Venkatesh Saligrama

research

∙ 09/21/2023

Learning to Drive Anywhere

Human drivers can seamlessly adapt their driving decisions across geogra...

0 Ruizhao Zhu, et al. ∙

research

∙ 09/27/2022

A Doubly Optimistic Strategy for Safe Linear Bandits

We propose a doubly optimistic strategy for the safe-linear-bandit probl...

0 Tianrui Chen, et al. ∙

research

∙ 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...

15 Pengkai Zhu, et al. ∙

research

∙ 07/07/2022

FedHeN: Federated Learning in Heterogeneous Networks

We propose a novel training recipe for federated learning with heterogen...

0 Durmus Alp Emre Acar, et al. ∙

research

∙ 04/17/2022

Learning Compositional Representations for Effective Low-Shot Generalization

We propose Recognition as Part Composition (RPC), an image encoding appr...

3 Samarth Mishra, et al. ∙

research

∙ 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...

0 Tianrui Chen, et al. ∙

research

∙ 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...

2 Samarth Mishra, et al. ∙

research

∙ 11/02/2021

Faster Convex Lipschitz Regression via 2-block ADMM

The task of approximating an arbitrary convex function arises in several...

0 Ali Siahkamari, et al. ∙

research

∙ 10/27/2021

Online Selective Classification with Limited Feedback

Motivated by applications to resource-limited and safety-critical domain...

5 Aditya Gangrade, et al. ∙

research

∙ 07/22/2021

Bandit Quickest Changepoint Detection

Detecting abrupt changes in temporal behavior patterns is of interest in...

0 Aditya Gopalan, et al. ∙

research

∙ 05/04/2021

Effectively Leveraging Attributes for Visual Similarity

Measuring similarity between two images often requires performing comple...

10 Samarth Mishra, et al. ∙

research

∙ 01/29/2021

Surprisingly Simple Semi-Supervised Domain Adaptation with Pretraining and Consistency

Visual domain adaptation involves learning to classify images from a tar...

9 Samarth Mishra, et al. ∙

research

∙ 11/07/2020

Limits on Testing Structural Changes in Ising Models

We present novel information-theoretic limits on detecting sparse change...

0 Aditya Gangrade, et al. ∙

research

∙ 10/23/2020

Online Algorithm for Unsupervised Sequential Selection with Contextual Information

In this paper, we study Contextual Unsupervised Sequential Selection (US...

0 Arun Verma, et al. ∙

research

∙ 10/15/2020

Selective Classification via One-Sided Prediction

We propose a novel method for selective classification (SC), a problem w...

5 Aditya Gangrade, et al. ∙

research

∙ 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...

0 Yun Yue, et al. ∙

research

∙ 07/05/2020

Piecewise Linear Regression via a Difference of Convex Functions

We present a new piecewise linear regression methodology that utilizes f...

0 Ali Siahkamari, et al. ∙

research

∙ 04/14/2020

Budget Learning via Bracketing

Conventional machine learning applications in the mobile/IoT setting tra...

0 Aditya Gangrade, et al. ∙

research

∙ 11/18/2019

Dont Even Look Once: Synthesizing Features for Zero-Shot Detection

Zero-shot detection, namely, localizing both seen and unseen objects, in...

0 Pengkai Zhu, et al. ∙

research

∙ 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...

0 Anil Kag, et al. ∙

research

∙ 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...

0 Anil Kag, et al. ∙

research

∙ 05/28/2019

Learning Bregman Divergences

Metric learning is the problem of learning a task-specific distance func...

0 Ali Siahkamari, et al. ∙

research

∙ 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...

12 Yao Ma, et al. ∙

research

∙ 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...

0 Ziming Zhang, et al. ∙

research

∙ 02/01/2019

Graph Resistance and Learning from Pairwise Comparisons

We consider the problem of learning the qualities of a collection of ite...

10 Julien M. Hendrickx, et al. ∙

research

∙ 01/25/2019

Learning for New Visual Environments with Limited Labels

In computer vision applications, such as domain adaptation (DA), few sho...

0 Pengkai Zhu, et al. ∙

research

∙ 01/15/2019

Online Algorithm for Unsupervised Sensor Selection

In many security and healthcare systems, the detection and diagnosis sys...

18 Arun Verma, et al. ∙

research

∙ 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...

0 Aditya Gangrade, et al. ∙

research

∙ 11/19/2018

Generalized Zero-Shot Recognition based on Visually Semantic Embedding

We propose a novel Generalized Zero-Shot learning (GZSL) method that is ...

0 Pengkai Zhu, et al. ∙

research

∙ 11/16/2018

Learning Where to Fixate on Foveated Images

Foveation, the ability to sequentially acquire high-acuity regions of a ...

12 Hanxiao Wang, et al. ∙

research

∙ 03/19/2018

Zero-Shot Detection

As we move towards large-scale object detection, it is unrealistic to ex...

0 Pengkai Zhu, et al. ∙

research

∙ 12/17/2017

Probabilistic Semantic Retrieval for Surveillance Videos with Activity Graphs

We present a novel framework for finding complex activities matching use...

0 Yuting Chen, et al. ∙

research

∙ 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...

0 Aditya Gangrade, et al. ∙

research

∙ 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...

0 Henghui Zhu, et al. ∙

research

∙ 05/26/2017

Adaptive Classification for Prediction Under a Budget

We propose a novel adaptive approximation approach for test-time resourc...

0 Feng Nan, et al. ∙

research

∙ 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...

0 Feng Nan, et al. ∙

research

∙ 04/25/2017

Dynamic Model Selection for Prediction Under a Budget

We present a dynamic model selection approach for resource-constrained p...

0 Feng Nan, et al. ∙

research

∙ 04/10/2017

Field of Groves: An Energy-Efficient Random Forest

Machine Learning (ML) algorithms, like Convolutional Neural Networks (CN...

0 Zafar Takhirov, et al. ∙

research

∙ 02/25/2017

Adaptive Neural Networks for Efficient Inference

We present an approach to adaptively utilize deep neural networks in ord...

0 Tolga Bolukbasi, et al. ∙

research

∙ 11/23/2016

Learning Joint Feature Adaptation for Zero-Shot Recognition

Zero-shot recognition (ZSR) aims to recognize target-domain data instanc...

0 Ziming Zhang, et al. ∙

research

∙ 08/26/2016

Clustering and Community Detection with Imbalanced Clusters

Spectral clustering methods which are frequently used in clustering and ...

0 Cem Aksoylar, et al. ∙

research

∙ 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...

0 Tolga Bolukbasi, et al. ∙

research

∙ 06/20/2016

Quantifying and Reducing Stereotypes in Word Embeddings

Machine learning algorithms are optimized to model statistical propertie...

0 Tolga Bolukbasi, et al. ∙

research

∙ 06/16/2016

Pruning Random Forests for Prediction on a Budget

We propose to prune a random forest (RF) for resource-constrained predic...

0 Feng Nan, et al. ∙

research

∙ 02/28/2016

Resource Constrained Structured Prediction

We study the problem of structured prediction under test-time budget con...

0 Tolga Bolukbasi, et al. ∙

research

∙ 01/22/2016

Learning Minimum Volume Sets and Anomaly Detectors from KNN Graphs

We propose a non-parametric anomaly detection algorithm for high dimensi...

0 Jonathan Root, et al. ∙

research

∙ 01/05/2016

Optimally Pruning Decision Tree Ensembles With Feature Cost

We consider the problem of learning decision rules for prediction with f...

0 Feng Nan, et al. ∙

research

∙ 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 ...

0 Ziming Zhang, et al. ∙

research

∙ 11/14/2015

Zero-Shot Learning via Joint Latent Similarity Embedding

Zero-shot recognition (ZSR) deals with the problem of predicting class l...

0 Ziming Zhang, et al. ∙

research

∙ 11/14/2015

Sequential Optimization for Efficient High-Quality Object Proposal Generation

We are motivated by the need for a generic object proposal generation al...

0 Ziming Zhang, et al. ∙

Venkatesh Saligrama

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