You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

no code implementations • 26 Mar 2023 • Brett Levac, Ajil Jalal, Kannan Ramchandran, Jonathan I. Tamir

Magnetic resonance imaging (MRI) exam protocols consist of multiple contrast-weighted images of the same anatomy to emphasize different tissue properties.

no code implementations • 20 Mar 2023 • Nived Rajaraman, Devvrit, Aryan Mokhtari, Kannan Ramchandran

We study the approximate local minima of the empirical mean square error, augmented with a smooth version of a group Lasso regularizer, $\sum_{i=1}^k \| U e_i \|_2$ and show that pruning the low $\ell_2$-norm columns results in a solution $U_{\text{prune}}$ which has the minimum number of columns $r$, yet is close to the ground truth in training loss.

no code implementations • 12 Feb 2023 • Nived Rajaraman, Yanjun Han, Jiantao Jiao, Kannan Ramchandran

We consider the sequential decision-making problem where the mean outcome is a non-linear function of the chosen action.

no code implementations • 30 Jan 2023 • Justin Kang, Ramtin Pedarsani, Kannan Ramchandran

To the best of our knowledge, these are the first fairness concepts for data that explicitly consider privacy constraints.

1 code implementation • 15 Jan 2023 • Yigit Efe Erginbas, Justin Singh Kang, Amirali Aghazadeh, Kannan Ramchandran

Fourier transformations of pseudo-Boolean functions are popular tools for analyzing functions of binary sequences.

no code implementations • 13 Dec 2022 • Yigit Efe Erginbas, Soham Phade, Kannan Ramchandran

Large-scale online recommendation systems must facilitate the allocation of a limited number of items among competing users while learning their preferences from user feedback.

no code implementations • 5 Oct 2022 • Amirali Aghazadeh, Nived Rajaraman, Tony Tu, Kannan Ramchandran

Data-driven machine learning models are being increasingly employed in several important inference problems in biology, chemistry, and physics which require learning over combinatorial spaces.

no code implementations • 8 Jul 2022 • Yigit Efe Erginbas, Soham Phade, Kannan Ramchandran

Recommendation systems when employed in markets play a dual role: they assist users in selecting their most desired items from a large pool and they help in allocating a limited number of items to the users who desire them the most.

2 code implementations • 12 Jun 2022 • Zhengming Zhang, Ashwinee Panda, Linyue Song, Yaoqing Yang, Michael W. Mahoney, Joseph E. Gonzalez, Kannan Ramchandran, Prateek Mittal

In this type of attack, the goal of the attacker is to use poisoned updates to implant so-called backdoors into the learned model such that, at test time, the model's outputs can be fixed to a given target for certain inputs.

no code implementations • 31 May 2022 • Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran, Tara Javidi, Arya Mazumdar

We propose and analyze a decentralized and asynchronous learning algorithm, namely Decentralized Non-stationary Competing Bandits (\texttt{DNCB}), where the agents play (restrictive) successive elimination type learning algorithms to learn their preference over the arms.

1 code implementation • 30 May 2022 • Gokul Swamy, Nived Rajaraman, Matthew Peng, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu, Jiantao Jiao, Kannan Ramchandran

In the tabular setting or with linear function approximation, our meta theorem shows that the performance gap incurred by our approach achieves the optimal $\widetilde{O} \left( \min({H^{3/2}} / {N}, {H} / {\sqrt{N}} \right)$ dependency, under significantly weaker assumptions compared to prior work.

1 code implementation • 6 Feb 2022 • Yaoqing Yang, Ryan Theisen, Liam Hodgkinson, Joseph E. Gonzalez, Kannan Ramchandran, Charles H. Martin, Michael W. Mahoney

We also show that among the three HT distributions considered in our paper, the E-TPL fitting of ESDs performs the most robustly when the models are trained in experimental settings, while the PL fitting achieves the best performance on well-trained Huggingface models, and that both E-TPL and PL metrics (which are both shape metrics) outperform scale metrics.

no code implementations • NeurIPS 2021 • Nived Rajaraman, Yanjun Han, Lin Yang, Jingbo Liu, Jiantao Jiao, Kannan Ramchandran

In contrast, when the MDP transition structure is known to the learner such as in the case of simulators, we demonstrate fundamental differences compared to the tabular setting in terms of the performance of an optimal algorithm, Mimic-MD (Rajaraman et al. (2020)) when extended to the function approximation setting.

1 code implementation • NeurIPS 2021 • Yaoqing Yang, Liam Hodgkinson, Ryan Theisen, Joe Zou, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney

Viewing neural network models in terms of their loss landscapes has a long history in the statistical mechanics approach to learning, and in recent years it has received attention within machine learning proper.

no code implementations • 13 Jul 2021 • Avishek Ghosh, Sayak Ray Chowdhury, Kannan Ramchandran

We propose and analyze a novel algorithm, namely \emph{Adaptive Reinforcement Learning (General)} (\texttt{ARL-GEN}) that adapts to the smallest such family where the true transition kernel $P^*$ lies.

no code implementations • 7 Jul 2021 • Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran

We then show that a much simpler explore-then-commit (ETC) style algorithm also obtains a regret rate of matching that of {\ttfamily FALCON}, despite not knowing the true model class.

no code implementations • 15 Jun 2021 • Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran

We show that, for any agent, the regret scales as $\mathcal{O}(\sqrt{T/N})$, if the agent is in a `well separated' cluster, or scales as $\mathcal{O}(T^{\frac{1}{2} + \varepsilon}/(N)^{\frac{1}{2} -\varepsilon})$ if its cluster is not well separated, where $\varepsilon$ is positive and arbitrarily close to $0$.

no code implementations • 16 May 2021 • Vipul Gupta, Avishek Ghosh, Michal Derezinski, Rajiv Khanna, Kannan Ramchandran, Michael Mahoney

To enhance practicability, we devise an adaptive scheme to choose L, and we show that this reduces the number of local iterations in worker machines between two model synchronizations as the training proceeds, successively refining the model quality at the master.

no code implementations • 17 Mar 2021 • Avishek Ghosh, Raj Kumar Maity, Arya Mazumdar, Kannan Ramchandran

Moreover, we validate our theoretical findings with experiments using standard datasets and several types of Byzantine attacks, and obtain an improvement of $25\%$ with respect to first order methods in iteration complexity.

no code implementations • 25 Feb 2021 • Nived Rajaraman, Yanjun Han, Lin F. Yang, Kannan Ramchandran, Jiantao Jiao

We establish an upper bound $O(|\mathcal{S}|H^{3/2}/N)$ for the suboptimality using the Mimic-MD algorithm in Rajaraman et al (2020) which we prove to be computationally efficient.

1 code implementation • 26 Oct 2020 • Amirali Aghazadeh, Vipul Gupta, Alex DeWeese, O. Ozan Koyluoglu, Kannan Ramchandran

We consider feature selection for applications in machine learning where the dimensionality of the data is so large that it exceeds the working memory of the (local) computing machine.

no code implementations • 18 Oct 2020 • Vipul Gupta, Dhruv Choudhary, Ping Tak Peter Tang, Xiaohan Wei, Xing Wang, Yuzhen Huang, Arun Kejariwal, Kannan Ramchandran, Michael W. Mahoney

This is done by identifying and updating only the most relevant neurons of the neural network for each training sample in the data.

no code implementations • 23 Sep 2020 • Swanand Kadhe, Nived Rajaraman, O. Ozan Koyluoglu, Kannan Ramchandran

In this paper, we propose a secure aggregation protocol, FastSecAgg, that is efficient in terms of computation and communication, and robust to client dropouts.

1 code implementation • 18 Aug 2020 • Vipul Gupta, Soham Phade, Thomas Courtade, Kannan Ramchandran

As one of the fastest-growing cloud services, serverless computing provides an opportunity to better serve both users and providers through the incorporation of market-based strategies for pricing and resource allocation.

Distributed, Parallel, and Cluster Computing Computer Science and Game Theory

1 code implementation • NeurIPS 2020 • Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney

Using these observations, we show that noise-augmentation on mixup training further increases boundary thickness, thereby combating vulnerability to various forms of adversarial attacks and OOD transforms.

1 code implementation • NeurIPS 2020 • Avishek Ghosh, Jichan Chung, Dong Yin, Kannan Ramchandran

We address the problem of federated learning (FL) where users are distributed and partitioned into clusters.

no code implementations • 4 Jun 2020 • Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran

This is the first algorithm that achieves such model selection guarantees.

no code implementations • 14 May 2020 • Swanand Kadhe, O. Ozan Koyluoglu, Kannan Ramchandran

When a particular code is used in this framework, its block-length determines the computation load, dimension determines the communication overhead, and minimum distance determines the straggler tolerance.

no code implementations • 23 Apr 2020 • Avishek Ghosh, Kannan Ramchandran

Furthermore, we compare AM with a gradient based heuristic algorithm empirically and show that AM dominates in iteration complexity as well as wall-clock time.

1 code implementation • 21 Jan 2020 • Vipul Gupta, Dominic Carrano, Yaoqing Yang, Vaishaal Shankar, Thomas Courtade, Kannan Ramchandran

Inexpensive cloud services, such as serverless computing, are often vulnerable to straggling nodes that increase end-to-end latency for distributed computation.

Distributed, Parallel, and Cluster Computing Information Theory Information Theory

no code implementations • 21 Nov 2019 • Avishek Ghosh, Raj Kumar Maity, Swanand Kadhe, Arya Mazumdar, Kannan Ramchandran

Moreover, we analyze the compressed gradient descent algorithm with error feedback (proposed in \cite{errorfeed}) in a distributed setting and in the presence of Byzantine worker machines.

no code implementations • 28 Jun 2019 • Swanand Kadhe, Jichan Chung, Kannan Ramchandran

In this paper, we propose an architecture based on 'fountain codes', a class of erasure codes, that enables any full node to 'encode' validated blocks into a small number of 'coded blocks', thereby reducing its storage costs by orders of magnitude.

Cryptography and Security Distributed, Parallel, and Cluster Computing Information Theory Information Theory

no code implementations • 21 Jun 2019 • Avishek Ghosh, Ashwin Pananjady, Adityanand Guntuboyina, Kannan Ramchandran

Max-affine regression refers to a model where the unknown regression function is modeled as a maximum of $k$ unknown affine functions for a fixed $k \geq 1$.

no code implementations • 16 Jun 2019 • Avishek Ghosh, Justin Hong, Dong Yin, Kannan Ramchandran

Then, leveraging the statistical model, we solve the robust heterogeneous Federated Learning problem \emph{optimally}; in particular our algorithm matches the lower bound on the estimation error in dimension and the number of data points.

no code implementations • 9 May 2019 • Orhan Ocal, Oguz H. Elibol, Gokce Keskin, Cory Stephenson, Anil Thomas, Kannan Ramchandran

Due to the use of a single encoder, our method can generalize to converting the voice of out-of-training speakers to speakers in the training dataset.

no code implementations • ICLR 2019 • Kamil Nar, Orhan Ocal, S. Shankar Sastry, Kannan Ramchandran

In this work, we study the binary classification of linearly separable datasets and show that linear classifiers could also have decision boundaries that lie close to their training dataset if cross-entropy loss is used for training.

no code implementations • 30 Apr 2019 • Swanand Kadhe, O. Ozan Koyluoglu, Kannan Ramchandran

In this work, our goal is to construct approximate gradient codes that are resilient to stragglers selected by a computationally unbounded adversary.

1 code implementation • 21 Mar 2019 • Vipul Gupta, Swanand Kadhe, Thomas Courtade, Michael W. Mahoney, Kannan Ramchandran

Motivated by recent developments in serverless systems for large-scale computation as well as improvements in scalable randomized matrix algorithms, we develop OverSketched Newton, a randomized Hessian-based optimization algorithm to solve large-scale convex optimization problems in serverless systems.

no code implementations • 24 Jan 2019 • Kamil Nar, Orhan Ocal, S. Shankar Sastry, Kannan Ramchandran

We show that differential training can ensure a large margin between the decision boundary of the neural network and the points in the training dataset.

2 code implementations • 6 Nov 2018 • Gary Cheng, Armin Askari, Kannan Ramchandran, Laurent El Ghaoui

In this paper, we consider the problem of selecting representatives from a data set for arbitrary supervised/unsupervised learning tasks.

1 code implementation • 6 Nov 2018 • Vipul Gupta, Shusen Wang, Thomas Courtade, Kannan Ramchandran

We propose OverSketch, an approximate algorithm for distributed matrix multiplication in serverless computing.

Distributed, Parallel, and Cluster Computing Information Theory Information Theory

1 code implementation • 29 Oct 2018 • Dong Yin, Kannan Ramchandran, Peter Bartlett

For binary linear classifiers, we prove tight bounds for the adversarial Rademacher complexity, and show that the adversarial Rademacher complexity is never smaller than its natural counterpart, and it has an unavoidable dimension dependence, unless the weight vector has bounded $\ell_1$ norm.

no code implementations • 9 Jul 2018 • Avishek Ghosh, Kannan Ramchandran

We argue that the error in the score estimate accumulated over $T$ iterations is small if the regret of the online convex game is small.

no code implementations • 14 Jun 2018 • Dong Yin, Yudong Chen, Kannan Ramchandran, Peter Bartlett

In this setting, the Byzantine machines may create fake local minima near a saddle point that is far away from any true local minimum, even when robust gradient estimators are used.

1 code implementation • ICML 2018 • Dong Yin, Yudong Chen, Kannan Ramchandran, Peter Bartlett

In particular, these algorithms are shown to achieve order-optimal statistical error rates for strongly convex losses.

no code implementations • 4 Jan 2018 • Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright

Accordingly, we study the problem of finding approximate rankings from pairwise comparisons.

no code implementations • 24 Oct 2017 • Jingge Zhu, Ye Pu, Vipul Gupta, Claire Tomlin, Kannan Ramchandran

As an application of the results, we demonstrate solving optimization problems using a sequential approximation approach, which accelerates the algorithm in a distributed system with stragglers.

no code implementations • 18 Jun 2017 • Dong Yin, Ashwin Pananjady, Max Lam, Dimitris Papailiopoulos, Kannan Ramchandran, Peter Bartlett

It has been experimentally observed that distributed implementations of mini-batch stochastic gradient descent (SGD) algorithms exhibit speedup saturation and decaying generalization ability beyond a particular batch-size.

1 code implementation • ICML 2017 • Reinhard Heckel, Kannan Ramchandran

We consider the online one-class collaborative filtering (CF) problem that consists of recommending items to users over time in an online fashion based on positive ratings only.

no code implementations • 28 Jun 2016 • Reinhard Heckel, Nihar B. Shah, Kannan Ramchandran, Martin J. Wainwright

We first analyze a sequential ranking algorithm that counts the number of comparisons won, and uses these counts to decide whether to stop, or to compare another pair of items, chosen based on confidence intervals specified by the data collected up to that point.

1 code implementation • NeurIPS 2016 • Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris Papailiopoulos, Ce Zhang, Michael. I. Jordan, Kannan Ramchandran, Chris Re, Benjamin Recht

We present CYCLADES, a general framework for parallelizing stochastic optimization algorithms in a shared memory setting.

no code implementations • 8 Dec 2015 • Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris Papailiopoulos, Kannan Ramchandran

We focus on two of the most basic building blocks of distributed learning algorithms: matrix multiplication and data shuffling.

no code implementations • NeurIPS 2015 • Xiao Li, Kannan Ramchandran

By writing the cut function as a polynomial and exploiting the graph structure, we propose a sketching algorithm to learn the an arbitrary $n$-node unknown graph using only few cut queries, which scales {\it almost linearly} in the number of edges and {\it sub-linearly} in the graph size $n$.

no code implementations • 19 Sep 2015 • Frank Ong, Sameer Pawar, Kannan Ramchandran

For the case when the spatial-domain measurements are corrupted by additive noise, our 2D-FFAST framework extends to a noise-robust version in sub-linear time of O(k log4 N ) using O(k log3 N ) measurements.

Information Theory Multimedia Systems and Control Information Theory

3 code implementations • 26 Aug 2015 • Xiao Li, Joseph K. Bradley, Sameer Pawar, Kannan Ramchandran

We consider the problem of computing the Walsh-Hadamard Transform (WHT) of some $N$-length input vector in the presence of noise, where the $N$-point Walsh spectrum is $K$-sparse with $K = {O}(N^{\delta})$ scaling sub-linearly in the input dimension $N$ for some $0<\delta<1$.

no code implementations • 24 Jul 2015 • Horia Mania, Xinghao Pan, Dimitris Papailiopoulos, Benjamin Recht, Kannan Ramchandran, Michael. I. Jordan

We demonstrate experimentally on a 16-core machine that the sparse and parallel version of SVRG is in some cases more than four orders of magnitude faster than the standard SVRG algorithm.

no code implementations • NeurIPS 2015 • Xinghao Pan, Dimitris Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael. I. Jordan

We present C4 and ClusterWild!, two algorithms for parallel correlation clustering that run in a polylogarithmic number of rounds and achieve nearly linear speedups, provably.

no code implementations • 6 May 2015 • Nihar B. Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramchandran, Martin J. Wainwright

Data in the form of pairwise comparisons arises in many domains, including preference elicitation, sporting competitions, and peer grading among others.

no code implementations • 1 Jan 2015 • Sameer Pawar, Kannan Ramchandran

If the DFT X of the signal x has only k non-zero coefficients (where k < n), can we do better?

no code implementations • 25 Jun 2014 • Nihar B. Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramchandran, Martin Wainwright

When eliciting judgements from humans for an unknown quantity, one often has the choice of making direct-scoring (cardinal) or comparative (ordinal) measurements.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.