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09/10/2023
Multi-modal Extreme Classification
This paper develops the MUFIN technique for extreme classification (XC) ...
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12/11/2022
Corruption-tolerant Algorithms for Generalized Linear Models
This paper presents SVAM (Sequential Variance-Altered MLE), a unified fr...
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10/28/2022
DELFI: Deep Mixture Models for Long-term Air Quality Forecasting in the Delhi National Capital Region
The identification and control of human factors in climate change is a r...
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07/10/2022
NGAME: Negative Mining-aware Mini-batching for Extreme Classification
Extreme Classification (XC) seeks to tag data points with the most relev...
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11/06/2021
AGGLIO: Global Optimization for Locally Convex Functions
This paper presents AGGLIO (Accelerated Graduated Generalized LInear-mod...
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09/28/2021
IGLU: Efficient GCN Training via Lazy Updates
Graph Convolution Networks (GCN) are used in numerous settings involving...
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08/01/2021
DECAF: Deep Extreme Classification with Label Features
Extreme multi-label classification (XML) involves tagging a data point w...
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07/31/2021
ECLARE: Extreme Classification with Label Graph Correlations
Deep extreme classification (XC) seeks to train deep architectures that ...
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06/25/2020
Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems
We provide the first global model recovery results for the IRLS (iterati...
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05/28/2020
MACER: A Modular Framework for Accelerated Compilation Error Repair
Automated compilation error repair, the problem of suggesting fixes to b...
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05/22/2020
Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization
Mass public quarantining, colloquially known as a lock-down, is a non-ph...
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05/28/2019
Accelerating Extreme Classification via Adaptive Feature Agglomeration
Extreme classification seeks to assign each data point, the most relevan...
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02/01/2019
DANTE: Deep AlterNations for Training nEural networks
We present DANTE, a novel method for training neural networks using the ...
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02/17/2018
Bayes-optimal Hierarchical Classification over Asymmetric Tree-Distance Loss
Hierarchical classification is supervised multi-class classification pro...
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01/31/2018
Optimizing Non-decomposable Measures with Deep Networks
We present a class of algorithms capable of directly training deep neura...
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12/21/2017
Non-convex Optimization for Machine Learning
A vast majority of machine learning algorithms train their models and pe...
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08/06/2016
On Context-Dependent Clustering of Bandits
We investigate a novel cluster-of-bandit algorithm CAB for collaborative...
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07/01/2016
Efficient and Consistent Robust Time Series Analysis
We study the problem of robust time series analysis under the standard a...
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05/13/2016
Online Optimization Methods for the Quantification Problem
The estimation of class prevalence, i.e., the fraction of a population t...
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10/12/2015
Context-Aware Bandits
We propose an efficient Context-Aware clustering of Bandits (CAB) algori...
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06/08/2015
Robust Regression via Hard Thresholding
We study the problem of Robust Least Squares Regression (RLSR) where sev...
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05/26/2015
Surrogate Functions for Maximizing Precision at the Top
The problem of maximizing precision at the top of a ranked list, often d...
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05/26/2015
Optimizing Non-decomposable Performance Measures: A Tale of Two Classes
Modern classification problems frequently present mild to severe label i...
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10/24/2014
Online and Stochastic Gradient Methods for Non-decomposable Loss Functions
Modern applications in sensitive domains such as biometrics and medicine...
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10/20/2014
On Iterative Hard Thresholding Methods for High-dimensional M-Estimation
The use of M-estimators in generalized linear regression models in high ...
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05/11/2013
On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions
In this paper, we study the generalization properties of online learning...
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02/18/2013
On Translation Invariant Kernels and Screw Functions
We explore the connection between Hilbertian metrics and positive defini...
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02/02/2013
Generalization Guarantees for a Binary Classification Framework for Two-Stage Multiple Kernel Learning
We present generalization bounds for the TS-MKL framework for two stage ...
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10/22/2012
Supervised Learning with Similarity Functions
We address the problem of general supervised learning when data can only...
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01/31/2012
Random Feature Maps for Dot Product Kernels
Approximating non-linear kernels using feature maps has gained a lot of ...
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12/22/2011
Similarity-based Learning via Data Driven Embeddings
We consider the problem of classification using similarity/distance func...
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10/19/2010