arXiv research

Optimal privacy-preserving ranking from noisy comparisons.

Paper tackles clustering with ordinal comparisons, achieving near-optimal results.

Optimizes identifying top-k items from comparisons with minimal comparisons.

The paper examines how optimizer comparisons in deep learning are influenced by hyperparameter tuning.

Paper introduces an efficient comparison operator for robust multi-objective optimization with uncertain objectives.

This paper optimizes the number of comparisons needed to find the best k items from pairwise comparisons.

We define a new type of metric comparison similar to the comparison of Alexandrov. We show that it has strong connections to continuity of optimal transport between regular measures on a Riemannian manifold, in particular to the so called MTW condition introduced by Xi-Nan Ma, Neil Trudinger and Xu-Jia Wang.

We study the active learning problem of top-$k$ ranking from multi-wise comparisons under the popular multinomial logit model. Our goal is to identify the top-$k$ items with high probability by adaptively querying sets for comparisons and observing the noisy output of the most preferred item from each comparison. To ac…

Paper proposes a sequential statistical test for comparing imitation learning policies with near-optimal stopping.

This paper tackles bandit optimization with a new pairwise comparison oracle for unknown strongly concave functions.

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…

This paper provides lower bounds on the convergence rate of Derivative Free Optimization (DFO) with noisy function evaluations, exposing a fundamental and unavoidable gap between the performance of algorithms with access to gradients and those with access to only function evaluations. However, there are situations in w…

New algorithm learns human preferences from few comparisons efficiently.

This paper provides a block coordinate descent algorithm to solve unconstrained optimization problems. In our algorithm, computation of function values or gradients is not required. Instead, pairwise comparison of function values is used. Our algorithm consists of two steps; one is the direction estimate step and the o…

In a context where most published articles are devoted to the development of "new methods", comparison studies are generally appreciated by readers but surprisingly given poor consideration by many scientific journals. In connection with recent articles on over-optimism and epistemology published in Bioinformatics, thi…

In this paper we discuss an extension of Perelman's comparison for quadrangles. Among applications of this new comparison theorem, we study the equidistance evolution of hypersurfaces in Alexandrov spaces with non-negative curvature. We show that, in certain cases, the equidistance evolution of hypersurfaces become tot…

New algorithm ranks players from partial comparisons with optimal rate.

We prove a splitting theorem for Riemannian n-manifolds with scalar curvature bounded below by a negative constant and containing certain area-minimising hypersurfaces (Theorem 3). Thus we generalise [25,Theorem 3] by Nunes. This splitting result follows from an area comparison theorem for hypersurfaces with non-positi…

New model for pairwise comparisons without stochastic transitivity.

We prove comparison theorems for the sub-Riemannian distortion coefficients appearing in interpolation inequalities. These results, which are equivalent to a sub-Laplacian comparison theorem for the sub-Riemannian distance, are obtained by introducing a suitable notion of sub-Riemannian Bakry-Émery curvature. The model…

Algorithm optimizes non-convex functions using dueling comparisons.

Bispectral OT improves dataset comparison by preserving intrinsic coherence.

COPT optimizes graph distances via simultaneous optimal transport.

Existing ordinal embedding methods usually follow a two-stage routine: outlier detection is first employed to pick out the inconsistent comparisons; then an embedding is learned from the clean data. However, learning in a multi-stage manner is well-known to suffer from sub-optimal solutions. In this paper, we propose a…

TAO outperforms other decision tree algorithms in accuracy.

Pairwise comparison data arises in many domains, including tournament rankings, web search, and preference elicitation. Given noisy comparisons of a fixed subset of pairs of items, we study the problem of estimating the underlying comparison probabilities under the assumption of strong stochastic transitivity (SST). We…

A new noise model for preferential Bayesian optimization using user anchors.

Paper establishes statistical inference for pairwise comparison models.

A common problem in machine learning is to rank a set of n items based on pairwise comparisons. Here ranking refers to partitioning the items into sets of pre-specified sizes according to their scores, which includes identification of the top-k items as the most prominent special case. The score of a given item is defi…

The paper proves Laplacian comparison theorems for modified m-Bakry-Emery Ricci tensors on Riemannian manifolds.

New algorithm learns permutations mixtures with optimal sample complexity.

Duel-Evolve uses LLM self-preferences for test-time optimization of discrete outputs.

We consider data in the form of pairwise comparisons of n items, with the goal of precisely identifying the top k items for some value of k < n, or alternatively, recovering a ranking of all the items. We analyze the Copeland counting algorithm that ranks the items in order of the number of pairwise comparisons won, an…

We consider the problem of learning the qualities of a collection of items by performing noisy comparisons among them. Following the standard paradigm, we assume there is a fixed "comparison graph" and every neighboring pair of items in this graph is compared $k$ times according to the Bradley-Terry-Luce model (where t…

Paper proposes an active preference learning method using radial basis functions.

DECS tool assesses swap rates of DEXes and Fusion outperforms competitors.

Paper proves optimal systolic inequality for manifolds with positive triRic curvature.

Evidence Networks simplify Bayesian model comparison for complex models.

Bayesian optimization learns DM preferences for multi-outcome experiments.

Standard optimizers perform as well as LARS and LAMB at large batch sizes.

PDO optimizes LLM prompts without labels, improving performance.

Bayesian optimization with preference learning using monotonic neural networks.

The purpose of this paper is to generalize the regular Optimal Reduction Theorem to general proper Dirac actions, formulated both in terms of point and orbit reduction. A comparison to general standard singular Dirac reduction is given emphasizing the desingularization role played by optimal reduction.

Uncoupled regression is the problem to learn a model from unlabeled data and the set of target values while the correspondence between them is unknown. Such a situation arises in predicting anonymized targets that involve sensitive information, e.g., one's annual income. Since existing methods for uncoupled regression …

NetOTC compares and aligns directed or undirected networks via random walk transitions.

We address the problem of learning a ranking by using adaptively chosen pairwise comparisons. Our goal is to recover the ranking accurately but to sample the comparisons sparingly. If all comparison outcomes are consistent with the ranking, the optimal solution is to use an efficient sorting algorithm, such as Quicksor…

New study finds optimal hyperparameter tuning crucial for fair optimizer comparisons.

Study on deep neural networks for reward modeling with pairwise comparison data.