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Differentially-Private Federated Linear Bandits
[article]
2020
arXiv
pre-print
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The rapid proliferation of decentralized learning systems mandates the need for differentially-private cooperative learning. In this paper, we study this in context of the contextual linear bandit: we consider a collection of agents cooperating to solve a common contextual bandit, while ensuring that their communication remains private. For this problem, we devise FedUCB, a multiagent private algorithm for both centralized and decentralized (peer-to-peer) federated learning. We provide a
arXiv:2010.11425v1
fatcat:mbuav4eq7vfhbbega56pda3h4m
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... s technical analysis of its utility in terms of regret, improving several results in cooperative bandit learning, and provide rigorous privacy guarantees as well. Our algorithms provide competitive performance both in terms of pseudoregret bounds and empirical benchmark performance in various multi-agent settings.
Coreset-Based Neural Network Compression
[article]
2018
arXiv
pre-print
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We propose a novel Convolutional Neural Network (CNN) compression algorithm based on coreset representations of filters. We exploit the redundancies extant in the space of CNN weights and neuronal activations (across samples) in order to obtain compression. Our method requires no retraining, is easy to implement, and obtains state-of-the-art compression performance across a wide variety of CNN architectures. Coupled with quantization and Huffman coding, we create networks that provide
arXiv:1807.09810v1
fatcat:r5deensjdjhfjcfdxjkrfjaopi
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... ke accuracy, with a memory footprint that is 832× smaller than the original AlexNet, while also introducing significant reductions in inference time as well. Additionally these compressed networks when fine-tuned, successfully generalize to other domains as well.
Coreset-Based Adaptive Tracking
[article]
2015
arXiv
pre-print
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We propose a method for learning from streaming visual data using a compact, constant size representation of all the data that was seen until a given moment. Specifically, we construct a 'coreset' representation of streaming data using a parallelized algorithm, which is an approximation of a set with relation to the squared distances between this set and all other points in its ambient space. We learn an adaptive object appearance model from the coreset tree in constant time and logarithmic
arXiv:1511.06147v1
fatcat:zujkqnzvnjgytgarjfdanibhoa
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... e and use it for object tracking by detection. Our method obtains excellent results for object tracking on three standard datasets over more than 100 videos. The ability to summarize data efficiently makes our method ideally suited for tracking in long videos in presence of space and time constraints. We demonstrate this ability by outperforming a variety of algorithms on the TLD dataset with 2685 frames on average. This coreset based learning approach can be applied for both real-time learning of small, varied data and fast learning of big data.
Cooperative Multi-Agent Bandits with Heavy Tails
[article]
2020
arXiv
pre-print
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Correspondence to: Abhimanyu Dubey <dubeya@mit.edu>. Proceedings of the 37 th International Conference on Machine Learning, Online, PMLR 119, 2020. Copyright 2020 by the author(s). ...
Dubey & Pentland (2019) provide an algorithm for Thompson Sampling for α-stable densities (Borak et al., 2005) , at family of heavy-tailed densities typically with infinite variance. ...
arXiv:2008.06244v1
fatcat:2cjp4v6hrnccbdjmfdrhkc2gru
Smaller Models, Better Generalization
[article]
2019
arXiv
pre-print
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Dubey Jayadeva
arXiv:1908.11250v1 [cs.LG] 29 Aug 2019
MIT Indian ...
eez142368@iitd.ac.in surajtripathi93@gmail.com
Abhimanyu ...
arXiv:1908.11250v1
fatcat:lkqvczu4tjferlq2fbuual6jrm
Modeling Image Virality with Pairwise Spatial Transformer Networks
[article]
2017
arXiv
pre-print
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The study of virality and information diffusion online is a topic gaining traction rapidly in the computational social sciences. Computer vision and social network analysis research have also focused on understanding the impact of content and information diffusion in making content viral, with prior approaches not performing significantly well as other traditional classification tasks. In this paper, we present a novel pairwise reformulation of the virality prediction problem as an attribute
arXiv:1709.07914v1
fatcat:xavvzwxeqbgbnitp44bbmqmcdy
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... diction task and develop a novel algorithm to model image virality on online media using a pairwise neural network. Our model provides significant insights into the features that are responsible for promoting virality and surpasses the existing state-of-the-art by a 12% average improvement in prediction. We also investigate the effect of external category supervision on relative attribute prediction and observe an increase in prediction accuracy for the same across several attribute learning datasets.
Kernel Methods for Cooperative Multi-Agent Contextual Bandits
[article]
2020
arXiv
pre-print
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Correspondence to: Abhimanyu Dubey <dubeya@mit.edu>. Proceedings of the 37 th International Conference on Machine Learning, Online, PMLR 119, 2020. ...
arXiv:2008.06220v1
fatcat:4dkbeqn3fnc5zejptl5bznyole
Evaluating Generative Adversarial Networks on Explicitly Parameterized Distributions
[article]
2018
arXiv
pre-print
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The true distribution parameterizations of commonly used image datasets are inaccessible. Rather than designing metrics for feature spaces with unknown characteristics, we propose to measure GAN performance by evaluating on explicitly parameterized, synthetic data distributions. As a case study, we examine the performance of 16 GAN variants on six multivariate distributions of varying dimensionalities and training set sizes. In this learning environment, we observe that: GANs exhibit similar
arXiv:1812.10782v1
fatcat:vkeo3bw42vajfbewfefx7645l4
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... formance trends across dimensionalities; learning depends on the underlying distribution and its complexity; the number of training samples can have a large impact on performance; evaluation and relative comparisons are metric-dependent; diverse sets of hyperparameters can produce a "best" result; and some GANs are more robust to hyperparameter changes than others. These observations both corroborate findings of previous GAN evaluation studies and make novel contributions regarding the relationship between size, complexity, and GAN performance.
MemeSequencer: Sparse Matching for Embedding Image Macros
[article]
2018
arXiv
pre-print
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Approaches that take context into account, such as the work of Singh and Lee [50] , and Dubey and Agarwal [24] perform much better, however, they face challenges in decoupling text information. ...
SkipThought Features [33] 52.95 SVM + Finetuned ResNet18 + Word2VecPooling [40] 55.68 SVM + Finetuned ResNet18 + SkipThought Features [33] 56.35 Xiao and Lee [57] 62.67 Singh and Lee [50] 61.98 Dubey ...
arXiv:1802.04936v1
fatcat:ratefwcsgfh45c6fhynb2gta44
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation
[article]
2021
arXiv
pre-print
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., 2019; Dubey & Pentland, 2020b) , as well as in the distributed settings (Hillel et al., 2013; Wang et al., 2019) . ...
Differentially-private algorithms have also been proposed (Dubey & Pentland, 2020b) . Reinforcement Learning with Function Approximation. ...
arXiv:2103.04972v1
fatcat:okyksfg4yffnrk6gs3hi6i4wkq
Examining Representational Similarity in ConvNets and the Primate Visual Cortex
[article]
2016
arXiv
pre-print
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We compare several ConvNets with different depth and regularization techniques with multi-unit macaque IT cortex recordings and assess the impact of the same on representational similarity with the primate visual cortex. We find that with increasing depth and validation performance, ConvNet features are closer to cortical IT representations.
arXiv:1609.03529v1
fatcat:igklyrtrgbf3xjt6j7oresjhj4
Maximum-Entropy Fine-Grained Classification
[article]
2018
arXiv
pre-print
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Fine-Grained Visual Classification (FGVC) is an important computer vision problem that involves small diversity within the different classes, and often requires expert annotators to collect data. Utilizing this notion of small visual diversity, we revisit Maximum-Entropy learning in the context of fine-grained classification, and provide a training routine that maximizes the entropy of the output probability distribution for training convolutional neural networks on FGVC tasks. We provide a
arXiv:1809.05934v2
fatcat:7sisnsbvrraytjp3bkhdlybxhq
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... retical as well as empirical justification of our approach, and achieve state-of-the-art performance across a variety of classification tasks in FGVC, that can potentially be extended to any fine-tuning task. Our method is robust to different hyperparameter values, amount of training data and amount of training label noise and can hence be a valuable tool in many similar problems.
Thompson Sampling on Symmetric α-Stable Bandits
[article]
2019
arXiv
pre-print
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Thompson Sampling provides an efficient technique to introduce prior knowledge in the multi-armed bandit problem, along with providing remarkable empirical performance. In this paper, we revisit the Thompson Sampling algorithm under rewards drawn from symmetric α-stable distributions, which are a class of heavy-tailed probability distributions utilized in finance and economics, in problems such as modeling stock prices and human behavior. We present an efficient framework for posterior
arXiv:1907.03821v2
fatcat:adndtf42g5c3bndlf5vlso2tge
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... , which leads to two algorithms for Thompson Sampling in this setting. We prove finite-time regret bounds for both algorithms, and demonstrate through a series of experiments the stronger performance of Thompson Sampling in this setting. With our results, we provide an exposition of symmetric α-stable distributions in sequential decision-making, and enable sequential Bayesian inference in applications from diverse fields in finance and complex systems that operate on heavy-tailed features.
No Peek: A Survey of private distributed deep learning
[article]
2018
arXiv
pre-print
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We survey distributed deep learning models for training or inference without accessing raw data from clients. These methods aim to protect confidential patterns in data while still allowing servers to train models. The distributed deep learning methods of federated learning, split learning and large batch stochastic gradient descent are compared in addition to private and secure approaches of differential privacy, homomorphic encryption, oblivious transfer and garbled circuits in the context of
arXiv:1812.03288v1
fatcat:7w3oheeljrgejit6dsj54ay5nq
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... neural networks. We study their benefits, limitations and trade-offs with regards to computational resources, data leakage and communication efficiency and also share our anticipated future trends.
Adaptive Methods for Aggregated Domain Generalization
[article]
2021
arXiv
pre-print
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Domain generalization involves learning a classifier from a heterogeneous collection of training sources such that it generalizes to data drawn from similar unknown target domains, with applications in large-scale learning and personalized inference. In many settings, privacy concerns prohibit obtaining domain labels for the training data samples, and instead only have an aggregated collection of training points. Existing approaches that utilize domain labels to create domain-invariant feature
arXiv:2112.04766v2
fatcat:xsi3whmwevfitjevpivjxkr2a4
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... epresentations are inapplicable in this setting, requiring alternative approaches to learn generalizable classifiers. In this paper, we propose a domain-adaptive approach to this problem, which operates in two steps: (a) we cluster training data within a carefully chosen feature space to create pseudo-domains, and (b) using these pseudo-domains we learn a domain-adaptive classifier that makes predictions using information about both the input and the pseudo-domain it belongs to. Our approach achieves state-of-the-art performance on a variety of domain generalization benchmarks without using domain labels whatsoever. Furthermore, we provide novel theoretical guarantees on domain generalization using cluster information. Our approach is amenable to ensemble-based methods and provides substantial gains even on large-scale benchmark datasets. The code can be found at: https://github.com/xavierohan/AdaClust_DomainBed
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