arXiv research

Research characterizes learnability of multilabel ranking problems.

Paper characterizes minimax regret rates for online ranking with top-k feedback.

Improves relevancy of black-box anomaly detectors with user feedback.

GRM uses graph neural networks to score process activity relevance.

BERT learns claim descriptions to identify patent novelty.

In machine learning, the choice of a learning algorithm that is suitable for the application domain is critical. The performance metric used to compare different algorithms must also reflect the concerns of users in the application domain under consideration. In this work, we propose a novel probability-based performan…

Gradual pruning reduces inference cost by pruning least important channels during training.

SoulMate links short texts through multi-aspect embeddings.

Paper proposes a method to identify negative transfers in multitask learning using surrogate models.

The problem of explaining the behavior of deep neural networks has recently gained a lot of attention. While several attribution methods have been proposed, most come without strong theoretical foundations, which raises questions about their reliability. On the other hand, the literature on cooperative game theory sugg…

Paper aims to improve relevance of e-commerce search results.

New insights into cascade feedback linearization of control systems.

New method learns from either positive or negative feedback alone.

User preferences for items can be inferred from either explicit feedback, such as item ratings, or implicit feedback, such as rental histories. Research in collaborative filtering has concentrated on explicit feedback, resulting in the development of accurate and scalable models. However, since explicit feedback is oft…

This paper improves image retrieval accuracy through novel relevance feedback methods.

Choppy optimizes ranked list truncation using Transformer architecture.

Recommender systems recommend items more accurately by analyzing users' potential interest on different brands' items. In conjunction with users' rating similarity, the presence of users' implicit feedbacks like clicking items, viewing items specifications, watching videos etc. have been proved to be helpful for learni…

Study evaluates new models using human feedback from another model.

Develops a new model for RLHF accounting for partially observed states and intermediate feedback.

Classifier learns to ignore unreliable feedback from end users.

Study on sample complexity for pure exploration in feedback graph settings.

One-bit feedback suffices for a bandit problem's optimal strategy.

Advocates a local feedback approach for RL in unknown systems.

A method corrects feedback shift in predicting conversion rates with delayed feedback.

Study how communication and feedback graphs affect learning outcomes.

New method uses correlated auxiliary feedback to reduce regret in parameterized bandits.

Test for linearizing 2-input systems with 2D feedback.

The problem of feedback equivalence for control systems is considered. An algebra of differential invariants and criteria for the feedback equivalence for regular control systems are found.

Reduces user feedback needed for accurate recommender systems.

DeepJSCC-f uses feedback to improve image transmission quality.

We present a study on reinforcement learning (RL) from human bandit feedback for sequence-to-sequence learning, exemplified by the task of bandit neural machine translation (NMT). We investigate the reliability of human bandit feedback, and analyze the influence of reliability on the learnability of a reward estimator,…

New algorithm for recommending best arms with aggregated feedback.

Study of reinforcement learning with additional feedback observations.

CausalRM models rewards from user feedback, overcoming noise and bias.

Paper tackles noisy bandit feedback for multiclass classification.

We propose a generalization of the best arm identification problem in stochastic multi-armed bandits (MAB) to the setting where every pull of an arm is associated with delayed feedback. The delay in feedback increases the effective sample complexity of standard algorithms, but can be offset if we have access to partial…

Active learning framework for optimizing human preferences in reinforcement learning.

Easy conditions found for simplifying complex systems.

New method uses information bottleneck to quantify variable importance in neural networks.

Paper studies CLO with partial feedback, improving decision-making in uncertain contexts.

We use online convex optimization (OCO) for setpoint tracking with uncertain, flexible loads. We consider full feedback from the loads, bandit feedback, and two intermediate types of feedback: partial bandit where a subset of the loads are individually observed and the rest are observed in aggregate, and Bernoulli feed…

CAFL breaks feedback loops in recommender systems using causal inference.

We consider combinatorial online learning with subset choices when only relative feedback information from subsets is available, instead of bandit or semi-bandit feedback which is absolute. Specifically, we study two regret minimisation problems over subsets of a finite ground set $[n]$, with subset-wise relative prefe…

Unified framework for expert selection with bandit and lower-bound feedback.

Modified BP attribution methods often ignore later layers' information, leading to misleading explanations.

Layer-wise Relevance Propagation (LRP) and saliency maps have been recently used to explain the predictions of Deep Learning models, specifically in the domain of text classification. Given different attribution-based explanations to highlight relevant words for a predicted class label, experiments based on word deleti…

GAIF enhances online multiple testing with feedback, improving statistical power.

New algorithms handle online prediction with bandit and delayed feedback, improving regret bounds.