Search Results for author:
Found 22 papers, 2 papers with code
Pseudo-labelling meets Label Smoothing for Noisy Partial Label Learning
Noisy PLL (NPLL) relaxes this constraint by allowing some partial labels to not contain the true label, enhancing the practicality of the problem.
RoLNiP: Robust Learning Using Noisy Pairwise Comparisons
This paper presents a robust approach for learning from noisy pairwise comparisons.
Delaytron: Efficient Learning of Multiclass Classifiers with Delayed Bandit Feedbacks
When $t+d_t>T$, we consider that the feedback for the $t$-th round is missing.
RISAN: Robust Instance Specific Abstention Network
In this paper, we propose deep architectures for learning instance specific abstain (reject option) binary classifiers.
Multiclass Classification using dilute bandit feedback
This paper introduces a new online learning framework for multiclass classification called learning with diluted bandit feedback.
The Curious Case of Convex Neural Networks
In this paper, we investigate a constrained formulation of neural networks where the output is a convex function of the input.
Learning Multiclass Classifier Under Noisy Bandit Feedback
This paper addresses the problem of multiclass classification with corrupted or noisy bandit feedback.
Online Algorithms for Multiclass Classification using Partial Labels
In this paper, we propose online algorithms for multiclass classification using partial labels.
Robust Deep Ordinal Regression Under Label Noise
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.
Expert2Coder: Capturing Divergent Brain Regions Using Mixture of Regression Experts
In this paper, we achieve this by clustering similar regions together and for every cluster we learn a different linear regression model using a mixture of linear experts model.
Online Active Learning of Reject Option Classifiers
In this paper, we propose novel algorithms for active learning of reject option classifiers.
PLUME: Polyhedral Learning Using Mixture of Experts
In this paper, we propose a novel mixture of expert architecture for learning polyhedral classifiers.
Mixture of Regression Experts in fMRI Encoding
We argue that each expert learns a certain region of brain activations corresponding to its category of words, which solves the problem of identifying the regions with a simple encoding model.
Exact Passive-Aggressive Algorithms for Learning to Rank Using Interval Labels
We also show experimentally that the proposed algorithms successfully learn accurate classifiers using interval labels as well as exact labels.
fMRI Semantic Category Decoding using Linguistic Encoding of Word Embeddings
Unlike the models with hand-crafted features that have been used in the literature, in this paper we propose a novel approach wherein decoding models are built with features extracted from popular linguistic encodings of Word2Vec, GloVe, Meta-Embeddings in conjunction with the empirical fMRI data associated with viewing several dozen concrete nouns.
Sparse Reject Option Classifier Using Successive Linear Programming
We also show that the excess risk of loss $L_d$ is upper bounded by the excess risk of $L_{dr}$.
PRIL: Perceptron Ranking Using Interval Labeled Data
In this paper, we propose an online learning algorithm PRIL for learning ranking classifiers using interval labeled data and show its correctness.
On the Robustness of Decision Tree Learning under Label Noise
In most practical problems of classifier learning, the training data suffers from the label noise.
Making Risk Minimization Tolerant to Label Noise
Through extensive empirical studies, we show that risk minimization under the $0-1$ loss, the sigmoid loss and the ramp loss has much better robustness to label noise when compared to the SVM algorithm.
Double Ramp Loss Based Reject Option Classifier
The goodness of a reject option classifier is quantified using $0-d-1$ loss function wherein a loss $d \in (0,. 5)$ is assigned for rejection.
K-Plane Regression
In this paper, we present a novel algorithm for piecewise linear regression which can learn continuous as well as discontinuous piecewise linear functions.
Polyceptron: A Polyhedral Learning Algorithm
In this paper we propose a new algorithm for learning polyhedral classifiers which we call as Polyceptron.
