arXiv research

Develops comparison-based hierarchical clustering algorithms without object representations.

Study compares crowdsourcing with lab experiments using comparison-based psychophysics.

New models ensure monotonicity in preference learning, improving accuracy especially with limited data.

Proposes a revenue function to evaluate dendrograms from comparisons.

TripletBoost learns classifiers from noisy triplet comparisons.

Paper investigates monotonicity issues in AI preference learning.

There is increasing interest in learning algorithms that involve interaction between human and machine. Comparison-based queries are among the most natural ways to get feedback from humans. A challenge in designing comparison-based interactive learning algorithms is coping with noisy answers. The most common fix is to …

Efficiently finds target objects using comparisons without features, scalable in both blind and non-blind settings.

CPS methods improve sample efficiency in robotics.

We consider machine learning in a comparison-based setting where we are given a set of points in a metric space, but we have no access to the actual distances between the points. Instead, we can only ask an oracle whether the distance between two points $i$ and $j$ is smaller than the distance between the points $i$ an…

The problem of content search through comparisons has recently received considerable attention. In short, a user searching for a target object navigates through a database in the following manner: the user is asked to select the object most similar to her target from a small list of objects. A new object list is then p…

Experiments used in current continual learning research do not faithfully assess fundamental challenges of learning continually. Instead of assessing performance on challenging and representative experiment designs, recent research has focused on increased dataset difficulty, while still using flawed experiment set-ups…

Test log-likelihood comparisons can be misleading.

We consider the problem of search through comparisons, where a user is presented with two candidate objects and reveals which is closer to her intended target. We study adaptive strategies for finding the target, that require knowledge of rank relationships but not actual distances between objects. We propose a new str…

Deep neuroevolution, that is evolutionary policy search methods based on deep neural networks, have recently emerged as a competitor to deep reinforcement learning algorithms due to their better parallelization capabilities. However, these methods still suffer from a far worse sample efficiency. In this paper we invest…

In the context of post-hoc interpretability, this paper addresses the task of explaining the prediction of a classifier, considering the case where no information is available, neither on the classifier itself, nor on the processed data (neither the training nor the test data). It proposes an instance-based approach wh…

Assume we are given a set of items from a general metric space, but we neither have access to the representation of the data nor to the distances between data points. Instead, suppose that we can actively choose a triplet of items (A,B,C) and ask an oracle whether item A is closer to item B or to item C. In this paper,…

Paper optimizes PCA for fairness using MMD and Stiefel manifold optimization.

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

New framework estimates treatment effects based on preferences.

Interactive interface for specifying complex tasks through demonstrations.

Benchmark evaluates financial misinformation detection models, revealing weaknesses without external context.

Optimal model improves AUC, recall, and F1 score for class-imbalanced business risk.

We propose a new yet natural algorithm for learning the graph structure of general discrete graphical models (a.k.a. Markov random fields) from samples. Our algorithm finds the neighborhood of a node by sequentially adding nodes that produce the largest reduction in empirical conditional entropy; it is greedy in the se…

Paper compares feature selection methods using GCM and LOCO, showing GCM methods generally outperform LOCO.

Algorithm optimizes non-convex functions using dueling comparisons.

Statistical framework improves LLM chatbot ranking.

This paper shows neural networks can solve complex graph problems efficiently.

Physics-informed neural networks improve baryonic predictions from dark matter simulations.

New method improves ABC for Bayesian model comparison.

We consider the problem of approximate $K$-means clustering with outliers and side information provided by same-cluster queries and possibly noisy answers. Our solution shows that, under some mild assumptions on the smallest cluster size, one can obtain an $(1+ε)$-approximation for the optimal potential with probabilit…

Paper proposes a method to verify PINN fidelity using Fisher information from dynamical systems.