31 citations · 159 across the 14 of their papers we have counts for
14 papers
Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Dataset
Arun Tejasvi Chaganty, Megan Leszczynski, Shu Zhang +3
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
Megan Leszczynski, Shu Zhang, Ravi Ganti +4
Recommender systems are ubiquitous yet often difficult for users to control, and adjust if recommendation quality is poor. This has motivated conversational recommender systems (CR…
MuLan: A Joint Embedding of Music Audio and Natural Language
Qingqing Huang, Aren Jansen, Joonseok Lee +3
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. This pa…
A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option
P Sharoff, Nishant A. Mehta, Ravi Ganti
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 u…
Thompson Sampling for Dynamic Pricing
Ravi Ganti, Matyas Sustik, Quoc Tran +1
In this paper we apply active learning algorithms for dynamic pricing in a prominent e-commerce website. Dynamic pricing involves changing the price of items on a regular basis, an…
Active Algorithms For Preference Learning Problems with Multiple Populations
Aniruddha Bhargava, Ravi Ganti, Robert Nowak
In this paper we model the problem of learning preferences of a population as an active learning problem. We propose an algorithm can adaptively choose pairs of items to show to us…
On Learning High Dimensional Structured Single Index Models
Nikhil Rao, Ravi Ganti, Laura Balzano +2
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 combinati…
Matrix Completion Under Monotonic Single Index Models
Ravi Ganti, Laura Balzano, Rebecca Willett
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. In real-world settings, ho…
Learning Single Index Models in High Dimensions
Ravi Ganti, Nikhil Rao, Rebecca M. Willett +1
Single Index Models (SIMs) are simple yet flexible semi-parametric models for classification and regression. Response variables are modeled as a nonlinear, monotonic function of a…
Active Model Aggregation via Stochastic Mirror Descent
Ravi Ganti
We consider the problem of learning convex aggregation of models, that is as good as the best convex aggregation, for the binary classification problem. Working in the stream based…
Sparse Linear Regression With Missing Data
Ravi Ganti, Rebecca M. Willett
This paper proposes a fast and accurate method for sparse regression in the presence of missing data. The underlying statistical model encapsulates the low-dimensional structure of…
Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens
Ravi Ganti, Alexander G. Gray
In this paper we propose a multi-armed bandit inspired, pool based active learning algorithm for the problem of binary classification. By carefully constructing an analogy between…
Local Support Vector Machines:Formulation and Analysis
Ravi Ganti, Alexander Gray
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…
UPAL: Unbiased Pool Based Active Learning
Ravi Ganti, Alexander Gray
In this paper we address the problem of pool based active learning, and provide an algorithm, called UPAL, that works by minimizing the unbiased estimator of the risk of a hypothes…