Search Results for author: Abir De

Found 31 papers, 15 papers with code

Retrieving Continuous Time Event Sequences using Neural Temporal Point Processes with Learnable Hashing

no code implementations13 Jul 2023 Vinayak Gupta, Srikanta Bedathur, Abir De

In detail, by CTES retrieval we mean that for an input query sequence, a retrieval system must return a ranked list of relevant sequences from a large corpus.

Point Processes Retrieval +1

Maximum Common Subgraph Guided Graph Retrieval: Late and Early Interaction Networks

no code implementations20 Oct 2022 Indradyumna Roy, Soumen Chakrabarti, Abir De

A common consideration for scoring similarity is the maximum common subgraph (MCS) between the query and corpus graphs, usually counting the number of common edges (i. e., MCES).


Neural Estimation of Submodular Functions with Applications to Differentiable Subset Selection

no code implementations20 Oct 2022 Abir De, Soumen Chakrabarti

We do not draw the concave function from a restricted family, but rather learn from data using a highly expressive neural network that implements a differentiable quadrature procedure.

Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processes

1 code implementation23 Jun 2022 Vinayak Gupta, Srikanta Bedathur, Sourangshu Bhattacharya, Abir De

In this work, we provide a novel unsupervised model and inference method for learning MTPP in presence of event sequences with missing events.

Point Processes Variational Inference

Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences

1 code implementation17 Feb 2022 Vinayak Gupta, Srikanta Bedathur, Abir De

To tackle this, we propose NEUROSEQRET which learns to retrieve and rank a relevant set of continuous-time event sequences for a given query sequence, from a large corpus of sequences.

Point Processes Retrieval

Learning to Select Exogenous Events for Marked Temporal Point Process

no code implementations NeurIPS 2021 Ping Zhang, Rishabh Iyer, Ashish Tendulkar, Gaurav Aggarwal, Abir De

Marked temporal point processes (MTPPs) have emerged as a powerful modelingtool for a wide variety of applications which are characterized using discreteevents localized in continuous time.

Point Processes

Global Convergence Using Policy Gradient Methods for Model-free Markovian Jump Linear Quadratic Control

no code implementations30 Nov 2021 Santanu Rathod, Manoj Bhadu, Abir De

Owing to the growth of interest in Reinforcement Learning in the last few years, gradient based policy control methods have been gaining popularity for Control problems as well.

Policy Gradient Methods

Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time

1 code implementation NeurIPS 2021 Anshul Nasery, Soumyadeep Thakur, Vihari Piratla, Abir De, Sunita Sarawagi

In several real world applications, machine learning models are deployed to make predictions on data whose distribution changes gradually along time, leading to a drift between the train and test distributions.

Counterfactual Explanations in Sequential Decision Making Under Uncertainty

1 code implementation NeurIPS 2021 Stratis Tsirtsis, Abir De, Manuel Gomez-Rodriguez

In this work, we initiate the development of methods to find counterfactual explanations for decision making processes in which multiple, dependent actions are taken sequentially over time.

Counterfactual Explanation Decision Making +1

Group Testing under Superspreading Dynamics

1 code implementation30 Jun 2021 Stratis Tsirtsis, Abir De, Lars Lorch, Manuel Gomez-Rodriguez

Testing is recommended for all close contacts of confirmed COVID-19 patients.

Training Data Subset Selection for Regression with Controlled Generalization Error

1 code implementation23 Jun 2021 Durga Sivasubramanian, Rishabh Iyer, Ganesh Ramakrishnan, Abir De

First, we represent this problem with simplified constraints using the dual of the original training problem and show that the objective of this new representation is a monotone and alpha-submodular function, for a wide variety of modeling choices.


Differentiable Learning Under Triage

2 code implementations NeurIPS 2021 Nastaran Okati, Abir De, Manuel Gomez-Rodriguez

However, the interplay between the prediction accuracy of the model and the human experts under algorithmic triage is not well understood.

GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training

3 code implementations27 Feb 2021 KrishnaTeja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Abir De, Rishabh Iyer

We show rigorous theoretical and convergence guarantees of the proposed algorithm and, through our extensive experiments on real-world datasets, show the effectiveness of our proposed framework.

Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach

no code implementations11 Feb 2021 Paramita Koley, Avirup Saha, Sourangshu Bhattacharya, Niloy Ganguly, Abir De

The networked opinion diffusion in online social networks (OSN) is often governed by the two genres of opinions - endogenous opinions that are driven by the influence of social contacts among users, and exogenous opinions which are formed by external effects like news, feeds etc.

Experimental Design

Long Horizon Forecasting With Temporal Point Processes

1 code implementation8 Jan 2021 Prathamesh Deshpande, Kamlesh Marathe, Abir De, Sunita Sarawagi

In recent years, marked temporal point processes (MTPPs) have emerged as a powerful modeling machinery to characterize asynchronous events in a wide variety of applications.

Point Processes

Classification Under Human Assistance

1 code implementation21 Jun 2020 Abir De, Nastaran Okati, Ali Zarezade, Manuel Gomez-Rodriguez

Experiments on synthetic and real-world data from several applications in medical diagnosis illustrate our theoretical findings and demonstrate that, under human assistance, supervised learning models trained to operate under different automation levels can outperform those trained for full automation as well as humans operating alone.

Classification General Classification +1

Learning to Switch Among Agents in a Team via 2-Layer Markov Decision Processes

no code implementations11 Feb 2020 Vahid Balazadeh, Abir De, Adish Singla, Manuel Gomez-Rodriguez

Reinforcement learning agents have been mostly developed and evaluated under the assumption that they will operate in a fully autonomous manner -- they will take all actions.

Autonomous Driving reinforcement-learning +1

Regression Under Human Assistance

1 code implementation6 Sep 2019 Abir De, Nastaran Okati, Paramita Koley, Niloy Ganguly, Manuel Gomez-Rodriguez

In this paper, we take a first step towards the development of machine learning models that are optimized to operate under different automation levels.

BIG-bench Machine Learning Medical Diagnosis +1

Can A User Anticipate What Her Followers Want?

no code implementations1 Sep 2019 Abir De, Adish Singla, Utkarsh Upadhyay, Manuel Gomez-Rodriguez

As a result, she may feel compelled to use the feedback she receives to (re-)estimate her followers' preferences and decides which stories to share next to receive more (positive) feedback.

Decision Making Two-sample testing

Differentially Private Link Prediction With Protected Connections

no code implementations20 Jul 2019 Abir De, Soumen Chakrabarti

Link prediction (LP) algorithms propose to each node a ranked list of nodes that are currently non-neighbors, as the most likely candidates for future linkage.

Learning-To-Rank Link Prediction

Consequential Ranking Algorithms and Long-term Welfare

no code implementations13 May 2019 Behzad Tabibian, Vicenç Gómez, Abir De, Bernhard Schölkopf, Manuel Gomez Rodriguez

Can we design ranking models that understand the consequences of their proposed rankings and, more importantly, are able to avoid the undesirable ones?


Stochastic Optimal Control of Epidemic Processes in Networks

no code implementations30 Oct 2018 Lars Lorch, Abir De, Samir Bhatt, William Trouleau, Utkarsh Upadhyay, Manuel Gomez-Rodriguez

We approach the development of models and control strategies of susceptible-infected-susceptible (SIS) epidemic processes from the perspective of marked temporal point processes and stochastic optimal control of stochastic differential equations (SDEs) with jumps.

Point Processes

Deep Reinforcement Learning of Marked Temporal Point Processes

1 code implementation NeurIPS 2018 Utkarsh Upadhyay, Abir De, Manuel Gomez-Rodriguez

In this paper, we address the above problem from the perspective of deep reinforcement learning of marked temporal point processes, where both the actions taken by an agent and the feedback it receives from the environment are asynchronous stochastic discrete events characterized using marked temporal point processes.

Marketing Point Processes +2

On the Complexity of Opinions and Online Discussions

1 code implementation19 Feb 2018 Utkarsh Upadhyay, Abir De, Aasish Pappu, Manuel Gomez-Rodriguez

Sports, and the Newsroom app suggest that unidimensional opinion models may often be unable to accurately represent online discussions, provide insights into human judgements and opinions, and show that our framework is able to circumvent language nuances such as sarcasm or humor by relying on human judgements instead of textual analysis.

Steering Social Activity: A Stochastic Optimal Control Point Of View

no code implementations19 Feb 2018 Ali Zarezade, Abir De, Utkarsh Upadhyay, Hamid R. Rabiee, Manuel Gomez-Rodriguez

At a network level, they may increase activity by incentivizing a few influential users to take more actions, which in turn will trigger additional actions by other users.

Point Processes

NeVAE: A Deep Generative Model for Molecular Graphs

2 code implementations14 Feb 2018 Bidisha Samanta, Abir De, Gourhari Jana, Pratim Kumar Chattaraj, Niloy Ganguly, Manuel Gomez-Rodriguez

Moreover, in contrast with the state of the art, our decoder is able to provide the spatial coordinates of the atoms of the molecules it generates.

Bayesian Optimization

Cheshire: An Online Algorithm for Activity Maximization in Social Networks

no code implementations6 Mar 2017 Ali Zarezade, Abir De, Hamid Rabiee, Manuel Gomez Rodriguez

Can we design an algorithm that finds when to incentivize users to take actions to maximize the overall activity in a social network?

Discriminative Link Prediction using Local Links, Node Features and Community Structure

no code implementations17 Oct 2013 Abir De, Niloy Ganguly, Soumen Chakrabarti

Apart from the new predictor, another contribution is a rigorous protocol for benchmarking and reporting LP algorithms, which reveals the regions of strengths and weaknesses of all the predictors studied here, and establishes the new proposal as the most robust.

Benchmarking Clustering +1

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