Search Results for author: Venkatesh Saligrama

Found 82 papers, 12 papers with code

Minimax Rate for Learning From Pairwise Comparisons in the BTL Model

no code implementations ICML 2020 Julien Hendrickx, Alex Olshevsky, Venkatesh Saligrama

We consider the problem of learning the qualities w_1, ... , w_n of a collection of items by performing noisy comparisons among them.

Federated Learning Based on Dynamic Regularization

1 code implementation ICLR 2021 Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, Venkatesh Saligrama

We propose a novel federated learning method for distributively training neural network models, where the server orchestrates cooperation between a subset of randomly chosen devices in each round.

Federated Learning

Faster Convex Lipschitz Regression via 2-block ADMM

1 code implementation2 Nov 2021 Ali Siahkamari, Durmus Alp Emre Acar, Christopher Liao, Kelly Geyer, Venkatesh Saligrama, Brian Kulis

The task of approximating an arbitrary convex function arises in several learning problems such as convex regression, learning with a difference of convex (DC) functions, and approximating Bregman divergences.

Metric Learning

Bandit Quickest Changepoint Detection

no code implementations NeurIPS 2021 Aditya Gopalan, Venkatesh Saligrama, Braghadeesh Lakshminarayanan

Many industrial and security applications employ a suite of sensors for detecting abrupt changes in temporal behavior patterns.

Time Adaptive Recurrent Neural Network

no code implementations CVPR 2021 Anil Kag, Venkatesh Saligrama

We propose a learning method that, dynamically modifies the time-constants of the continuous-time counterpart of a vanilla RNN.

Effectively Leveraging Attributes for Visual Similarity

1 code implementation ICCV 2021 Samarth Mishra, Zhongping Zhang, Yuan Shen, Ranjitha Kumar, Venkatesh Saligrama, Bryan Plummer

This enables our model to identify that two images contain the same attribute, but can have it deemed irrelevant (e. g., due to fine-grained differences between them) and ignored for measuring similarity between the two images.

Surprisingly Simple Semi-Supervised Domain Adaptation with Pretraining and Consistency

1 code implementation29 Jan 2021 Samarth Mishra, Kate Saenko, Venkatesh Saligrama

With our Pretraining and Consistency (PAC) approach, we achieve state of the art target accuracy on this semi-supervised domain adaptation task, surpassing multiple adversarial domain alignment methods, across multiple datasets.

Unsupervised Domain Adaptation

Limits on Testing Structural Changes in Ising Models

no code implementations NeurIPS 2020 Aditya Gangrade, Bobak Nazer, Venkatesh Saligrama

We present novel information-theoretic limits on detecting sparse changes in Isingmodels, a problem that arises in many applications where network changes canoccur due to some external stimuli.

Change Detection

Online Algorithm for Unsupervised Sequential Selection with Contextual Information

no code implementations NeurIPS 2020 Arun Verma, Manjesh K. Hanawal, Csaba Szepesvári, Venkatesh Saligrama

In this paper, we study Contextual Unsupervised Sequential Selection (USS), a new variant of the stochastic contextual bandits problem where the loss of an arm cannot be inferred from the observed feedback.

Multi-Armed Bandits

Selective Classification via One-Sided Prediction

no code implementations15 Oct 2020 Aditya Gangrade, Anil Kag, Venkatesh Saligrama

We propose a novel method for selective classification (SC), a problem which allows a classifier to abstain from predicting some instances, thus trading off accuracy against coverage (the fraction of instances predicted).

General Classification Generalization Bounds

Piecewise Linear Regression via a Difference of Convex Functions

2 code implementations ICML 2020 Ali Siahkamari, Aditya Gangrade, Brian Kulis, Venkatesh Saligrama

We present a new piecewise linear regression methodology that utilizes fitting a difference of convex functions (DC functions) to the data.

RNNs Incrementally Evolving on an Equilibrium Manifold: A Panacea for Vanishing and Exploding Gradients?

no code implementations ICLR 2020 Anil Kag, Ziming Zhang, Venkatesh Saligrama

Recurrent neural networks (RNNs) are particularly well-suited for modeling long-term dependencies in sequential data, but are notoriously hard to train because the error backpropagated in time either vanishes or explodes at an exponential rate.

Budget Learning via Bracketing

no code implementations14 Apr 2020 Aditya Gangrade, Durmus Alp Emre Acar, Venkatesh Saligrama

We propose a new formulation for the BL problem via the concept of bracketings.

Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models

no code implementations NeurIPS 2019 Aditya Gangrade, Praveen Venkatesh, Bobak Nazer, Venkatesh Saligrama

Overall, for large changes, $s \gg \sqrt{n}$, we need only $\mathrm{SNR}= O(1)$ whereas a na\"ive test based on community recovery with $O(s)$ errors requires $\mathrm{SNR}= \Theta(\log n)$.

Two-sample testing

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

no code implementations18 Nov 2019 Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama

Zero-shot detection, namely, localizing both seen and unseen objects, increasingly gains importance for large-scale applications, with large number of object classes, since, collecting sufficient annotated data with ground truth bounding boxes is simply not scalable.

Object Detection

RNNs Evolving on an Equilibrium Manifold: A Panacea for Vanishing and Exploding Gradients?

no code implementations22 Aug 2019 Anil Kag, Ziming Zhang, Venkatesh Saligrama

Recurrent neural networks (RNNs) are particularly well-suited for modeling long-term dependencies in sequential data, but are notoriously hard to train because the error backpropagated in time either vanishes or explodes at an exponential rate.

Learning to Approximate a Bregman Divergence

2 code implementations NeurIPS 2020 Ali Siahkamari, Xide Xia, Venkatesh Saligrama, David Castanon, Brian Kulis

Bregman divergences generalize measures such as the squared Euclidean distance and the KL divergence, and arise throughout many areas of machine learning.

Metric Learning

Equilibrated Recurrent Neural Network: Neuronal Time-Delayed Self-Feedback Improves Accuracy and Stability

no code implementations2 Mar 2019 Ziming Zhang, Anil Kag, Alan Sullivan, Venkatesh Saligrama

We show that such self-feedback helps stabilize the hidden state transitions leading to fast convergence during training while efficiently learning discriminative latent features that result in state-of-the-art results on several benchmark datasets at test-time.

Graph Resistance and Learning from Pairwise Comparisons

no code implementations1 Feb 2019 Julien M. Hendrickx, Alex Olshevsky, Venkatesh Saligrama

The algorithm has a relative error decay that scales with the square root of the graph resistance, and provide a matching lower bound (up to log factors).

Learning Classifiers for Domain Adaptation, Zero and Few-Shot Recognition Based on Learning Latent Semantic Parts

no code implementations25 Jan 2019 Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama

In computer vision applications, such as domain adaptation (DA), few shot learning (FSL) and zero-shot learning (ZSL), we encounter new objects and environments, for which insufficient examples exist to allow for training "models from scratch," and methods that adapt existing models, trained on the presented training environment, to the new scenario are required.

Domain Adaptation Few-Shot Learning +1

Online Algorithm for Unsupervised Sensor Selection

no code implementations15 Jan 2019 Arun Verma, Manjesh K. Hanawal, Csaba Szepesvári, Venkatesh Saligrama

We set up the USS problem as a stochastic partial monitoring problem and develop an algorithm with sub-linear regret under the WD property.

Testing Changes in Communities for the Stochastic Block Model

no code implementations29 Nov 2018 Aditya Gangrade, Praveen Venkatesh, Bobak Nazer, Venkatesh Saligrama

Overall, for large changes, $s \gg \sqrt{n}$, we need only $\mathrm{SNR}= O(1)$ whereas a na\"ive test based on community recovery with $O(s)$ errors requires $\mathrm{SNR}= \Theta(\log n)$.

Stochastic Block Model Two-sample testing

Robust Text Classifier on Test-Time Budgets

1 code implementation IJCNLP 2019 Md. Rizwan Parvez, Tolga Bolukbasi, Kai-Wei Chang, Venkatesh Saligrama

We propose a generic and interpretable learning framework for building robust text classification model that achieves accuracy comparable to full models under test-time budget constraints.

General Classification Text Classification

Zero-Shot Detection

1 code implementation19 Mar 2018 Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama

While we utilize semantic features during training, our method is agnostic to semantic information for unseen classes at test-time.

Object Detection

Probabilistic Semantic Retrieval for Surveillance Videos with Activity Graphs

no code implementations17 Dec 2017 Yu-Ting Chen, Joseph Wang, Yannan Bai, Gregory Castañón, Venkatesh Saligrama

We present a novel framework for finding complex activities matching user-described queries in cluttered surveillance videos.

Semantic Retrieval

Lower Bounds for Two-Sample Structural Change Detection in Ising and Gaussian Models

no code implementations28 Oct 2017 Aditya Gangrade, Bobak Nazer, Venkatesh Saligrama

We study the trade-off between the sample sizes and the reliability of change detection, measured as a minimax risk, for the important cases of the Ising models and the Gaussian Markov random fields restricted to the models which have network structures with $p$ nodes and degree at most $d$, and obtain information-theoretic lower bounds for reliable change detection over these models.

Change Detection

Connected Subgraph Detection with Mirror Descent on SDPs

no code implementations ICML 2017 Cem Aksoylar, Lorenzo Orecchia, Venkatesh Saligrama

We propose a novel, computationally efficient mirror-descent based optimization framework for subgraph detection in graph-structured data.

Community Detection

Crowdsourcing with Sparsely Interacting Workers

no code implementations20 Jun 2017 Yao Ma, Alex Olshevsky, Venkatesh Saligrama, Csaba Szepesvari

We then formulate a weighted rank-one optimization problem to estimate skills based on observations on an irreducible, aperiodic interaction graph.

Matrix Completion

Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget

no code implementations31 May 2017 Henghui Zhu, Feng Nan, Ioannis Paschalidis, Venkatesh Saligrama

Deep neural network (DNN) based approaches hold significant potential for reinforcement learning (RL) and have already shown remarkable gains over state-of-art methods in a number of applications.

Decision Making Feature Engineering

Comments on the proof of adaptive submodular function minimization

no code implementations10 May 2017 Feng Nan, Venkatesh Saligrama

We point out an issue with Theorem 5 appearing in "Group-based active query selection for rapid diagnosis in time-critical situations".

Active Learning Stochastic Optimization

Dynamic Model Selection for Prediction Under a Budget

no code implementations25 Apr 2017 Feng Nan, Venkatesh Saligrama

Our objective is to minimize overall average cost without sacrificing accuracy.

Model Selection

Field of Groves: An Energy-Efficient Random Forest

no code implementations10 Apr 2017 Zafar Takhirov, Joseph Wang, Marcia S. Louis, Venkatesh Saligrama, Ajay Joshi

In this work, we present a field of groves (FoG) implementation of random forests (RF) that achieves an accuracy comparable to CNNs and SVMs under tight energy budgets.

General Classification

Adaptive Neural Networks for Efficient Inference

2 code implementations ICML 2017 Tolga Bolukbasi, Joseph Wang, Ofer Dekel, Venkatesh Saligrama

We first pose an adaptive network evaluation scheme, where we learn a system to adaptively choose the components of a deep network to be evaluated for each example.

Learning Joint Feature Adaptation for Zero-Shot Recognition

no code implementations23 Nov 2016 Ziming Zhang, Venkatesh Saligrama

In this paper we propose a novel framework of learning data-dependent feature transforms for scoring similarity between an arbitrary pair of source and target data instances to account for the wide variability in target domain.

Zero-Shot Learning

Sequential Learning without Feedback

no code implementations18 Oct 2016 Manjesh Hanawal, Csaba Szepesvari, Venkatesh Saligrama

We reduce USS to a special case of multi-armed bandit problem with side information and develop polynomial time algorithms that achieve sublinear regret.

Clustering and Community Detection with Imbalanced Clusters

no code implementations26 Aug 2016 Cem Aksoylar, Jing Qian, Venkatesh Saligrama

Spectral clustering methods which are frequently used in clustering and community detection applications are sensitive to the specific graph constructions particularly when imbalanced clusters are present.

Community Detection graph partitioning

Quantifying and Reducing Stereotypes in Word Embeddings

no code implementations20 Jun 2016 Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, Adam Kalai

Machine learning algorithms are optimized to model statistical properties of the training data.

Word Embeddings

Zero-Shot Learning via Joint Latent Similarity Embedding

no code implementations CVPR 2016 Ziming Zhang, Venkatesh Saligrama

It takes an arbitrary pair of source and target domain instances as input and predicts whether or not they come from the same class, i. e. whether there is a match.

Dictionary Learning Zero-Shot Learning

Sequential Optimization for Efficient High-Quality Object Proposal Generation

no code implementations14 Nov 2015 Ziming Zhang, Yun Liu, Xi Chen, Yanjun Zhu, Ming-Ming Cheng, Venkatesh Saligrama, Philip H. S. Torr

We propose a novel object proposal algorithm, BING++, which inherits the virtue of good computational efficiency of BING but significantly improves its proposal localization quality.

Object Proposal Generation

Efficient Training of Very Deep Neural Networks for Supervised Hashing

no code implementations CVPR 2016 Ziming Zhang, Yu-Ting Chen, Venkatesh Saligrama

In this paper, we propose training very deep neural networks (DNNs) for supervised learning of hash codes.

Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction

no code implementations NeurIPS 2015 Joseph Wang, Kirill Trapeznikov, Venkatesh Saligrama

We learn node policies in the DAG by reducing the global objective to a series of cost sensitive learning problems.

Algorithms for Linear Bandits on Polyhedral Sets

no code implementations26 Sep 2015 Manjesh K. Hanawal, Amir Leshem, Venkatesh Saligrama

We then provide a nearly optimal algorithm and show that its expected regret scales as $O(N\log^{1+\epsilon}(T))$ for an arbitrary small $\epsilon >0$.

Group Membership Prediction

no code implementations ICCV 2015 Ziming Zhang, Yu-Ting Chen, Venkatesh Saligrama

In this context we propose a novel probability model and introduce latent {\em view-specific} and {\em view-shared} random variables to jointly account for the view-specific appearance and cross-view similarities among data instances.

Person Re-Identification

Sensor Selection by Linear Programming

no code implementations9 Sep 2015 Joseph Wang, Kirill Trapeznikov, Venkatesh Saligrama

We decompose the problem, which is known to be intractable, into combinatorial (tree structures) and continuous parts (node decision rules) and propose to solve them separately.

Necessary and Sufficient Conditions and a Provably Efficient Algorithm for Separable Topic Discovery

no code implementations23 Aug 2015 Weicong Ding, Prakash Ishwar, Venkatesh Saligrama

We develop necessary and sufficient conditions and a novel provably consistent and efficient algorithm for discovering topics (latent factors) from observations (documents) that are realized from a probabilistic mixture of shared latent factors that have certain properties.

Topic Models

Cheap Bandits

no code implementations15 Jun 2015 Manjesh Kumar Hanawal, Venkatesh Saligrama, Michal Valko, R\' emi Munos

We consider stochastic sequential learning problems where the learner can observe the \textit{average reward of several actions}.

Learning Mixed Membership Mallows Models from Pairwise Comparisons

no code implementations3 Apr 2015 Weicong Ding, Prakash Ishwar, Venkatesh Saligrama

Our key algorithmic insight for estimation is to establish a statistical connection between M4 and topic models by viewing pairwise comparisons as words, and users as documents.

Topic Models

Feature-Budgeted Random Forest

no code implementations20 Feb 2015 Feng Nan, Joseph Wang, Venkatesh Saligrama

We seek decision rules for prediction-time cost reduction, where complete data is available for training, but during prediction-time, each feature can only be acquired for an additional cost.

Minimax Optimal Sparse Signal Recovery with Poisson Statistics

no code implementations21 Jan 2015 Mohammad H. Rohban, Delaram Motamedvaziri, Venkatesh Saligrama

We are motivated by problems that arise in a number of applications such as Online Marketing and Explosives detection, where the observations are usually modeled using Poisson statistics.

Max-Cost Discrete Function Evaluation Problem under a Budget

no code implementations12 Jan 2015 Feng Nan, Joseph Wang, Venkatesh Saligrama

We develop a broad class of \emph{admissible} impurity functions that admit monomials, classes of polynomials, and hinge-loss functions that allow for flexible impurity design with provably optimal approximation bounds.

General Classification

A Topic Modeling Approach to Ranking

no code implementations11 Dec 2014 Weicong Ding, Prakash Ishwar, Venkatesh Saligrama

We propose a topic modeling approach to the prediction of preferences in pairwise comparisons.

Efficient Minimax Signal Detection on Graphs

no code implementations NeurIPS 2014 Jing Qian, Venkatesh Saligrama

Several problems such as network intrusion, community detection, and disease outbreak can be described by observations attributed to nodes or edges of a graph.

Community Detection

A Novel Visual Word Co-occurrence Model for Person Re-identification

no code implementations24 Oct 2014 Ziming Zhang, Yu-Ting Chen, Venkatesh Saligrama

We first map each pixel of an image to a visual word using a codebook, which is learned in an unsupervised manner.

Person Re-Identification

RAPID: Rapidly Accelerated Proximal Gradient Algorithms for Convex Minimization

no code implementations13 Jun 2014 Ziming Zhang, Venkatesh Saligrama

In this paper, we propose a new algorithm to speed-up the convergence of accelerated proximal gradient (APG) methods.

PRISM: Person Re-Identification via Structured Matching

no code implementations13 Jun 2014 Ziming Zhang, Venkatesh Saligrama

From a visual perspective re-id is challenging due to significant changes in visual appearance of individuals in cameras with different pose, illumination and calibration.

Graph Matching Person Re-Identification

A Rank-SVM Approach to Anomaly Detection

no code implementations2 May 2014 Jing Qian, Jonathan Root, Venkatesh Saligrama, Yu-Ting Chen

The resulting anomaly detector is shown to be asymptotically optimal and adaptive in that for any false alarm rate alpha, its decision region converges to the alpha-percentile level set of the unknown underlying density.

Anomaly Detection

Retrieval in Long Surveillance Videos using User Described Motion and Object Attributes

no code implementations1 May 2014 Greg Castanon, Mohamed Elgharib, Venkatesh Saligrama, Pierre-Marc Jodoin

We present a content-based retrieval method for long surveillance videos both for wide-area (Airborne) as well as near-field imagery (CCTV).

Sparse Recovery with Linear and Nonlinear Observations: Dependent and Noisy Data

no code implementations12 Mar 2014 Cem Aksoylar, Venkatesh Saligrama

We formulate sparse support recovery as a salient set identification problem and use information-theoretic analyses to characterize the recovery performance and sample complexity.

Information-Theoretic Bounds for Adaptive Sparse Recovery

no code implementations24 Feb 2014 Cem Aksoylar, Venkatesh Saligrama

We show that adaptivity cannot decrease sample complexity in group testing, 1-bit CS and CS with linear sparsity.

Compressive Sensing

Sensing-Aware Kernel SVM

no code implementations2 Dec 2013 Weicong Ding, Prakash Ishwar, Venkatesh Saligrama, W. Clem Karl

We propose a novel approach for designing kernels for support vector machines (SVMs) when the class label is linked to the observation through a latent state and the likelihood function of the observation given the state (the sensing model) is available.

General Classification Image Classification

Necessary and Sufficient Conditions for Novel Word Detection in Separable Topic Models

no code implementations30 Oct 2013 Weicong Ding, Prakash Ishwar, Mohammad H. Rohban, Venkatesh Saligrama

The simplicial condition and other stronger conditions that imply it have recently played a central role in developing polynomial time algorithms with provable asymptotic consistency and sample complexity guarantees for topic estimation in separable topic models.

Topic Models

Spectral Clustering with Imbalanced Data

no code implementations9 Sep 2013 Jing Qian, Venkatesh Saligrama

Spectral clustering is sensitive to how graphs are constructed from data particularly when proximal and imbalanced clusters are present.

graph partitioning

Sparse Signal Processing with Linear and Nonlinear Observations: A Unified Shannon-Theoretic Approach

no code implementations2 Apr 2013 Cem Aksoylar, George Atia, Venkatesh Saligrama

These mutual information expressions unify conditions for both linear and nonlinear observations.

An Impossibility Result for High Dimensional Supervised Learning

no code implementations29 Jan 2013 Mohammad Hossein Rohban, Prakash Ishwar, Birant Orten, William C. Karl, Venkatesh Saligrama

We study high-dimensional asymptotic performance limits of binary supervised classification problems where the class conditional densities are Gaussian with unknown means and covariances and the number of signal dimensions scales faster than the number of labeled training samples.

General Classification

Local Supervised Learning through Space Partitioning

no code implementations NeurIPS 2012 Joseph Wang, Venkatesh Saligrama

We show that space partitioning can be equivalently reformulated as a supervised learning problem and consequently any discriminative learning method can be utilized in conjunction with our approach.

General Classification

Probabilistic Belief Revision with Structural Constraints

no code implementations NeurIPS 2010 Peter Jones, Venkatesh Saligrama, Sanjoy Mitter

Experts (human or computer) are often required to assess the probability of uncertain events.

Anomaly Detection with Score functions based on Nearest Neighbor Graphs

no code implementations NeurIPS 2009 Manqi Zhao, Venkatesh Saligrama

We propose a novel non-parametric adaptive anomaly detection algorithm for high dimensional data based on score functions derived from nearest neighbor graphs on n-point nominal data.

Anomaly Detection

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