
Effectively Leveraging Attributes for Visual Similarity
Measuring similarity between two images often requires performing comple...
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Surprisingly Simple SemiSupervised Domain Adaptation with Pretraining and Consistency
Visual domain adaptation involves learning to classify images from a tar...
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Limits on Testing Structural Changes in Ising Models
We present novel informationtheoretic limits on detecting sparse change...
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Online Algorithm for Unsupervised Sequential Selection with Contextual Information
In this paper, we study Contextual Unsupervised Sequential Selection (US...
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Selective Classification via OneSided Prediction
We propose a novel method for selective classification (SC), a problem w...
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RNN Training along Locally Optimal Trajectories via FrankWolfe Algorithm
We propose a novel and efficient training method for RNNs by iteratively...
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Piecewise Linear Regression via a Difference of Convex Functions
We present a new piecewise linear regression methodology that utilizes f...
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Budget Learning via Bracketing
Conventional machine learning applications in the mobile/IoT setting tra...
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Dont Even Look Once: Synthesizing Features for ZeroShot Detection
Zeroshot detection, namely, localizing both seen and unseen objects, in...
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RNNs Evolving on an Equilibrium Manifold: A Panacea for Vanishing and Exploding Gradients?
Recurrent neural networks (RNNs) are particularly wellsuited for modeli...
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RNNs Evolving in Equilibrium: A Solution to the Vanishing and Exploding Gradients
Recurrent neural networks (RNNs) are particularly wellsuited for modeli...
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Learning Bregman Divergences
Metric learning is the problem of learning a taskspecific distance func...
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Gradient Descent for Sparse RankOne Matrix Completion for CrowdSourced Aggregation of Sparsely Interacting Workers
We consider worker skill estimation for the singlecoin DawidSkene crow...
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Equilibrated Recurrent Neural Network: Neuronal TimeDelayed SelfFeedback Improves Accuracy and Stability
We propose a novel Equilibrated Recurrent Neural Network (ERNN) to comb...
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Graph Resistance and Learning from Pairwise Comparisons
We consider the problem of learning the qualities of a collection of ite...
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Learning for New Visual Environments with Limited Labels
In computer vision applications, such as domain adaptation (DA), few sho...
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Online Algorithm for Unsupervised Sensor Selection
In many security and healthcare systems, the detection and diagnosis sys...
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Testing Changes in Communities for the Stochastic Block Model
We introduce the problems of goodnessoffit and twosample testing of t...
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Generalized ZeroShot Recognition based on Visually Semantic Embedding
We propose a novel Generalized ZeroShot learning (GZSL) method that is ...
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Learning Where to Fixate on Foveated Images
Foveation, the ability to sequentially acquire highacuity regions of a ...
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ZeroShot Detection
As we move towards largescale object detection, it is unrealistic to ex...
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Probabilistic Semantic Retrieval for Surveillance Videos with Activity Graphs
We present a novel framework for finding complex activities matching use...
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Lower Bounds for TwoSample Structural Change Detection in Ising and Gaussian Models
The change detection problem is to determine if the Markov network struc...
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Sequential Dynamic Decision Making with Deep Neural Nets on a TestTime Budget
Deep neural network (DNN) based approaches hold significant potential fo...
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Adaptive Classification for Prediction Under a Budget
We propose a novel adaptive approximation approach for testtime resourc...
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Comments on the proof of adaptive submodular function minimization
We point out an issue with Theorem 5 appearing in "Groupbased active qu...
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Dynamic Model Selection for Prediction Under a Budget
We present a dynamic model selection approach for resourceconstrained p...
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Field of Groves: An EnergyEfficient Random Forest
Machine Learning (ML) algorithms, like Convolutional Neural Networks (CN...
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Adaptive Neural Networks for Efficient Inference
We present an approach to adaptively utilize deep neural networks in ord...
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Learning Joint Feature Adaptation for ZeroShot Recognition
Zeroshot recognition (ZSR) aims to recognize targetdomain data instanc...
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Clustering and Community Detection with Imbalanced Clusters
Spectral clustering methods which are frequently used in clustering and ...
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Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
The blind application of machine learning runs the risk of amplifying bi...
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Quantifying and Reducing Stereotypes in Word Embeddings
Machine learning algorithms are optimized to model statistical propertie...
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Pruning Random Forests for Prediction on a Budget
We propose to prune a random forest (RF) for resourceconstrained predic...
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Resource Constrained Structured Prediction
We study the problem of structured prediction under testtime budget con...
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Learning Minimum Volume Sets and Anomaly Detectors from KNN Graphs
We propose a nonparametric anomaly detection algorithm for high dimensi...
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Optimally Pruning Decision Tree Ensembles With Feature Cost
We consider the problem of learning decision rules for prediction with f...
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Efficient Training of Very Deep Neural Networks for Supervised Hashing
In this paper, we propose training very deep neural networks (DNNs) for ...
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ZeroShot Learning via Joint Latent Similarity Embedding
Zeroshot recognition (ZSR) deals with the problem of predicting class l...
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Sequential Optimization for Efficient HighQuality Object Proposal Generation
We are motivated by the need for a generic object proposal generation al...
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Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction
We study the problem of reducing testtime acquisition costs in classifi...
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Group Membership Prediction
The group membership prediction (GMP) problem involves predicting whethe...
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ZeroShot Learning via Semantic Similarity Embedding
In this paper we consider a version of the zeroshot learning problem wh...
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Sensor Selection by Linear Programming
We learn sensor trees from training data to minimize sensor acquisition ...
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Necessary and Sufficient Conditions and a Provably Efficient Algorithm for Separable Topic Discovery
We develop necessary and sufficient conditions and a novel provably cons...
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Learning Mixed Membership Mallows Models from Pairwise Comparisons
We propose a novel parameterized family of Mixed Membership Mallows Mode...
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FeatureBudgeted Random Forest
We seek decision rules for predictiontime cost reduction, where complet...
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Learning Efficient Anomaly Detectors from KNN Graphs
We propose a nonparametric anomaly detection algorithm for high dimensi...
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Minimax Optimal Sparse Signal Recovery with Poisson Statistics
We are motivated by problems that arise in a number of applications such...
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A Topic Modeling Approach to Ranking
We propose a topic modeling approach to the prediction of preferences in...
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Venkatesh Saligrama
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Professor at Boston University and Research Affiliate at MIT