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Found 35 papers, 9 papers with code
Graph Regularized Encoder Training for Extreme Classification
The paper notices that in these settings, it is much more effective to use graph data to regularize encoder training than to implement a GCN.
Multi-modal Extreme Classification
This paper develops the MUFIN technique for extreme classification (XC) tasks with millions of labels where datapoints and labels are endowed with visual and textual descriptors.
Corruption-tolerant Algorithms for Generalized Linear Models
This paper presents SVAM (Sequential Variance-Altered MLE), a unified framework for learning generalized linear models under adversarial label corruption in training data.
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 rapidly growing concern and robust, real-time air-quality monitoring and forecasting plays a critical role in allowing effective policy formulation and implementation.
NGAME: Negative Mining-aware Mini-batching for Extreme Classification
This paper identifies that memory overheads of popular negative mining techniques often force mini-batch sizes to remain small and slow training down.
AGGLIO: Global Optimization for Locally Convex Functions
This paper presents AGGLIO (Accelerated Graduated Generalized LInear-model Optimization), a stage-wise, graduated optimization technique that offers global convergence guarantees for non-convex optimization problems whose objectives offer only local convexity and may fail to be even quasi-convex at a global scale.
IGLU: Efficient GCN Training via Lazy Updates
Training multi-layer Graph Convolution Networks (GCN) using standard SGD techniques scales poorly as each descent step ends up updating node embeddings for a large portion of the graph.
DECAF: Deep Extreme Classification with Label Features
This paper develops the DECAF algorithm that addresses these challenges by learning models enriched by label metadata that jointly learn model parameters and feature representations using deep networks and offer accurate classification at the scale of millions of labels.
ECLARE: Extreme Classification with Label Graph Correlations
This paper presents ECLARE, a scalable deep learning architecture that incorporates not only label text, but also label correlations, to offer accurate real-time predictions within a few milliseconds.
Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems
We provide the first global model recovery results for the IRLS (iteratively reweighted least squares) heuristic for robust regression problems.
MACER: A Modular Framework for Accelerated Compilation Error Repair
Automated compilation error repair, the problem of suggesting fixes to buggy programs that fail to compile, has generated significant interest in recent years.
Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization
Mass public quarantining, colloquially known as a lock-down, is a non-pharmaceutical intervention to check spread of disease.
Accelerating Extreme Classification via Adaptive Feature Agglomeration
Extreme classification seeks to assign each data point, the most relevant labels from a universe of a million or more labels.
DANTE: Deep AlterNations for Training nEural networks
We present DANTE, a novel method for training neural networks using the alternating minimization principle.
Bayes-optimal Hierarchical Classification over Asymmetric Tree-Distance Loss
We extend the consistency of hierarchical classification algorithm over asymmetric tree distance loss.
Optimizing Non-decomposable Measures with Deep Networks
We present a class of algorithms capable of directly training deep neural networks with respect to large families of task-specific performance measures such as the F-measure and the Kullback-Leibler divergence that are structured and non-decomposable.
Training Autoencoders by Alternating Minimization
We present DANTE, a novel method for training neural networks, in particular autoencoders, using the alternating minimization principle.
Non-convex Optimization for Machine Learning
The goal of this monograph is to both, introduce the rich literature in this area, as well as equip the reader with the tools and techniques needed to analyze these simple procedures for non-convex problems.
Consistent Robust Regression
We present the first efficient and provably consistent estimator for the robust regression problem.
On Context-Dependent Clustering of Bandits
We investigate a novel cluster-of-bandit algorithm CAB for collaborative recommendation tasks that implements the underlying feedback sharing mechanism by estimating the neighborhood of users in a context-dependent manner.
Efficient and Consistent Robust Time Series Analysis
We illustrate our methods on synthetic datasets and show that our methods indeed are able to consistently recover the optimal parameters despite a large fraction of points being corrupted.
Online Optimization Methods for the Quantification Problem
The estimation of class prevalence, i. e., the fraction of a population that belongs to a certain class, is a very useful tool in data analytics and learning, and finds applications in many domains such as sentiment analysis, epidemiology, etc.
Sparse Local Embeddings for Extreme Multi-label Classification
The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set.
Context-Aware Bandits
We propose an efficient Context-Aware clustering of Bandits (CAB) algorithm, which can capture collaborative effects.
Locally Non-linear Embeddings for Extreme Multi-label Learning
Embedding based approaches make training and prediction tractable by assuming that the training label matrix is low-rank and hence the effective number of labels can be reduced by projecting the high dimensional label vectors onto a low dimensional linear subspace.
Extreme Multi-Label Classification
General Classification
+2
Robust Regression via Hard Thresholding
In this work, we study a simple hard-thresholding algorithm called TORRENT which, under mild conditions on X, can recover w* exactly even if b corrupts the response variables in an adversarial manner, i. e. both the support and entries of b are selected adversarially after observing X and w*.
Optimizing Non-decomposable Performance Measures: A Tale of Two Classes
Modern classification problems frequently present mild to severe label imbalance as well as specific requirements on classification characteristics, and require optimizing performance measures that are non-decomposable over the dataset, such as F-measure.
Surrogate Functions for Maximizing Precision at the Top
At the heart of our results is a family of truly upper bounding surrogates for prec@k. These surrogates are motivated in a principled manner and enjoy attractive properties such as consistency to prec@k under various natural margin/noise conditions.
Online and Stochastic Gradient Methods for Non-decomposable Loss Functions
In this work we initiate a study of online learning techniques for such non-decomposable loss functions with an aim to enable incremental learning as well as design scalable solvers for batch problems.
On Iterative Hard Thresholding Methods for High-dimensional M-Estimation
Our results rely on a general analysis framework that enables us to analyze several popular hard thresholding style algorithms (such as HTP, CoSaMP, SP) in the high dimensional regression setting.
Large-scale Multi-label Learning with Missing Labels
The multi-label classification problem has generated significant interest in recent years.
On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions
We are also able to analyze a class of memory efficient online learning algorithms for pairwise learning problems that use only a bounded subset of past training samples to update the hypothesis at each step.
Supervised Learning with Similarity Functions
a given supervised learning task and then adapt a well-known landmarking technique to provide efficient algorithms for supervised learning using ''good'' similarity functions.
Similarity-based Learning via Data Driven Embeddings
We propose a landmarking-based approach to obtaining a classifier from such learned goodness criteria.
Random Projection Trees Revisited
The Random Projection Tree (RPTree) structures proposed in [Dasgupta-Freund-STOC-08] are space partitioning data structures that automatically adapt to various notions of intrinsic dimensionality of data.
