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09/21/2023
Learning to Drive Anywhere
Human drivers can seamlessly adapt their driving decisions across geogra...
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09/27/2022
A Doubly Optimistic Strategy for Safe Linear Bandits
We propose a doubly optimistic strategy for the safe-linear-bandit probl...
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07/14/2022
Fine-grained Few-shot Recognition by Deep Object Parsing
In our framework, an object is made up of K distinct parts or units, and...
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07/07/2022
FedHeN: Federated Learning in Heterogeneous Networks
We propose a novel training recipe for federated learning with heterogen...
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04/17/2022
Learning Compositional Representations for Effective Low-Shot Generalization
We propose Recognition as Part Composition (RPC), an image encoding appr...
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04/01/2022
Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk
We investigate a natural but surprisingly unstudied approach to the mult...
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11/30/2021
Task2Sim : Towards Effective Pre-training and Transfer from Synthetic Data
Pre-training models on Imagenet or other massive datasets of real images...
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11/02/2021
Faster Convex Lipschitz Regression via 2-block ADMM
The task of approximating an arbitrary convex function arises in several...
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10/27/2021
Online Selective Classification with Limited Feedback
Motivated by applications to resource-limited and safety-critical domain...
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07/22/2021
Bandit Quickest Changepoint Detection
Detecting abrupt changes in temporal behavior patterns is of interest in...
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05/04/2021
Effectively Leveraging Attributes for Visual Similarity
Measuring similarity between two images often requires performing comple...
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01/29/2021
Surprisingly Simple Semi-Supervised Domain Adaptation with Pretraining and Consistency
Visual domain adaptation involves learning to classify images from a tar...
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11/07/2020
Limits on Testing Structural Changes in Ising Models
We present novel information-theoretic limits on detecting sparse change...
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10/23/2020
Online Algorithm for Unsupervised Sequential Selection with Contextual Information
In this paper, we study Contextual Unsupervised Sequential Selection (US...
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10/15/2020
Selective Classification via One-Sided Prediction
We propose a novel method for selective classification (SC), a problem w...
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10/12/2020
RNN Training along Locally Optimal Trajectories via Frank-Wolfe Algorithm
We propose a novel and efficient training method for RNNs by iteratively...
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07/05/2020
Piecewise Linear Regression via a Difference of Convex Functions
We present a new piecewise linear regression methodology that utilizes f...
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04/14/2020
Budget Learning via Bracketing
Conventional machine learning applications in the mobile/IoT setting tra...
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11/18/2019
Dont Even Look Once: Synthesizing Features for Zero-Shot Detection
Zero-shot detection, namely, localizing both seen and unseen objects, in...
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08/22/2019
RNNs Evolving on an Equilibrium Manifold: A Panacea for Vanishing and Exploding Gradients?
Recurrent neural networks (RNNs) are particularly well-suited for modeli...
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08/22/2019
RNNs Evolving in Equilibrium: A Solution to the Vanishing and Exploding Gradients
Recurrent neural networks (RNNs) are particularly well-suited for modeli...
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05/28/2019
Learning Bregman Divergences
Metric learning is the problem of learning a task-specific distance func...
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04/25/2019
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers
We consider worker skill estimation for the single-coin Dawid-Skene crow...
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03/02/2019
Equilibrated Recurrent Neural Network: Neuronal Time-Delayed Self-Feedback Improves Accuracy and Stability
We propose a novel Equilibrated Recurrent Neural Network (ERNN) to comb...
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02/01/2019
Graph Resistance and Learning from Pairwise Comparisons
We consider the problem of learning the qualities of a collection of ite...
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01/25/2019
Learning for New Visual Environments with Limited Labels
In computer vision applications, such as domain adaptation (DA), few sho...
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01/15/2019
Online Algorithm for Unsupervised Sensor Selection
In many security and healthcare systems, the detection and diagnosis sys...
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11/29/2018
Testing Changes in Communities for the Stochastic Block Model
We introduce the problems of goodness-of-fit and two-sample testing of t...
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11/19/2018
Generalized Zero-Shot Recognition based on Visually Semantic Embedding
We propose a novel Generalized Zero-Shot learning (GZSL) method that is ...
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11/16/2018
Learning Where to Fixate on Foveated Images
Foveation, the ability to sequentially acquire high-acuity regions of a ...
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03/19/2018
Zero-Shot Detection
As we move towards large-scale object detection, it is unrealistic to ex...
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12/17/2017
Probabilistic Semantic Retrieval for Surveillance Videos with Activity Graphs
We present a novel framework for finding complex activities matching use...
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10/28/2017
Lower Bounds for Two-Sample Structural Change Detection in Ising and Gaussian Models
The change detection problem is to determine if the Markov network struc...
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05/31/2017
Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget
Deep neural network (DNN) based approaches hold significant potential fo...
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05/26/2017
Adaptive Classification for Prediction Under a Budget
We propose a novel adaptive approximation approach for test-time resourc...
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05/10/2017
Comments on the proof of adaptive submodular function minimization
We point out an issue with Theorem 5 appearing in "Group-based active qu...
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04/25/2017
Dynamic Model Selection for Prediction Under a Budget
We present a dynamic model selection approach for resource-constrained p...
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04/10/2017
Field of Groves: An Energy-Efficient Random Forest
Machine Learning (ML) algorithms, like Convolutional Neural Networks (CN...
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02/25/2017
Adaptive Neural Networks for Efficient Inference
We present an approach to adaptively utilize deep neural networks in ord...
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11/23/2016
Learning Joint Feature Adaptation for Zero-Shot Recognition
Zero-shot recognition (ZSR) aims to recognize target-domain data instanc...
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08/26/2016
Clustering and Community Detection with Imbalanced Clusters
Spectral clustering methods which are frequently used in clustering and ...
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07/21/2016
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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06/20/2016
Quantifying and Reducing Stereotypes in Word Embeddings
Machine learning algorithms are optimized to model statistical propertie...
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06/16/2016
Pruning Random Forests for Prediction on a Budget
We propose to prune a random forest (RF) for resource-constrained predic...
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02/28/2016
Resource Constrained Structured Prediction
We study the problem of structured prediction under test-time budget con...
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01/22/2016
Learning Minimum Volume Sets and Anomaly Detectors from KNN Graphs
We propose a non-parametric anomaly detection algorithm for high dimensi...
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01/05/2016
Optimally Pruning Decision Tree Ensembles With Feature Cost
We consider the problem of learning decision rules for prediction with f...
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11/14/2015
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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11/14/2015
Zero-Shot Learning via Joint Latent Similarity Embedding
Zero-shot recognition (ZSR) deals with the problem of predicting class l...
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11/14/2015