AIPS improves ranking policy evaluation by adapting to diverse user behavior.
problem Inaccurate Off-Policy Evaluation of ranking policies due to high variance under diverse user behavior.
method Developed Adaptive IPS (AIPS) that adapts to different user behaviors and minimizes MSE.
result AIPS achieves minimum variance among unbiased estimators and provides significant empirical accuracy improvement.
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 O ( ( N − L ) log ( T ) ) O((N-L)\log(T)) O (( N − L ) log ( T )) regret. 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.
Analyzes re-ranking diversification algorithms based on function optimization.
problem Improving diversification in ranking systems.
method Examines re-ranking algorithms based on maximizing submodular/modular functions and their optimality in terms of total curvature.
result Adjusting hyperparameters can optimize relevance-diversity trade-offs.
Paper tackles online ranking and diversification in recommender systems.
problem Maximizing relevance and diversity in ranked lists for online recommendation.
method CascadeHybrid approach that combines contextual bandits for relevance and topical diversity.
result CascadeHybrid outperforms baselines in real-world datasets.
Study uses network analysis to rank countries based on food production diversity and specialization.
problem Ranking countries based on their food production diversity and specialization.
method Network analysis of country-food production data, transforming into overlap matrices, identifying subsets, and ranking based on fitness and specialization.
result Countries with high fitness produce highly specialized food commodities, while those with low fitness produce diverse but low specialized food products.
LoRA-MCL improves language models by generating diverse sentence continuations.
problem Language models struggle with generating diverse, plausible sentence continuations.
method Low-Rank Adaptation combined with Multiple Choice Learning (MCL) to handle ambiguity.
result LoRA-MCL generates high-diversity and relevant outputs in various tasks.
Algorithm learns diverse recommendations from partial clicks.
problem Learning diverse recommendations from partial user feedback.
method Online learning algorithm for cascade model.
result Gap-free upper bound on n-step regret proved.
The paper improves recommendation systems by ensuring their outputs are reliable.
problem Recommendation systems often lack reliability guarantees for their outputs.
method The method uses a pre-trained ranking model to create a set of items with rigorous FDR control.
result The approach provides a way to guarantee the reliability of recommendation outputs.
This paper improves cross-modal learning to rank by using self-paced learning with non-linear mapping functions.
problem Challenges in learning cross-modal similarity, especially with linear mapping functions and equal importance assumption.
method Incorporates self-paced learning theory with diversity into cross-modal learning to rank, using non-linear mapping functions.
result Significant improvements over state-of-the-art methods in cross-modal retrieval tasks.
Unsupervised scheme ranks sentences in text documents based on semantic importance.
problem Ranking sentences in text documents without labeled data.
method Extracts essential words and phrases, constructs semantic phrase and sentence graphs, applies PageRank, combines scores, and optimizes for topic diversity.
result SSR outperforms individual judges and compares favorably with combined rankings on benchmarks.
Many latent (factorized) models have been proposed for recommendation tasks like collaborative filtering and for ranking tasks like document or image retrieval and annotation. Common to all those methods is that during inference the items are scored independently by their similarity to the query in the latent embedding…
New method learns DPP kernel for faster product recommendation.
problem Efficiently learning DPP kernels for recommendation.
method Low-rank factorization of DPP kernel matrix.
result Significantly faster learning and prediction times.
The paper sorts big data by revealed preferences, improving consumer and policy decisions.
problem Sorting diverse consumer preferences for big data objects like colleges.
method Endogenous weighting of revealed preferences, considering spillover effects.
result Consistent steady-state solution to counterbalance equilibrium.
Paper tackles diversity in Airbnb search results.
problem Show diverse results to improve user experience.
method Heuristic based approaches → Deep learning solution using RNNs.
result Novel deep learning solution improves diversity in search results.
Paper presents a deterministic method for diverse subset selection.
problem Diverse subset selection problems in recommendation, summarization, and search.
method Greedy deterministic adaptation of k-DPP for low-rank approximations and image search.
result The method yields low-rank approximations of kernel matrices and demonstrates effectiveness in image search.
LoCo-RLHF models diverse human feedback with contextual information.
problem Heterogeneous human feedback from diverse contexts and preferences.
method Low-rank contextual preference model, PRS policy.
result LoCo-RLHF achieves tighter sub-optimality gap than existing methods.
A deep reinforcement learning approach for slate re-ranking in e-commerce.
problem Improving user satisfaction in e-commerce by optimizing the ranking of items in a slate.
method Generator and Critic approach, using reinforcement learning and a Full Slate Critic model.
result The Generator and Critic approach significantly outperforms existing methods in slate evaluation and efficiency.
Identifies latent actions and dynamics from offline data with diverse demonstrators.
problem Recovering latent actions and environment dynamics from action-free trajectories.
method Assumes distinct policies for each demonstrator, identifies latent transitions and policies via matrix factorization.
result Identifies latent transitions and demonstrator policies up to permutation.
Diverse sampling improves kernel methods' performance in sparse regions.
problem Improving kernel methods' performance in sparse regions of datasets.
method Using Determinantal Point Processes (DPP) for sampling diverse landmarks in Nyström approximation.
result Nyström kernel regression with diverse landmarks increases accuracy in sparse regions of the dataset.
New model fusion method improves Bitcoin price prediction accuracy.
problem Improving robustness in financial price prediction models.
method Combinatorial Fusion Analysis (CFA) combining score and rank combinations.
result Significantly improved MAPE performance of 0.19\%.
Hierarchical framework for model evaluation on leaderboards
problem Uncertainty and variability in model performance across tasks
method Hierarchical framework with task-level and leaderboard-level rank prediction intervals
result Statistically valid and informative model rank intervals
New gossip algorithms improve robustness of rank-based statistics in decentralized systems.
problem Ensuring robustness in decentralized AI and edge intelligence systems, especially in the presence of corrupted or adversarial data.
method Developed asynchronous gossip algorithms for computing rank-based statistics.
result First convergence rate bound for asynchronous gossip-based rank estimation.
Study ranking in generalized linear bandits with position and item dependencies.
problem Complex reward function due to position and item dependencies in recommendation systems.
method Model position and item dependencies, design UCB and Thompson Sampling algorithms.
result Generalizes existing studies in position dependencies and graph theory.
Ensemble method ranks homologous proteins robustly across various similarity metrics.
problem Ranking homologous proteins in a candidate set with high accuracy and robustness.
method Ensemble of models and assessment metrics, phalanxes, and aggregation of diverse metrics.
result Ensemble of phalanxes identifies strong and diverse subsets of feature variables for robust ranking.
MARS automatically selects tensor decomposition ranks, improving performance in neural network tasks.
problem Determining optimal decomposition ranks in tensor decompositions.
method MARS uses binary masks to learn optimal tensor structure during training via relaxed MAP estimation.
result MARS achieves better results than previous methods in various tasks.
Paper fine-tunes LLMs for financial tasks using data fusion.
problem Improving LLMs for financial analysis tasks.
method Fine-tuned Llama3-8B and Mistral-7B using PEFT and LoRA, combined datasets for data fusion.
result Enhanced model performance across financial tasks.
Paper introduces new norms for rank-constrained optimization problems.
problem Rank-constrained optimization problems in various fields.
method Introduces a family of low-rank inducing norms and regularizers.
result Other low-rank inducing norms outperform nuclear norm in matrix completion problems.
This paper tackles ranking-based performance normalization for optimization algorithms.
problem Ranking optimization algorithms across diverse numerical scales disrupts performance comparisons.
method Introduces absolute ranking and a sampling-based computational method to address numerical scale variation.
result Provides a more robust framework for assessing performance across multiple algorithms and problems.
PILNO uses neural operators to solve PDEs efficiently on point clouds.
problem Solving partial differential equations (PDEs) on point cloud data efficiently.
method Physics-informed low-rank neural operator framework combining low-rank kernel approximations and an encoder-decoder architecture.
result PILNO efficiently approximates solution operators of PDEs on point cloud data, satisfying PDE constraints and boundary conditions.
Paper presents a rank-1 approximation method for natural policy gradients in deep RL.
problem Computing natural gradients requires inverting the Fisher Information Matrix, which is computationally expensive.
method Develops a rank-1 approximation to the inverse Fisher Information Matrix for efficient natural policy optimization.
result The rank-1 approximation converges faster and has similar sample complexity to stochastic policy gradient methods.
We describe dynamics of financial market observables and apply to portfolio performance.
problem Lack of mathematical description of financial market observables.
method Rank-based models and functionally generated portfolios.
result Performance of functionally generated portfolios studied over short and medium-term horizons.
Training task diversity improves ICL with linear attention.
problem Understanding the impact of training task diversity on in-context learning.
method Modeling training task vectors as a mixture of low-rank Gaussians.
result Our model explains why training with task diversity shortens the ICL plateau and achieves out-of-distribution generalization.
Unified framework for statistical inference of low-rank tensors.
problem Statistical inference for tensors in high-dimensional data.
method Unified framework using debiasing and tangent space projection.
result Achieves asymptotic normality and minimax-optimal confidence intervals.
Efficient algorithm for Hadamard decomposition of matrices.
problem Decomposing matrices into low-rank factors efficiently.
method Alternating optimization with SVD-inspired initialization and momentum.
result Significantly improved performance compared to existing methods.
RSIC identifies multiple ranks of interest in NMF by analyzing residual sensitivity.
problem Determining the optimal rank in NMF.
method RSIC analyzes sensitivity of relative residuals to different initializations.
result RSIC identifies meaningful ranks consistent with data structure.
New method predicts binary matrix entries using empirical Bayes and low-rank structure.
problem Predicting unobserved entries in binary matrices.
method Empirical Bayes method motivated by Efron--Morris estimator, exploiting low-rank structure.
result Superior performance in predictive accuracy, calibration, and efficiency compared to existing methods.
Simplifies complex RL policies by ranking important decisions.
problem Complexity in RL policies makes them hard to analyze and interpret.
method Statistical fault localisation to rank states and prune unimportant decisions.
result Pruned policies can perform similarly to original policies, improving interpretability.
Rank-based Bayesian Optimization improves molecule selection in chemical systems.
problem Optimizing chemical compounds using traditional regression models.
method Introducing Rank-based Bayesian Optimization (RBO) using ranking models.
result RBO outperforms regression-based BO, especially for rough landscapes and activity cliffs.
FLoE adapts LLMs by selectively deploying LoRA adapters based on layer importance and task requirements.
problem Uniform LoRA deployment across all layers leads to inefficient and redundant parameter allocation.
method FLoE uses Fisher information to dynamically identify task-critical layers and optimizes LoRA ranks.
result FLoE achieves significant efficiency-accuracy trade-offs, especially in resource-constrained environments.
Proposes a deep learning model for timely and accurate recommendations.
problem Inability to provide timely recommendations and ranking issues with implicit feedback.
method Unified cross-network solution using listwise ranking for implicit data.
result Superior performance in accuracy, novelty, and diversity compared to baselines.
iSplit LBI predicts individualized partial rankings from ties, outperforming state-of-the-art methods.
problem Predicting partial rankings from pairwise comparisons with ties, considering individual preferences.
method Variable splitting-based algorithm (iSplit LBI) that generates a sequence of estimations with a regularization path, decomposing parameters into abnormal signals, personalized signals, and random noise.
result iSplit LBI significantly outperforms state-of-the-art alternatives in predicting individualized partial rankings.
Hyperfitting improves LLM generation quality by enhancing diversity, contrary to simple temperature scaling.
problem Improving open-ended generation quality of LLMs with minimal fine-tuning effort.
method Demonstrates that hyperfitting, a phenomenon where LLMs are fine-tuned to near-zero training loss, enhances generation quality and mitigates repetition.
result Hyperfitting is distinct from temperature scaling and involves a dynamic, context-dependent rank reordering mechanism in the final transformer block.
This paper evaluates various loss functions for Transformer models in stock ranking.
problem Evaluating loss functions for Transformer models in stock ranking.
method Systematic evaluation of advanced loss functions (pointwise, pairwise, listwise) on S&P 500 data.
result Different loss functions impact a model's ability to discern profitable relative orderings among assets.
Paper improves uncertainty estimation in LLM-as-a-judge systems.
problem Improving uncertainty estimation in LLM-as-a-judge frameworks.
method Generalised probabilistic modelling and improved uncertainty estimates.
result Proposed uncertainty estimates significantly improve system efficiency.
New algorithm tackles dynamic query routing to multiple embedding models.
problem Dynamic query routing to multiple embedding models under adversarial conditions.
method Formalized as adversarial contextual linear bandit with low-rank experts, proposed HPG algorithm.
result HPG algorithm achieves linearized policy regret of i l d e O ( s M T ) ilde{\mathcal O}(s\sqrt{M T}) i l d e O ( s M T ) . We have studied numerically the statistical mechanics of the dynamic phenomena, including money circulation and economic mobility, in some transfer models. The models on which our investigations were performed are the basic model proposed by A. Dragulescu and V. Yakovenko [1], the model with uniform saving rate develop…
Paper presents a technique using Spearman's Rank Correlation Coefficient for KE in TDs.
problem Extracting common characteristics and grouping similar TDs.
method Spearman's Rank Correlation Coefficient (SRCC) for KE.
result SRCC proves a comprehensive measure for high-quality KE.