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Found 8 papers, 1 papers with code
Offline Multi-task Transfer RL with Representational Penalization
We study the problem of representation transfer in offline Reinforcement Learning (RL), where a learner has access to episodic data from a number of source tasks collected a priori, and aims to learn a shared representation to be used in finding a good policy for a target task.
Initializing Services in Interactive ML Systems for Diverse Users
(ii) Suboptimal Local Solutions: The total loss (sum of loss functions across all users and all services) landscape is not convex even if the individual losses on a single service are convex, making it likely for the learning dynamics to get stuck in local minima.
Scalable Distributional Robustness in a Class of Non Convex Optimization with Guarantees
Distributionally robust optimization (DRO) has shown lot of promise in providing robustness in learning as well as sample based optimization problems.
Multiscale Generative Models: Improving Performance of a Generative Model Using Feedback from Other Dependent Generative Models
Recent work has used generative models (GANs in particular) for providing high-fidelity simulation of real-world systems.
Conditional Expectation based Value Decomposition for Scalable On-Demand Ride Pooling
Owing to the benefits for customers (lower prices), drivers (higher revenues), aggregation companies (higher revenues) and the environment (fewer vehicles), on-demand ride pooling (e. g., Uber pool, Grab Share) has become quite popular.
Changepoint Analysis of Topic Proportions in Temporal Text Data
Changepoint analysis deals with unsupervised detection and/or estimation of time-points in time-series data, when the distribution generating the data changes.
NeurInt : Learning to Interpolate through Neural ODEs
In this work, we propose a novel generative model that learns a flexible non-parametric prior over interpolation trajectories, conditioned on a pair of source and target images.
NeurInt-Learning Interpolation by Neural ODEs
A range of applications require learning image generation models whose latent space effectively captures the high-level factors of variation in the data distribution, which can be judged by its ability to interpolate between images smoothly.
