arXiv research

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

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 …

Contextual policy search (CPS) is a class of multi-task reinforcement learning algorithms that is particularly useful for robotic applications. A recent state-of-the-art method is Contextual Covariance Matrix Adaptation Evolution Strategies (C-CMA-ES). It is based on the standard black-box optimization algorithm CMA-ES…

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…

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…

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…

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

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

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…

We address the classical problem of hierarchical clustering, but in a framework where one does not have access to a representation of the objects or their pairwise similarities. Instead, we assume that only a set of comparisons between objects is available, that is, statements of the form "objects $i$ and $j$ are more …

Proposes a revenue function to evaluate dendrograms from comparisons.

Paper investigates monotonicity issues in AI preference learning.

We consider the problem of classification in a comparison-based setting: given a set of objects, we only have access to triplet comparisons of the form "object $x_i$ is closer to object $x_j$ than to object $x_k$." In this paper we introduce TripletBoost, a new method that can learn a classifier just from such triplet …

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…

Algorithm optimizes non-convex functions using dueling comparisons.

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.

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.

We aim at developing and improving the imbalanced business risk modeling via jointly using proper evaluation criteria, resampling, cross-validation, classifier regularization, and ensembling techniques. Area Under the Receiver Operating Characteristic Curve (AUC of ROC) is used for model comparison based on 10-fold cro…

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

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

A new language for neural architecture search decouples search spaces and algorithms.

Statistical framework improves LLM chatbot ranking.

This paper compares Grid Search, Random Search, and Genetic Algorithm for NAS.

Efficient search methods can outperform random search on challenging tasks.

A new framework generates large hierarchical search spaces for neural architectures.

MICO uses mutual information co-training to improve selective search efficiency.

This work recommends personalized search stories to users based on their interests.

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

A method to reduce memory usage in NAS by pruning the search space.

Paper uses CMAB to improve NAS efficiency and accuracy.

New agent learns from previous search spaces to improve NAS efficiency.

PGS uses neural networks to improve policies online without search trees.

New method reduces NAS search time and complexity.

Approaches to learning Bayesian networks from data typically combine a scoring function with a heuristic search procedure. Given a Bayesian network structure, many of the scoring functions derived in the literature return a score for the entire equivalence class to which the structure belongs. When using such a scoring…

FiGS searches over a larger space of architectures for efficient mobile models.

This work proposes searching for optimal operation distribution in neural architecture search.

CrossBeam learns to search more efficiently in program synthesis.

Deep-n-Cheap automates deep learning model search for low complexity.

Paper presents a new trie for integer sketches to improve similarity searches.

Improves diffusion model performance and efficiency through classical search.

einspace expands NAS search space to include diverse neural architectures.

We consider a framework for structured prediction based on search in the space of complete structured outputs. Given a structured input, an output is produced by running a time-bounded search procedure guided by a learned cost function, and then returning the least cost output uncovered during the search. This framewor…