21 citations · 47 across the 19 of their papers we have counts for
9 papers · 1 filter
Algorithmic Guarantees for Distilling Supervised and Offline RL Datasets
Aaryan Gupta, Rishi Saket, Aravindan Raghuveer
Given a training dataset, the goal of dataset distillation is to derive a synthetic dataset such that models trained on the latter perform as well as those trained on the training…
Dense and Diverse Goal Coverage in Multi Goal Reinforcement Learning
Sagalpreet Singh, Rishi Saket, Aravindan Raghuveer
Reinforcement Learning algorithms are primarily focused on learning a policy that maximizes expected return. As a result, the learned policy can exploit one or few reward sources.…
Aggregating Data for Optimal and Private Learning
Sushant Agarwal, Yukti Makhija, Rishi Saket +1
Multiple Instance Regression (MIR) and Learning from Label Proportions (LLP) are learning frameworks arising in many applications, where the training data is partitioned into disjo…
Weak to Strong Learning from Aggregate Labels
Yukti Makhija, Rishi Saket
In learning from aggregate labels, the training data consists of sets or "bags" of feature-vectors (instances) along with an aggregate label for each bag derived from the (usually…
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…
Label Differential Privacy via Aggregation
Anand Brahmbhatt, Rishi Saket, Shreyas Havaldar +3
This paper explores the use of linear aggregation to protect the privacy of sensitive training labels through the concept of \emph{label differential privacy} (label-DP) while main…
PAC Learning Linear Thresholds from Label Proportions
Anand Brahmbhatt, Rishi Saket, Aravindan Raghuveer
Learning from label proportions (LLP) is a generalization of supervised learning in which the training data is available as sets or bags of feature-vectors (instances) along with t…
LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions
Anand Brahmbhatt, Mohith Pokala, Rishi Saket +1
In the task of Learning from Label Proportions (LLP), a model is trained on groups (a.k.a bags) of instances and their corresponding label proportions to predict labels for individ…
Multi-Variate Time Series Forecasting on Variable Subsets
Jatin Chauhan, Aravindan Raghuveer, Rishi Saket +2
We formulate a new inference task in the domain of multivariate time series forecasting (MTSF), called Variable Subset Forecast (VSF), where only a small subset of the variables is…