Abhijin Adiga — accepted AI-conference papers

2026

AG-STELLA: Spatio-Temporal Learning for Water-related Agricultural Land Use Activity Mapping with AlphaEarth

Nibir Chandra Mandal, Oishee Bintey Hoque, Kyle Luong, Samarth Swarup, Kirti Rajagopalan, Mandy Wilson +2

IJCAI 2026

Accurate mapping of agricultural land use activity, particularly long-term transition from cropland to pasture and short-term transition between cropland to fallow land, is essential for sustainable water management, drought response, and food-system resilience which directly supports United Nations

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2026

Information Theoretic Optimal Surveillance for Epidemic Prevalence in Networks

Ritwick Mishra, Abhijin Adiga, Madhav Marathe, S. S. Ravi, Ravi Tandon, Anil Vullikanti

AAAI 2026technical

Estimating the true prevalence of an epidemic outbreak is a key public health problem. This is challenging because surveillance is usually resource intensive and biased. In the network setting, prior work on cost sensitive disease surveillance has focused on choosing a subset of individuals (or node

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2025

A Unifying Information-theoretic Perspective on Evaluating Generative Models

Alexis Fox, Samarth Swarup, Abhijin Adiga

AAAI 2025technical

Considering the difficulty of interpreting generative model output, there is significant current research focused on determining meaningful evaluation metrics. Several recent approaches utilize "precision" and "recall," borrowed from the classification domain, to individually quantify the output fid…

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2025

IGraSS: Learning to Identify Infrastructure Networks from Satellite Imagery by Iterative Graph-constrained Semantic Segmentation

Oishee Bintey Hoque, Abhijin Adiga, Aniruddha Adiga, Siddharth Chaudhary, Madhav V. Marathe, S.S. Ravi +3

IJCAI 2025

Accurate canal network mapping is essential for water management, including irrigation planning and infrastructure maintenance. State-of-the-art semantic segmentation models for infrastructure mapping, such as roads, rely on large, well-annotated remote sensing datasets. However, incomplete or inade

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2025

IRRISIGHT: A Large-Scale Multimodal Dataset and Scalable Pipeline to Address Irrigation and Water Management in Agriculture

Nibir Chandra Mandal, Oishee Bintey Hoque, Mandy L Wilson, Samarth Swarup, Sayjro K Nouwakpo, Abhijin Adiga +1

NeurIPS 2025poster

The lack of fine-grained, large-scale datasets on water availability presents a critical barrier to applying machine learning (ML) for agricultural water management. Since there are multiple natural and anthropogenic factors that influence water availability, incorporating diverse multimodal feature…

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2025

Knowledge-Informed Deep Learning for Irrigation Type Mapping from Remote Sensing

Oishee Bintey Hoque, Nibir Chandra Mandal, Abhijin Adiga, Samarth Swarup, Sayjro Kossi Nouwakpo, Amanda Wilson +1

IJCAI 2025

Accurate mapping of irrigation methods is crucial for sustainable agricultural practices and food systems. However, existing models that rely solely on spectral features from satellite imagery are ineffective due to the complexity of agricultural landscapes and limited training data, making this a c

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2024

Efficient PAC Learnability of Dynamical Systems Over Multilayer Networks

Zirou Qiu, Abhijin Adiga, Madhav Marathe, S. S. Ravi, Daniel Rosenkrantz, Richard Stearns +1

ICML 2024poster

Networked dynamical systems are widely used as formal models of real-world cascading phenomena, such as the spread of diseases and information. Prior research has addressed the problem of learning the behavior of an unknown dynamical system when the underlying network has a single layer. In this wor…

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2024

Learning the Topology and Behavior of Discrete Dynamical Systems

Zirou Qiu, Abhijin Adiga, Madhav V. Marathe, S. S. Ravi, Daniel J. Rosenkrantz, Richard E. Stearns +1

AAAI 2024technical

Discrete dynamical systems are commonly used to model the spread of contagions on real-world networks. Under the PAC framework, existing research has studied the problem of learning the behavior of a system, assuming that the underlying network is known. In this work, we focus on a more challenging…

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2023

Reconstructing an Epidemic Outbreak Using Steiner Connectivity

Ritwick Mishra, Jack Heavey, Gursharn Kaur, Abhijin Adiga, Anil Vullikanti

AAAI 2023technical

Only a subset of infections is actually observed in an outbreak, due to multiple reasons such as asymptomatic cases and under-reporting. Therefore, reconstructing an epidemic cascade given some observed cases is an important step in responding to such an outbreak. A maximum likelihood solution to th…

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2023

Resource Sharing through Multi-Round Matchings

Yohai Trabelsi, Abhijin Adiga, Sarit Kraus, S. S. Ravi, Daniel J. Rosenkrantz

AAAI 2023technical

Applications such as employees sharing office spaces over a workweek can be modeled as problems where agents are matched to resources over multiple rounds. Agents' requirements limit the set of compatible resources and the rounds in which they want to be matched. Viewing such an application as a mu…

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2022

Efficiently Learning the Topology and Behavior of a Networked Dynamical System Via Active Queries

Daniel J Rosenkrantz, Abhijin Adiga, Madhav Marathe, Zirou Qiu, S S Ravi, Richard Stearns +1

ICML 2022spotlight

Using a discrete dynamical system model, many papers have addressed the problem of learning the behavior (i.e., the local function at each node) of a networked system through active queries, assuming that the network topology is known. We address the problem of inferring both the topology of the net…

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2020

Boolean Games: Inferring Agents' Goals Using Taxation Queries

Abhijin Adiga, Sarit Kraus, Oleg Maksimov, S. S. Ravi

IJCAI 2020poster

In Boolean games, each agent controls a set of Boolean variables and has a goal represented by a propositional formula. We study inference problems in Boolean games assuming the presence of a PRINCIPAL who has the ability to control the agents and impose taxation schemes. Previous work used t…

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2019

PAC Learnability of Node Functions in Networked Dynamical Systems

Abhijin Adiga, Chris J Kuhlman, Madhav Marathe, S Ravi, Anil Vullikanti

ICML 2019oral

We consider the PAC learnability of the local functions at the vertices of a discrete networked dynamical system, assuming that the underlying network is known. Our focus is on the learnability of threshold functions. We show that several variants of threshold functions are PAC learnable and provide…

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