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Found 13 papers, 1 papers with code
Experimental Design for Active Transductive Inference in Large Language Models
We design the LLM prompt by adaptively choosing few-shot examples for a given inference query.
Logic-Scaffolding: Personalized Aspect-Instructed Recommendation Explanation Generation using LLMs
The unique capabilities of Large Language Models (LLMs), such as the natural language text generation ability, position them as strong candidates for providing explanation for recommendations.
Fixed-Budget Best-Arm Identification with Heterogeneous Reward Variances
Our algorithms rely on non-uniform budget allocations among the arms where the arms with higher reward variances are pulled more often than those with lower variances.
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems
Recurrent neural networks have proven effective in modeling sequential user feedbacks for recommender systems.
Bridging Recommendation and Marketing via Recurrent Intensity Modeling
RIM allows us to rethink recommendation in a Matching (Mtch) scenario, where the benefits of the users (e. g., ItemRec relevance) and item providers (e. g., item-exposure guarantees) are considered at the same time.
Language Models as Recommender Systems: Evaluations and Limitations
Pre-trained language models (PLMs) such as BERT and GPT learn general text representations and encode extensive world knowledge; thus, they can be efficiently and accurately adapted to various downstream tasks.
Zero-Shot Recommender Systems
This poses a chicken-and-egg problem for early-stage products, whose amount of data, in turn, relies on the performance of their RS.
Recurrent Exploration Networks for Recommender Systems
Recurrent neural networks have proven effective in modeling sequential user feedbacks for recommender systems.
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling
To solve this problem, we consider a marginalized importance sampling (MIS) estimator that recursively estimates the state marginal distribution for the target policy at every step.
Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of Resources
One difficulty we observe is that the noise in the stochastic momentum estimation is accumulated over time and will have delayed effects when the batch size changes.
Imitation-Regularized Offline Learning
To solve both problems, we show how one can use policy improvement (PIL) objectives, regularized by policy imitation (IML).
Active Search for Sparse Signals with Region Sensing
Autonomous systems can be used to search for sparse signals in a large space; e. g., aerial robots can be deployed to localize threats, detect gas leaks, or respond to distress calls.
Σ-Optimality for Active Learning on Gaussian Random Fields
For active learning on GRFs, the commonly used V-optimality criterion queries nodes that reduce the L2 (regression) loss.
