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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.

no code implementations • 21 Sep 2023 • Ruizhao Zhu, Peng Huang, Eshed Ohn-Bar, Venkatesh Saligrama

Human drivers can seamlessly adapt their driving decisions across geographical locations with diverse conditions and rules of the road, e. g., left vs. right-hand traffic.

1 code implementation • International Conference on Learning Representations 2023 • Anil Kag, Igor Fedorov, Aditya Gangrade, Paul Whatmough, Venkatesh Saligrama

Training a hybrid learner is difficult since we lack annotations of hard edge-examples.

1 code implementation • International Conference on Learning Representations 2023 • Anil Kag, Durmus Alp Emre Acar, Aditya Gangrade, Venkatesh Saligrama

We propose a novel knowledge distillation (KD) method to selectively instill teacher knowledge into a student model motivated by situations where the student's capacity is significantly smaller than that of the teachers.

no code implementations • 1 Feb 2023 • Chen Chen, Dylan Walker, Venkatesh Saligrama

We propose a novel supervised learning approach for political ideology prediction (PIP) that is capable of predicting out-of-distribution inputs.

no code implementations • 27 Sep 2022 • Tianrui Chen, Aditya Gangrade, Venkatesh Saligrama

The safe linear bandit problem (SLB) is an online approach to linear programming with unknown objective and unknown round-wise constraints, under stochastic bandit feedback of rewards and safety risks of actions.

no code implementations • 14 Jul 2022 • Ruizhao Zhu, Pengkai Zhu, Samarth Mishra, Venkatesh Saligrama

An object is parsed by estimating the locations of these K parts and a set of active templates that can reconstruct the part features.

no code implementations • 7 Jul 2022 • Durmus Alp Emre Acar, Venkatesh Saligrama

We propose a novel training recipe for federated learning with heterogeneous networks where each device can have different architectures.

no code implementations • 17 Apr 2022 • Samarth Mishra, Pengkai Zhu, Venkatesh Saligrama

RPC encodes images by first decomposing them into salient parts, and then encoding each part as a mixture of a small number of prototypes, each representing a certain concept.

no code implementations • 1 Apr 2022 • Tianrui Chen, Aditya Gangrade, Venkatesh Saligrama

We investigate a natural but surprisingly unstudied approach to the multi-armed bandit problem under safety risk constraints.

1 code implementation • CVPR 2022 • Anil Kag, Venkatesh Saligrama

Convolutional neural networks (CNNs) rely on the depth of the architecture to obtain complex features.

no code implementations • CVPR 2022 • Samarth Mishra, Rameswar Panda, Cheng Perng Phoo, Chun-Fu (Richard) Chen, Leonid Karlinsky, Kate Saenko, Venkatesh Saligrama, Rogerio S. Feris

It is thus better to tailor synthetic pre-training data to a specific downstream task, for best performance.

no code implementations • 30 Nov 2021 • Samarth Mishra, Rameswar Panda, Cheng Perng Phoo, Chun-Fu Chen, Leonid Karlinsky, Kate Saenko, Venkatesh Saligrama, Rogerio S. Feris

It is thus better to tailor synthetic pre-training data to a specific downstream task, for best performance.

3 code implementations • 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.

no code implementations • 2 Nov 2021 • Ali Siahkamari, Durmus Alp Emre Acar, Christopher Liao, Kelly Geyer, Venkatesh Saligrama, Brian Kulis

For the task of convex Lipschitz regression, we establish that our proposed algorithm converges with iteration complexity of $ O(n\sqrt{d}/\epsilon)$ for a dataset $\bm X \in \mathbb R^{n\times d}$ and $\epsilon > 0$.

1 code implementation • NeurIPS 2021 • Aditya Gangrade, Anil Kag, Ashok Cutkosky, Venkatesh Saligrama

For example, this may model an adaptive decision to invoke more resources on this instance.

1 code implementation • 29 Sep 2021 • Anil Kag, Igor Fedorov, Aditya Gangrade, Paul Whatmough, Venkatesh Saligrama

The first network is a low-capacity network that can be deployed on an edge device, whereas the second is a high-capacity network deployed in the cloud.

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.

1 code implementation • International Conference on Machine Learning 2021 • Anil Kag, Venkatesh Saligrama

BPTT updates RNN parameters on an instance by back-propagating the error in time over the entire sequence length, and as a result, leads to poor trainability due to the well-known gradient explosion/decay phenomena.

1 code implementation • 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.

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.

1 code implementation • 29 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.

Semi-supervised Domain Adaptation Unsupervised Domain Adaptation

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.

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.

1 code implementation • 15 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).

1 code implementation • 12 Oct 2020 • Yun Yue, Ming Li, Venkatesh Saligrama, Ziming Zhang

We propose to utilize the Frank-Wolfe (FW) algorithm in this context.

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.

1 code implementation • 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.

no code implementations • 14 Apr 2020 • Aditya Gangrade, Durmus Alp Emre Acar, Venkatesh Saligrama

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

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 $\mathrm{SNR}= \Theta(\log n)$.

2 code implementations • NeurIPS 2019 • Don Dennis, Durmus Alp Emre Acar, Vikram Mandikal, Vinu Sankar Sadasivan, Venkatesh Saligrama, Harsha Vardhan Simhadri, Prateek Jain

The second layer consumes the output of the first layer using a second RNN thus capturing long dependencies.

no code implementations • 18 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.

no code implementations • 22 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.

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.

no code implementations • ICML 2018 • Yao Ma, Alex Olshevsky, Venkatesh Saligrama, Csaba Szepesvari

We consider worker skill estimation for the single-coin Dawid-Skene crowdsourcing model.

no code implementations • 2 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.

no code implementations • 1 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).

no code implementations • 25 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.

no code implementations • 15 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.

no code implementations • 29 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 $\mathrm{SNR}= \Theta(\log n)$.

no code implementations • CVPR 2019 • Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama

To bridge the gap, we propose a novel low-dimensional embedding of visual instances that is "visually semantic."

no code implementations • ICCV 2019 • Hanxiao Wang, Venkatesh Saligrama, Stan Sclaroff, Vitaly Ablavsky

We consider the problem of fine-grained classification on an edge camera device that has limited power.

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.

1 code implementation • 19 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.

no code implementations • 17 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.

no code implementations • 28 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.

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.

no code implementations • 20 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.

no code implementations • 31 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.

no code implementations • NeurIPS 2017 • Feng Nan, Venkatesh Saligrama

Our novel bottom-up method first trains a high-accuracy complex model.

no code implementations • 10 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".

no code implementations • 25 Apr 2017 • Feng Nan, Venkatesh Saligrama

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

no code implementations • 10 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.

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.

no code implementations • 23 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.

no code implementations • 18 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.

no code implementations • 26 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.

8 code implementations • NeurIPS 2016 • Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, Adam Kalai

Geometrically, gender bias is first shown to be captured by a direction in the word embedding.

no code implementations • 20 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.

no code implementations • NeurIPS 2016 • Feng Nan, Joseph Wang, Venkatesh Saligrama

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

no code implementations • 28 Feb 2016 • Tolga Bolukbasi, Kai-Wei Chang, Joseph Wang, Venkatesh Saligrama

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

no code implementations • 22 Jan 2016 • Jonathan Root, Venkatesh Saligrama, Jing Qian

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

no code implementations • 5 Jan 2016 • Feng Nan, Joseph Wang, Venkatesh Saligrama

We propose a novel 0-1 integer program formulation for ensemble pruning.

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.

no code implementations • 14 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.

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.

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.

no code implementations • 26 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 $\epsilon >0$.

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.

no code implementations • ICCV 2015 • Ziming Zhang, Venkatesh Saligrama

In this paper we consider a version of the zero-shot learning problem where seen class source and target domain data are provided.

no code implementations • 9 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.

no code implementations • 23 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.

no code implementations • 15 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}.

no code implementations • 3 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.

no code implementations • 20 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.

no code implementations • 6 Feb 2015 • Jing Qian, Jonathan Root, Venkatesh Saligrama

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

no code implementations • 21 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.

no code implementations • 12 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.

no code implementations • 11 Dec 2014 • Weicong Ding, Prakash Ishwar, Venkatesh Saligrama

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

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.

no code implementations • 24 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.

no code implementations • 13 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.

no code implementations • 13 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.

no code implementations • 2 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.

no code implementations • 1 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).

no code implementations • 12 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.

no code implementations • 24 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.

no code implementations • 2 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.

no code implementations • 30 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.

no code implementations • 9 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.

no code implementations • 2 Apr 2013 • Cem Aksoylar, George Atia, Venkatesh Saligrama

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

no code implementations • 29 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.

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.

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.

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.

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