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Found 14 papers, 2 papers with code
Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Dataset
Users in consumption domains, like music, are often able to more efficiently provide preferences over a set of items (e. g. a playlist or radio) than over single items (e. g. songs).
Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation
This has motivated conversational recommender systems (CRSs), with control provided through natural language feedback.
MAQA: A Multimodal QA Benchmark for Negation
Multimodal learning can benefit from the representation power of pretrained Large Language Models (LLMs).
MuLan: A Joint Embedding of Music Audio and Natural Language
Music tagging and content-based retrieval systems have traditionally been constructed using pre-defined ontologies covering a rigid set of music attributes or text queries.
A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option
We consider a sequential decision-making problem where an agent can take one action at a time and each action has a stochastic temporal extent, i. e., a new action cannot be taken until the previous one is finished.
Thompson Sampling for Dynamic Pricing
We show that one can use active learning algorithms such as Thompson sampling to more efficiently learn the underlying parameters in a pricing problem.
Active Algorithms For Preference Learning Problems with Multiple Populations
In this paper we model the problem of learning preferences of a population as an active learning problem.
On Learning High Dimensional Structured Single Index Models
Single Index Models (SIMs) are simple yet flexible semi-parametric models for machine learning, where the response variable is modeled as a monotonic function of a linear combination of features.
Matrix Completion Under Monotonic Single Index Models
Most recent results in matrix completion assume that the matrix under consideration is low-rank or that the columns are in a union of low-rank subspaces.
Learning Single Index Models in High Dimensions
Single Index Models (SIMs) are simple yet flexible semi-parametric models for classification and regression.
Sparse Linear Regression With Missing Data
This paper proposes a fast and accurate method for sparse regression in the presence of missing data.
Active Model Aggregation via Stochastic Mirror Descent
We consider the problem of learning convex aggregation of models, that is as good as the best convex aggregation, for the binary classification problem.
Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens
The design of this sampling distribution is also inspired by the analogy between active learning and multi-armed bandits.
Local Support Vector Machines:Formulation and Analysis
We provide a formulation for Local Support Vector Machines (LSVMs) that generalizes previous formulations, and brings out the explicit connections to local polynomial learning used in nonparametric estimation literature.
