A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The paper proposes methods to identify and sample from mixtures of Mallows models for top-k rankings.
This paper explores the preference-based top- rank aggregation problem. Suppose that a collection of items is repeatedly compared in pairs, and one wishes to recover a consistent ordering that emphasizes the top- ranked items, based on partially revealed preferences. We focus on the Bradley-Terry-Luce (BTL) model…
We study the top- ranking problem where the goal is to recover the set of top- ranked items out of a large collection of items based on partially revealed preferences. We consider an adversarial crowdsourced setting where there are two population sets, and pairwise comparison samples drawn from one of the populat…
Motivated by applications in recommender systems, web search, social choice and crowdsourcing, we consider the problem of identifying the set of top items from noisy pairwise comparisons. In our setting, we are non-actively given pairwise comparisons between each pair of items, where each comparison has noi…
We present online boosting algorithms for multilabel ranking with top-k feedback, where the learner only receives information about the top k items from the ranking it provides. We propose a novel surrogate loss function and unbiased estimator, allowing weak learners to update themselves with limited information. Using…
Optimizes ranking of top-k players from partial comparison data.
Optimizes identifying top-k items from comparisons with minimal comparisons.
Paper extends top-k Mallows model for better user preference analysis.
This paper compares average-K and top-K classification methods under ambiguity.
Recurrent models can produce infinite sequences, causing bias; new methods prevent this.
Top-k Combinatorial Bandits generalize multi-armed bandits, where at each round any subset of out of arms may be chosen and the sum of the rewards is gained. We address the full-bandit feedback, in which the agent observes only the sum of rewards, in contrast to the semi-bandit feedback, in which the agent obse…
We explore the top- rank aggregation problem. Suppose a collection of items is compared in pairs repeatedly, and we aim to recover a consistent ordering that focuses on the top- ranked items based on partially revealed preference information. We investigate the Bradley-Terry-Luce model in which one ranks items ac…
A simple modification improves GAN performance by discarding bad samples.
New insights into the top-K sparse softmax gating function for deep learning.
This paper explores the adaptive (active) PAC (probably approximately correct) top- ranking (i.e., top- item selection) and total ranking problems from -wise () comparisons under the multinomial logit (MNL) model. By adaptively choosing sets to query and observing the noisy output of the most favored …
The well-known Gumbel-Max trick for sampling from a categorical distribution can be extended to sample elements without replacement. We show how to implicitly apply this 'Gumbel-Top-' trick on a factorized distribution over sequences, allowing to draw exact samples without replacement using a Stochastic Beam Sea…
Paper analyzes trade-offs in top-k classification accuracies and proposes a new loss function.
Class ambiguity is typical in image classification problems with a large number of classes. When classes are difficult to discriminate, it makes sense to allow k guesses and evaluate classifiers based on the top-k error instead of the standard zero-one loss. We propose top-k multiclass SVM as a direct method to optimiz…
The top- error is often employed to evaluate performance for challenging classification tasks in computer vision as it is designed to compensate for ambiguity in ground truth labels. This practical success motivates our theoretical analysis of consistent top- classification. Surprisingly, it is not rigorously und…
New approaches estimate recommendation metrics using sampling.
Paper introduces a new loss function for deep imbalanced classification.
Smoothed top-k operator improves model training efficiency.
We study the active learning problem of top- ranking from multi-wise comparisons under the popular multinomial logit model. Our goal is to identify the top- 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 introduces efficient top-k selection with differential privacy.
We introduce a new sampling method for large language models that balances diversity and parallelism.
In order to push the performance on realistic computer vision tasks, the number of classes in modern benchmark datasets has significantly increased in recent years. This increase in the number of classes comes along with increased ambiguity between the class labels, raising the question if top-1 error is the right perf…
Paper characterizes minimax regret rates for online ranking with top-k feedback.
Unified model for prediction and deferral selects top-k entities efficiently.
Top-k error is currently a popular performance measure on large scale image classification benchmarks such as ImageNet and Places. Despite its wide acceptance, our understanding of this metric is limited as most of the previous research is focused on its special case, the top-1 error. In this work, we explore two direc…
Master-slave architecture tackles combinatorial multi-armed bandits with diversity constraints.
Paper introduces MPES for top-k ranking BO with preferential observations.
Study improves top-k set prediction with low cardinality.
This paper is concerned with the problem of top- ranking from pairwise comparisons. Given a collection of items and a few pairwise comparisons across them, one wishes to identify the set of items that receive the highest ranks. To tackle this problem, we adopt the logistic parametric model --- the Bradley-Te…
Proposes differentiable and sparse top-k operators for neural networks.
New method reduces variance in estimating PL model expectations.
Study on top- classification with new loss functions and algorithms.
Unified framework for deferring queries to top-k experts, improving accuracy-cost trade-offs.
We propose the Limited Multi-Label (LML) projection layer as a new primitive operation for end-to-end learning systems. The LML layer provides a probabilistic way of modeling multi-label predictions limited to having exactly k labels. We derive efficient forward and backward passes for this layer and show how the layer…
New method makes CNN interpretations robust to adversarial attacks.
Introduces top- regularization for better feature selection in machine learning.
New methods provide stable ranking without assumptions on data distributions.
Distributed stochastic gradient descent (SGD) algorithms are widely deployed in training large-scale deep learning models, while the communication overhead among workers becomes the new system bottleneck. Recently proposed gradient sparsification techniques, especially Top- sparsification with error compensation (To…
We propose a novel technique for analyzing adaptive sampling called the {\em Simulator}. Our approach differs from the existing methods by considering not how much information could be gathered by any fixed sampling strategy, but how difficult it is to distinguish a good sampling strategy from a bad one given the limit…
It is well-known that classifiers are vulnerable to adversarial perturbations. To defend against adversarial perturbations, various certified robustness results have been derived. However, existing certified robustnesses are limited to top-1 predictions. In many real-world applications, top- predictions are more rel…
The paper addresses calibration in label ranking, a structured prediction task.
Paper tackles ranking items with a semi-random comparison graph and a monotone adversary.
RAMPART ranks top-k features more accurately than existing methods.
Proposes a method to infer ranking properties and top-K rankings with uncertainty quantification.