arXiv research

Paper proposes a combined model for better recommendation by integrating explicit and implicit feedbacks.

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…

Agents learn user preferences with less explicit feedback via spatial interface valuing.

A new learning method for prosthetic arms without explicit rewards.

We present the first real-world application of methods for improving neural machine translation (NMT) with human reinforcement, based on explicit and implicit user feedback collected on the eBay e-commerce platform. Previous work has been confined to simulation experiments, whereas in this paper we work with real logge…

New insights into cascade feedback linearization of control systems.

Recommenders personalize the web content by typically using collaborative filtering to relate users (or items) based on explicit feedback, e.g., ratings. The difficulty of collecting this feedback has recently motivated to consider implicit feedback (e.g., item consumption along with the corresponding time). In this pa…

We investigate contextual online learning with nonparametric (Lipschitz) comparison classes under different assumptions on losses and feedback information. For full information feedback and Lipschitz losses, we design the first explicit algorithm achieving the minimax regret rate (up to log factors). In a partial feedb…

The abstract explores connections between reinforcement learning, scaling, and diffusion.

The paper analyzes matrix completion with unlabeled implicit feedback and provides error bounds.

New method for online learning IC models with node-level feedback.

New algorithm allows IGL to work with action-inclusive feedback.

Reduces user feedback needed for accurate recommender systems.

In recent years, deep neural networks have yielded state-of-the-art performance on several tasks. Although some recent works have focused on combining deep learning with recommendation, we highlight three issues of existing models. First, these models cannot work on both explicit and implicit feedback, since the networ…

Proposes CNN-based analog CSI feedback for FDD MIMO-OFDM systems.

Robot learns user preferences from brain signals.

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

Families of explicit solutions are found to a nonlinear Black-Scholes equation which incorporates the feedback-effect of a large trader in case of market illiquidity. The typical solution of these families will have a payoff which approximates a strangle. These solutions were used to test numerical schemes for solving …

New oracle uses uncertainty for active classification with noisy feedback.

Proposes a new theoretical framework for PbRL that requires less human feedback.

Hybrid approach combines user feedback and machine learning for predicting user satisfaction.

Model analyzes OTC market making with reputation feedback.

Model analyzes how reputation feedback affects OTC market making.

Efficient algorithm for learning from indirect feedback in complex decision-making scenarios.

SetRank tackles collaborative ranking from implicit feedback using setwise Bayesian approach.

SCR diversifies recommendations by applying style transfer to user profiles.

New RLHF algorithm identifies optimal policies from human feedback without explicit reward inference.

This paper extends a Kyle model to include price-responsive traders, revealing new dynamics and equilibria.

QS-BO optimizes functions using only rank-based feedback.

We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode active learning method for binary classification, and apply it for acquiring meaningful user feedback in the context of content-based image retrieval. Instead of combining different heuristics such as uncertainty, diversity, or density, our met…

A new algorithm for conversational recommendation systems using dueling bandits in GLMs.

Algorithm identifies best arm in linked bandits with reduced feedback.

Proposes a meta-learning method to improve recommender systems with biased feedback.

AutoStan improves Bayesian models via predictive feedback.

The paper constructs optimal hedging strategies for options with price impact.

We consider a model of optimal investment and consumption with both habit formation and partial observations in incomplete Itô processes market. The investor chooses his consumption under the addictive habits constraint while only observing the market stock prices but not the instantaneous rate of return. Applying the …

Paper tackles offline preference-based RL with human feedback.

The paper tackles PAC ranking with subset-wise feedback, achieving optimal sample complexity.

Study controlled contagion with state-dependent killing, proving a comparison principle.

Plug-in method improves performative prediction accuracy.

Local PBO methods improve preferential BO in high-dimensional problems.

In recent years, Variational Autoencoders (VAEs) have been shown to be highly effective in both standard collaborative filtering applications and extensions such as incorporation of implicit feedback. We extend VAEs to collaborative filtering with side information, for instance when ratings are combined with explicit t…

Solves infinite horizon portfolio problem with path-dependent labor income.

Study learns optimal bidding strategy in auctions with dynamic values and aggregated feedback.

The paper enhances a virtual assistant's humor to improve user satisfaction.

Advanced and effective collaborative filtering methods based on explicit feedback assume that unknown ratings do not follow the same model as the observed ones (\emph{not missing at random}). In this work, we build on this assumption, and introduce a novel dynamic matrix factorization framework that allows to set an ex…

Quantum-enhanced metrology aims to estimate an unknown parameter such that the precision scales better than the shot-noise bound. Single-shot adaptive quantum-enhanced metrology (AQEM) is a promising approach that uses feedback to tweak the quantum process according to previous measurement outcomes. Techniques and form…

The paper tackles fair sequential decision making with biased linear bandit feedback.