systems can worsen social and economic inequalities.
method Formulated underranking as a new problem, proved a lower bound, and presented a fair ranking algorithm.
result Algorithm achieves best of underranking and group fairness, confirming theoretical trade-off.
This work simplifies proximal mapping for low-rank norms.
problem Efficient computation of proximal mappings for low-rank inducing norms.
method Reduces proximal mapping to nested binary search, solving simpler problems analytically.
result Simplified computation of proximal mappings for various norms.
New algorithms estimate Jacobian matrices for large-scale machine learning.
problem Efficiently computing search directions for large nonlinear least squares.
method Exploit low-rank structure in Hessian to estimate Jacobian matrices.
result Two algorithms perform well compared to state-of-the-art methods.
neuralRank ranks models for better deep learning applications
problem Finding the most suitable pre-trained neural network model for a specific task
method Developed a ranking algorithm that evaluates model suitability based on discriminating power
result Models ranked highly by neuralRank tend to have higher accuracy in practice
TFB simplifies Bayesian LLM uncertainty estimation without extra training.
problem Estimating uncertainty in LLM responses remains challenging.
method Training-Free Bayesianization (TFB) that transforms low-rank adapters into Bayesian ones without additional training.
result TFB achieves superior uncertainty estimation and generalization compared to existing methods.
QS-BO optimizes functions using only rank-based feedback.
problem Optimizing expensive functions with unreliable or unavailable metric values.
method Quantile-scaling pipeline to convert ranks into Gaussian targets.
result QS-BO consistently achieves lower objective values and is statistically significant.
Adversarial self-play in two-player games has delivered impressive results when used with reinforcement learning algorithms that combine deep neural networks and tree search. Algorithms like AlphaZero and Expert Iteration learn tabula-rasa, producing highly informative training data on the fly. However, the self-play t…
Paper proposes a new evaluation method for NAS search phase.
problem NAS search phase effectiveness not well evaluated.
method Compare NAS solutions with random selection; evaluate weight sharing strategy.
result State-of-the-art NAS algorithms perform similarly to random selection.
Scalable model for slate recommendation learns reward probabilities.
problem Scalable personalized slate recommendation in large action spaces.
method Probabilistic Rank and Reward (PRR) model combining reward, interaction, and rank.
result PRR outperforms existing methods and is scalable to large action spaces.
Higher-Order Ranking and Link Prediction: From Closing Triangles to Closing Higher-Order Motifscs.LG
New methods for faster ranking and link prediction using higher-order motifs.
problem Real-time ranking and link prediction in applications like web search.
method Higher-order ranking and link prediction methods based on closing higher-order network motifs.
result The methods are faster and more efficient than existing methods based on closing triangles.
Protein Thoughts interprets protein interactions with clear reasoning, improving prediction accuracy.
problem Lack of mechanistic justification in protein-protein interaction predictions.
method Interpretable search problem reformulation, hypothesis-guided entropy-regularized Tree-of-Thoughts search, embedding-space flow matching.
result Improves mean best-binder rank from 47.7 to 11.2 on SHS148k benchmark.
A new estimator reduces bias and variance in ranking policy evaluation.
problem Estimating ranking policies using logged data in recommender systems.
method Cascade Doubly Robust estimator based on the cascade assumption.
result The estimator reduces bias and variance compared to existing methods.
Regret-Aware Black-Box Optimization with Natural Gradients, Trust-Regions and Entropy Controlstat.ML
Improved MORE algorithm reduces regret in black-box optimization and RL tasks.
problem Noisy fitness evaluations and poor sample quality in black-box optimization.
method Decouples mean and covariance updates, uses entropy scheduling, and simplifies model learning.
result Significantly reduces regret in black-box optimization and RL tasks.
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…
We give a construction of hyperbolic 3-manifolds with rank two fundamental groups and report an experimental search to find such manifolds. Our manifolds are all surface bundles over the circle with genus two surface fiber. For the manifolds so obtained, we then examine whether they are of Heegaard genus two or not. As…
An alternative proof of the vulnerability of retrieval in high intrinsic dimensionality neighborhoodcs.LG
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.