The paper studies ranking algorithms from pairwise and listwise comparisons, deriving lower bounds and optimal algorithms.
problem Designing efficient ranking algorithms from pairwise and listwise comparisons.
method Deriving lower bounds and proposing optimal algorithms for top-k and total ranking problems.
result The proposed algorithms match the derived lower bounds and are optimal up to a logarithmic factor.
SQL-Rank improves recommendation systems by modeling user rankings as permutations.
problem Improving recommendation systems by better modeling user rankings.
method SQL-Rank uses a listwise approach based on a permutation model to construct user-specific rankings.
result SQL-Rank outperforms current state-of-the-art algorithms for implicit feedback and explicit feedback.
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.
A new model for supervised learning to rank using gradient estimation.
problem Improving supervised learning to rank methods.
method Proposes a new model with a gradient estimator for categorical variables.
result Achieves better or comparable results compared to existing methods.
Paper studies ranking from noisy comparisons with minimal assumptions.
problem Finding exact ranking from noisy comparisons with minimal assumptions.
method Adaptive comparisons, lower and upper bounds derivation.
result Nearly optimal pairwise ranking algorithms and extensions to listwise ranking.
Linear memory stores associations up to a logarithmic scale, but listwise retrieval can handle a quadratic scale.
problem How many key-value associations can a linear memory store?
method Analyzed linear memory models for top-1 and listwise retrieval, proving phase transitions and developing asymptotic theories.
result Linear memory has a logarithmic capacity for top-1 retrieval and a quadratic capacity for listwise retrieval.
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.
SetRank tackles collaborative ranking from implicit feedback using setwise Bayesian approach.
problem Challenges in pairwise and listwise approaches for implicit feedback.
method SetRank is a novel setwise Bayesian approach that accommodates implicit feedback characteristics.
result SetRank outperforms state-of-the-art baselines on real-world datasets.
For many internet businesses, presenting a given list of items in an order that maximizes a certain metric of interest (e.g., click-through-rate, average engagement time etc.) is crucial. We approach the aforementioned task from a learning-to-rank perspective which reveals a new problem setup. In traditional learning-t…
Efficiently calculates PL model likelihood for partitioned preference data.
problem Computational infeasibility of calculating PL model likelihood for partitioned preference data.
method Random utility model formulation and efficient numerical integration approach.
result Proposed method outperforms existing LTR baselines and scales to real-world tasks.
Proposes new listwise learning-to-rank models to address rating ties and document relevance.
problem Rating ties and document relevance in existing listwise learning-to-rank models.
method Models ranking as selecting documents from a candidate set based on unique rating levels. Uses a new loss function and adapted RNN model for refining prediction scores.
result Models notably outperform state-of-the-art learning-to-rank models on four public datasets.
Advances in collaborative filtering and ranking methods.
problem Improving recommendation systems efficiency and accuracy.
method Graph information encoding, pairwise and listwise approaches, regularization techniques, personalization.
result New methods significantly improve recommendation system performance.
Unified transformer-based LT-TTD improves ranking efficiency and quality.
problem Decoupled L1 and L2 models in recommendation and search systems cause irreversible error propagation and suboptimal ranking.
method LT-TTD combines two-tower models with transformer expressivity in a unified listwise learning framework, providing theoretical guarantees and UPQE evaluation.
result LT-TTD reduces irretrievable relevant items and achieves better global optimization than disjoint training.
Framework learns sentence order from paragraphs using attention and transformer networks.
problem Learning to order sentences from a paragraph.
method Bidirectional sentence encoder and self-attention transformer network for ranking.
result Framework outperforms state-of-the-art methods on sentence ordering and discrimination tasks.
Proposes a cross entropy loss for better ranking algorithms.
problem Improving the theoretical understanding and performance of ranking algorithms.
method Introduces a cross entropy-based loss function that is a convex bound on NDCG and consistent with NDCG.
result Empirically, the proposed method outperforms existing algorithms in quality and robustness.
Learning to rank is a supervised learning problem where the output space is the space of rankings but the supervision space is the space of relevance scores. We make theoretical contributions to the learning to rank problem both in the online and batch settings. First, we propose a perceptron-like algorithm for learnin…
New algorithm predicts ranked stock lists for long-short portfolios.
problem Constructing effective long-short stock portfolios using machine learning.
method Proposes a new listwise learn-to-rank loss function to emphasize top and bottom of a rank list.
result Demonstrates superior performance in constructing long-short portfolios with a 38% annual return.
Study on missing data mechanisms and simple imputation methods in fairness of machine learning algorithms.
problem Impact of missing data mechanisms and simple imputation methods on fairness of machine learning algorithms.
method Three popular datasets for classification fairness were used. Missing values were generated using three missing data mechanisms. Various missing data handling techniques (listwise deletion, mean imputation, mode imputation, multiple imputation) were applied to the datasets. Fairness was assessed using classification algorithms (random forests).
result Missing data mechanism does not significantly impact fairness; listwise deletion gives highest fairness on average.
Scorio.jl ranks systems from repeated tasks using various methods.
problem Evaluating and ranking systems from repeated responses to shared tasks.
method Common tensor-based interface for multiple ranking methods.
result Pilot experiments show stability and runtime scaling.
Perceptron is a classic online algorithm for learning a classification function. In this paper, we provide a novel extension of the perceptron algorithm to the learning to rank problem in information retrieval. We consider popular listwise performance measures such as Normalized Discounted Cumulative Gain (NDCG) and Av…
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.
A new method, VIF, calculates influence for non-decomposable losses efficiently.
problem Efficiently calculating influence for complex machine learning models with non-decomposable losses.
method Revisiting influence function from robust statistics, proposing Versatile Influence Function (VIF) for any non-decomposable loss.
result VIF method is up to 10^3 times faster than brute-force methods and closely matches influence results.
New algorithm improves asset ranking for better cross-sectional portfolios.
problem Sub-optimal ranking of assets in cross-sectional systematic strategies.
method Learning-to-rank algorithms to enhance portfolio construction.
result Modern machine learning ranking algorithms boost Sharpe Ratios by approximately threefold.
ADPO optimizes relative advantage in reinforcement learning from human feedback.
problem Optimizing policy alignment in reinforcement learning from human preferences.
method ADPO explicitly parameterizes the optimal structure through anchored logits, decoupling response quality from prior popularity.
result Empirically, ADPO achieves state-of-the-art performance on reasoning tasks, outperforming GRPO by 30.9 percent.
Proposes a method for explaining ranking decisions in learning systems.
problem Limited work on interpreting ranking decisions from learning systems.
method Model agnostic local explanation method using optimization to maximize validity.
result Approach outperforms other methods in validity across different LTR models.
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…
JPLink uses machine learning to match jobs with RIASEC labels.
problem Matching jobs with RIASEC labels requires significant manual effort.
method JPLink uses text content and O*NET knowledge to assign RIASEC labels to jobs.
result JPLink outperforms conventional baselines in matching jobs with RIASEC labels.
MiM-StocR combines momentum indicators and adaptive ranking loss for better stock recommendation.
problem Lack of simultaneous short-term trend and ranking prediction in stock recommendation models.
method Integrates momentum indicators and proposes Adaptive-k ApproxNDCG for ranking optimization.
result MiM-StocR outperforms state-of-the-art MTL baselines in stock recommendation.
Survey data imputation methods impact feature selection and importance assessment.
problem Impact of different imputation methods on feature selection and importance assessment in survey data.
method Investigated eight imputation methods (listwise deletion, MICE, missRanger, mixGBoost) and three learners (Random Forest, XGBoost, linear model) in a simulation study.
result Different imputation methods yield varying feature selection and importance assessments.
Paper ranks influential Tor Darknet onion domains using content features.
problem Measuring influence of criminal onion domains in Tor Darknet.
method Content-based features from multiple sources, Learning-to-Rank approach.
result Listwise approach outperforms other methods with NDCG of 0.95 for top-10.
Graph comparison ties to Alexandrov's theorems.
problem Graph comparison conditions on metric spaces.
method Proof of Alexandrov's implications from graph comparisons.
result Complete description of graphs with trivial comparisons.
In this work, we will verify some comparison results on Kahler manifolds. They are complex Hessian comparison for the distance function from a closed complex submanifold of a Kahler manifold with holomorphic bisectional curvature bounded below by a constant, eigenvalue comparison and volume comparison in terms of scala…
The paper extends volume comparison results to total σ_l-curvature.
problem Comparing total σ_l-curvature with σ_k-curvature.
method Volume comparison theorem extension to σ-curvature comparison.
result Comparison holds for metrics close to strictly stable positive Einstein metrics.
Paper extends curvature estimates to new tensor types.
problem Mean curvature and volume comparison estimates for integral generalized quasi-Einstein tensors.
method Extends existing comparison results to new tensor types.
result Global diameter estimates derived from comparison results.
New method improves prediction accuracy in comparison data.
problem Efficiently predicting outcomes in limited comparison data.
method Empirical Bayes shrinkage methods for pairwise uncertainty estimation.
result Empirical Bayes shrinkage outperforms standard methods in comparison data.
A new comparison theorem for geometric spaces.
problem Geometric space comparison theorems.
method Relative form of Toponogov comparison theorem.
result New geometric space comparison theorem established.
Paper investigates rigidity phenomena for weighted Ricci curvature bounds with Laplacian comparison theorem.
problem Investigating rigidity phenomena for weighted Ricci curvature bounds.
method Derived comparison geometric estimates and generalized for non-symmetric Laplacian.
result Obtained rigidity results for Laplacian comparison theorem, diameter comparisons, and volume comparisons.
Study on manifolds with density using modified Hessians for curvature comparison.
problem Developing comparison geometry on manifolds with density.
method Modified Hessian approach based on weighted sectional curvature framework.
result Derivation of Hessian comparison and shape operator comparison theorems.
Flatness of manifolds with open flat subsets proven using bipolar comparisons.
problem Conditions for flatness in Riemannian manifolds.
method Using (3,3)-bipolar comparisons and open flat subsets.
result Flatness of manifolds proven under specific conditions.
New model reduces bias in crowdsourced pairwise comparisons.
problem Crowdsourced pairwise comparisons are biased due to perceptual factors.
method factorBT model accounts for irrelevant factors affecting worker answers.
result factorBT produces more accurate rankings than previous models.
Researchers developed volume comparison theorems in Finsler spacetimes.
problem Volume comparison in Finsler spacetimes with specific curvature conditions.
method Riccati equation techniques applied to (1+n)-dimensional Lorentz--Finsler manifolds. result Established volume comparison theorems for standard sets in Lorentzian volumes (SCLVs).
The study establishes comparison theorems for weighted Finsler manifolds and spacetimes.
problem Analyzing weighted Finsler manifolds and spacetimes with curvature conditions.
method Using weight function and ε-range, the Bonnet-Myers theorem, Laplacian comparison theorem, and Bishop-Gromov volume comparison theorem are formulated. result New comparison theorems for weighted Finsler manifolds and spacetimes are derived, including those for weighted Riemannian manifolds.
Defines a new metric comparison related to optimal transport.
problem Metric comparison on Riemannian manifolds.
method Connections to continuity of optimal transport and MTW condition.
result Strong connections to optimal transport and MTW condition.
CV inference can be invalid for relatively unstable model comparisons.
problem The validity of cross-validation for model comparison is questioned when models are relatively unstable.
method The study proves that simple, individually stable models can generate relatively unstable comparisons, invalidating CV inference.
result The Lasso and soft-thresholding generate relatively unstable comparisons, invalidating CV inferences.
Paper proves a new volume comparison theorem for Riemannian manifolds.
problem Comparing volumes of boundaries in Riemannian manifolds.
method Inspired by Schur's theorem, applies to Riemannian manifolds with Ricci curvature.
result Provides a new Schur's type volume comparison theorem.
Proposes a revenue function to evaluate dendrograms from comparisons.
problem Evaluate dendrograms from comparisons without ground-truth.
method Introduces a new revenue function related to Dasgupta's cost.
result Revenue function allows meaningful evaluation of dendrograms.
We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given two instances is more likely to be positive. Learning from such oracles has multiple applications where obtaining direct labels is harder bu…
An algorithm finds approximate rankings from pairwise comparisons with near-optimal comparisons.
problem Ranking items based on pairwise comparisons with minimal comparisons.
method Active ranking algorithm that decides comparisons based on confidence intervals.
result The algorithm succeeds in recovering approximate rankings with near-optimal comparisons.