281 citations · 692 across the 30 of their papers we have counts for
32 papers · 1 filter
Linear Strategic Classification with Endogenous Improvements
Siddharth Shrivastava, Mahvith Akshintala, B Vamsha Vardhan Reddy +3
Classical models of strategic classification treat agents' responses to a deployed classifier as cosmetic: features change, but true labels do not. We study an improvement-aware va…
Training-Free Cross-Architecture Merging for Graph Neural Networks
Rishabh Bhattacharya, Vikaskumar Kalsariya, Naresh Manwani
Model merging has emerged as a powerful paradigm for combining the capabilities of distinct expert models without the high computational cost of retraining, yet current methods are…
EdgeMask-DG*: Learning Domain-Invariant Graph Structures via Adversarial Edge Masking
Rishabh Bhattacharya, Naresh Manwani
Structural shifts pose a significant challenge for graph neural networks, as graph topology acts as a covariate that can vary across domains. Existing domain generalization methods…
DFORD: Directional Feedback based Online Ordinal Regression Learning
Naresh Manwani, M Elamparithy, Tanish Taneja
In this paper, we introduce directional feedback in the ordinal regression setting, in which the learner receives feedback on whether the predicted label is on the left or the righ…
Achieving Fair PCA Using Joint Eigenvalue Decomposition
Vidhi Rathore, Naresh Manwani
Principal Component Analysis (PCA) is a widely used method for dimensionality reduction, but it often overlooks fairness, especially when working with data that includes demographi…
Optimal Strategies for Federated Learning Maintaining Client Privacy
Uday Bhaskar, Varul Srivastava, Avyukta Manjunatha Vummintala +2
Federated Learning (FL) emerged as a learning method to enable the server to train models over data distributed among various clients. These clients are protective about their data…
Predict Confidently, Predict Right: Abstention in Dynamic Graph Learning
Jayadratha Gayen, Himanshu Pal, Naresh Manwani +1
Many real-world systems can be modeled as dynamic graphs, where nodes and edges evolve over time, requiring specialized models to capture their evolving dynamics in risk-sensitive…
Node Classification With Integrated Reject Option
Uday Bhaskar, Jayadratha Gayen, Charu Sharma +1
One of the key tasks in graph learning is node classification. While Graph neural networks have been used for various applications, their adaptivity to reject option setting is not…
ILAEDA: An Imitation Learning Based Approach for Automatic Exploratory Data Analysis
Abhijit Manatkar, Devarsh Patel, Hima Patel +1
Automating end-to-end Exploratory Data Analysis (AutoEDA) is a challenging open problem, often tackled through Reinforcement Learning (RL) by learning to predict a sequence of anal…
Towards Calibrated Losses for Adversarial Robust Reject Option Classification
Vrund Shah, Tejas Chaudhari, Naresh Manwani
Robustness towards adversarial attacks is a vital property for classifiers in several applications such as autonomous driving, medical diagnosis, etc. Also, in such scenarios, wher…
RoLNiP: Robust Learning Using Noisy Pairwise Comparisons
Samartha S Maheshwara, Naresh Manwani
This paper presents a robust approach for learning from noisy pairwise comparisons. We propose sufficient conditions on the loss function under which the risk minimization framewor…
Delaytron: Efficient Learning of Multiclass Classifiers with Delayed Bandit Feedbacks
Naresh Manwani, Mudit Agarwal
In this paper, we present online algorithm called {\it Delaytron} for learning multi class classifiers using delayed bandit feedbacks. The sequence of feedback delays $\{d_t\}_{t=1…
RISAN: Robust Instance Specific Abstention Network
Bhavya Kalra, Kulin Shah, Naresh Manwani
In this paper, we propose deep architectures for learning instance specific abstain (reject option) binary classifiers. The proposed approach uses double sigmoid loss function as d…
Multiclass Classification using dilute bandit feedback
Gaurav Batra, Naresh Manwani
This paper introduces a new online learning framework for multiclass classification called learning with diluted bandit feedback. At every time step, the algorithm predicts a candi…
The Curious Case of Convex Neural Networks
Sarath Sivaprasad, Ankur Singh, Naresh Manwani +1
In this paper, we investigate a constrained formulation of neural networks where the output is a convex function of the input. We show that the convexity constraints can be enforce…
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…
Online Algorithms for Multiclass Classification using Partial Labels
Rajarshi Bhattacharjee, Naresh Manwani
In this paper, we propose online algorithms for multiclass classification using partial labels. We propose two variants of Perceptron called Avg Perceptron and Max Perceptron to de…
Expert2Coder: Capturing Divergent Brain Regions Using Mixture of Regression Experts
Subba Reddy Oota, Naresh Manwani, Raju S. Bapi
fMRI semantic category understanding using linguistic encoding models attempts to learn a forward mapping that relates stimuli to the corresponding brain activation. State-of-the-a…
Online Active Learning of Reject Option Classifiers
Kulin Shah, Naresh Manwani
Active learning is an important technique to reduce the number of labeled examples in supervised learning. Active learning for binary classification has been well addressed in mach…
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…
Mixture of Regression Experts in fMRI Encoding
Subba Reddy Oota, Adithya Avvaru, Naresh Manwani +1
fMRI semantic category understanding using linguistic encoding models attempt to learn a forward mapping that relates stimuli to the corresponding brain activation. Classical encod…
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…
PRIL: Perceptron Ranking Using Interval Labeled Data
Naresh Manwani
In this paper, we propose an online learning algorithm PRIL for learning ranking classifiers using interval labeled data and show its correctness. We show its convergence in finite…