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Gaurav Aggarwal

8 accepted papers

2024

Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components

Soumyabrata Pal, Prateek Varshney, Gagan Madan, Prateek Jain, Abhradeep Thakurta, Gaurav Aggarwal +2
AISTATS 2024poster

Personalization of machine learning (ML) predictions for individual users/domains/enterprises is critical for practical recommendation systems. Standard personalization approaches involve learning a user/domain specific \emph{embedding} that is fed into a fixed global model which can be limiting. On…

Cited by 1Source ↗PDF ↗Scholar ↗
2023

A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations

Sohan Rudra, Saksham Goel, Anirban Santara, Claudio Gentile, Laurent Perron, Fei Xia +3
ICRA 2023poster

Object-goal navigation (Object-nav) entails searching, recognizing and navigating to a target object. Object-nav has been extensively studied by the Embodied-AI community, but most solutions are often restricted to considering static objects (e.g., television, fridge, etc.), We propose a modular fra…

Cited by 7Source ↗Scholar ↗
2023

Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks

Eeshaan Jain, Tushar Nandy, Gaurav Aggarwal, Ashish V. Tendulkar, Rishabh K Iyer, Abir De
NeurIPS 2023poster

Existing subset selection methods for efficient learning predominantly employ discrete combinatorial and model-specific approaches, which lack generalizability--- for each new model, the algorithm has to be executed from the beginning. Therefore, for an unseen architecture, one cannot use the subset…

Cited by 7Source ↗PDF ↗code ↗Scholar ↗
2023

Test-time Adaptation with Slot-Centric Models

Mihir Prabhudesai, Anirudh Goyal, Sujoy Paul, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Gaurav Aggarwal +3
ICML 2023poster

Current visual detectors, though impressive within their training distribution, often fail to parse out-of-distribution scenes into their constituent entities. Recent test-time adaptation methods use auxiliary self-supervised losses to adapt the network parameters to each test example independently…

Cited by 11Source ↗PDF ↗code ↗Scholar ↗
2022

Learning to Plan Variable Length Sequences of Actions with a Cascading Bandit Click Model of User Feedback

Anirban Santara, Gaurav Aggarwal, Shuai Li, Claudio Gentile
AISTATS 2022poster

Motivated by problems of ranking with partial information, we introduce a variant of the cascading bandit model that considers flexible length sequences with varying rewards and losses. We formulate two generative models for this problem within the generalized linear setting, and design and analyze…

Cited by 4Source ↗PDF ↗Scholar ↗
2022

Novel Class Discovery without Forgetting

K J Joseph, Sujoy Paul, Gaurav Aggarwal, Soma Biswas, Piyush Rai, Kai Han +1
ECCV 2022poster

"Humans possess an innate ability to identify and differentiate instances that they are not familiar with, by leveraging and adapting the knowledge that they have acquired so far. Importantly, they achieve this without deteriorating the performance on their earlier learning. Inspired by this, we ide…

Cited by 52Source ↗PDF ↗Scholar ↗
2021

Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare

Arpita Biswas, Gaurav Aggarwal, Pradeep Varakantham, Milind Tambe
IJCAI 2021poster

In many public health settings, it is important for patients to adhere to health programs, such as taking medications and periodic health checks. Unfortunately, beneficiaries may gradually disengage from such programs, which is detrimental to their health. A concrete example of gradual disengagement…

Cited by 58Source ↗PDF ↗Scholar ↗
2021

Learning to Select Exogenous Events for Marked Temporal Point Process

Ping Zhang, Rishabh K Iyer, Ashish V. Tendulkar, Gaurav Aggarwal, Abir De
NeurIPS 2021poster

Marked temporal point processes (MTPPs) have emerged as a powerful modeling tool for a wide variety of applications which are characterized using discrete events localized in continuous time. In this context, the events are of two types endogenous events which occur due to the influence of the previ…

Cited by 10Source ↗PDF ↗Scholar ↗