10 citations · 10 across the 4 of their papers we have counts for
5 papers
Conditional Flow Models Convert Epistemic Uncertainty into Apparent Aleatoric Variance
Navodita Sharma, Abhishek Shetty, Max Simchowitz +1
Conditional Flow Models (CFMs) are increasingly being used in place of deterministic predictors across machine learning, including for tasks that were classically formulated as reg…
Preserving Expert-Level Privacy in Offline Reinforcement Learning
Navodita Sharma, Vishnu Vinod, Abhradeep Thakurta +4
The offline reinforcement learning (RL) problem aims to learn an optimal policy from historical data collected by one or more behavioural policies (experts) by interacting with an…
Learning from Label Proportions and Covariate-shifted Instances
Sagalpreet Singh, Navodita Sharma, Shreyas Havaldar +2
In many applications, especially due to lack of supervision or privacy concerns, the training data is grouped into bags of instances (feature-vectors) and for each bag we have only…
Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation
Shreyas Havaldar, Navodita Sharma, Shubhi Sareen +2
Learning from Label Proportions (LLP) is a learning problem where only aggregate level labels are available for groups of instances, called bags, during training, and the aim is to…
Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution
Sankalp Garg, Navodita Sharma, Woojeong Jin +1
Time series prediction is an important problem in machine learning. Previous methods for time series prediction did not involve additional information. With a lot of dynamic knowle…