7.7k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Harish-Chandra Research InstituteIN27 papers
- International Institute of Information TechnologyIN23 papers
- Homi Bhabha National InstituteIN20 papers
- Indian Institute of Science Education and Research ThiruvananthapuramIN20 papers
- Indian Institute of Science BangaloreIN15 papers
- Indian Institute of Technology HyderabadIN14 papers
- Institute of Physics, BhubaneshwarIN11 papers
- Birla Institute of Technology and Science, PilaniIN10 papers
- National University of SingaporeSG9 papers
- Indian Institute of Technology KanpurIN8 papers
- Université Libre de BruxellesBE8 papers
- Birla Institute of Technology and Science - Hyderabad CampusIN7 papers
22 papers · 1 filter
Remote sensing framework for geological mapping via stacked autoencoders and clustering
Sandeep Nagar, Ehsan Farahbakhsh, Joseph Awange +1
Supervised machine learning methods for geological mapping via remote sensing face limitations due to the scarcity of accurately labelled training data that can be addressed by uns…
Reappraising Domain Generalization in Neural Networks
Sarath Sivaprasad, Akshay Goindani, Vaibhav Garg +3
Given that Neural Networks generalize unreasonably well in the IID setting (with benign overfitting and betterment in performance with more parameters), OOD presents a consistent f…
Sleeping Combinatorial Bandits
Kumar Abhishek, Ganesh Ghalme, Sujit Gujar +1
In this paper, we study an interesting combination of sleeping and combinatorial stochastic bandits. In the mixed model studied here, at each discrete time instant, an arbitrary \e…
No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks
Shyamgopal Karthik, Ameya Prabhu, Puneet K. Dokania +1
There has been increasing interest in building deep hierarchy-aware classifiers that aim to quantify and reduce the severity of mistakes, and not just reduce the number of errors.…
Reducing the Variance of Variational Estimates of Mutual Information by Limiting the Critic's Hypothesis Space to RKHS
P Aditya Sreekar, Ujjwal Tiwari, Anoop Namboodiri
Mutual information (MI) is an information-theoretic measure of dependency between two random variables. Several methods to estimate MI, from samples of two random variables with un…
Maximum Reward Formulation In Reinforcement Learning
Sai Krishna Gottipati, Yashaswi Pathak, Rohan Nuttall +6
Reinforcement learning (RL) algorithms typically deal with maximizing the expected cumulative return (discounted or undiscounted, finite or infinite horizon). However, several cruc…
Off-policy Bandits with Deficient Support
Noveen Sachdeva, Yi Su, Thorsten Joachims
Learning effective contextual-bandit policies from past actions of a deployed system is highly desirable in many settings (e.g. voice assistants, recommendation, search), since it…
Learning Multiclass Classifier Under Noisy Bandit Feedback
Mudit Agarwal, Naresh Manwani
This paper addresses the problem of multiclass classification with corrupted or noisy bandit feedback. In this setting, the learner may not receive true feedback. Instead, it recei…
Robust Deep Ordinal Regression Under Label Noise
Bhanu Garg, Naresh Manwani
The real-world data is often susceptible to label noise, which might constrict the effectiveness of the existing state of the art algorithms for ordinal regression. Existing works…
Ballooning Multi-Armed Bandits
Ganesh Ghalme, Swapnil Dhamal, Shweta Jain +2
In this paper, we introduce Ballooning Multi-Armed Bandits (BL-MAB), a novel extension of the classical stochastic MAB model. In the BL-MAB model, the set of available arms grows (…
Value-of-Information based Arbitration between Model-based and Model-free Control
Krishn Bera, Yash Mandilwar, Bapi Raju
There have been numerous attempts in explaining the general learning behaviours using model-based and model-free methods. While the model-based control is flexible yet computationa…
Generating Counterfactual and Contrastive Explanations using SHAP
Shubham Rathi
With the advent of GDPR, the domain of explainable AI and model interpretability has gained added impetus. Methods to extract and communicate visibility into decision-making models…
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
Philipp Becker, Harit Pandya, Gregor Gebhardt +3
In order to integrate uncertainty estimates into deep time-series modelling, Kalman Filters (KFs) (Kalman et al., 1960) have been integrated with deep learning models, however, suc…
PLUME: Polyhedral Learning Using Mixture of Experts
Kulin Shah, P. S. Sastry, Naresh Manwani
In this paper, we propose a novel mixture of expert architecture for learning polyhedral classifiers. We learn the parameters of the classifierusing an expectation maximization alg…
Neural Network Attributions: A Causal Perspective
Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar +1
We propose a new attribution method for neural networks developed using first principles of causality (to the best of our knowledge, the first such). The neural network architectur…
Sequential Variational Autoencoders for Collaborative Filtering
Noveen Sachdeva, Giuseppe Manco, Ettore Ritacco +1
Variational autoencoders were proven successful in domains such as computer vision and speech processing. Their adoption for modeling user preferences is still unexplored, although…
FNNC: Achieving Fairness through Neural Networks
Padala Manisha, Sujit Gujar
In classification models fairness can be ensured by solving a constrained optimization problem. We focus on fairness constraints like Disparate Impact, Demographic Parity, and Equa…
Attention-based Neural Text Segmentation
Pinkesh Badjatiya, Litton J Kurisinkel, Manish Gupta +1
Text segmentation plays an important role in various Natural Language Processing (NLP) tasks like summarization, context understanding, document indexing and document noise removal…
Exact Passive-Aggressive Algorithms for Learning to Rank Using Interval Labels
Naresh Manwani, Mohit Chandra
In this paper, we propose exact passive-aggressive (PA) online algorithms for learning to rank. The proposed algorithms can be used even when we have interval labels instead of act…
Generative Adversarial Networks (GANs): What it can generate and What it cannot?
P Manisha, Sujit Gujar
In recent years, Generative Adversarial Networks (GANs) have received significant attention from the research community. With a straightforward implementation and outstanding resul…
DiGrad: Multi-Task Reinforcement Learning with Shared Actions
Parijat Dewangan, S Phaniteja, K Madhava Krishna +2
Most reinforcement learning algorithms are inefficient for learning multiple tasks in complex robotic systems, where different tasks share a set of actions. In such environments a…
Double Ramp Loss Based Reject Option Classifier
Naresh Manwani, Kalpit Desai, Sanand Sasidharan +1
We consider the problem of learning reject option classifiers. The goodness of a reject option classifier is quantified using loss function wherein a loss is…