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no code implementations • 13 Jul 2023 • Liam Collins, Hamed Hassani, Mahdi Soltanolkotabi, Aryan Mokhtari, Sanjay Shakkottai

In the multi-task learning setting, a variety of studies have shown effective feature learning by simple linear models.

1 code implementation • 2 Jul 2023 • Litu Rout, Negin Raoof, Giannis Daras, Constantine Caramanis, Alexandros G. Dimakis, Sanjay Shakkottai

We present the first framework to solve linear inverse problems leveraging pre-trained latent diffusion models.

no code implementations • 30 May 2023 • Ronshee Chawla, Daniel Vial, Sanjay Shakkottai, R. Srikant

The study of collaborative multi-agent bandits has attracted significant attention recently.

no code implementations • 15 Feb 2023 • Advait Parulekar, Liam Collins, Karthikeyan Shanmugam, Aryan Mokhtari, Sanjay Shakkottai

The goal of contrasting learning is to learn a representation that preserves underlying clusters by keeping samples with similar content, e. g. the ``dogness'' of a dog, close to each other in the space generated by the representation.

no code implementations • 13 Feb 2023 • Matthew Faw, Litu Rout, Constantine Caramanis, Sanjay Shakkottai

Despite the richness, an emerging line of works achieves the $\widetilde{\mathcal{O}}(\frac{1}{\sqrt{T}})$ rate of convergence when the noise of the stochastic gradients is deterministically and uniformly bounded.

no code implementations • 2 Feb 2023 • Litu Rout, Advait Parulekar, Constantine Caramanis, Sanjay Shakkottai

To the best of our knowledge, this is the first linear convergence result for a diffusion based image inpainting algorithm.

no code implementations • 30 May 2022 • Advait Parulekar, Karthikeyan Shanmugam, Sanjay Shakkottai

These are representations of the covariates such that the best model on top of the representation is invariant across training environments.

no code implementations • 29 May 2022 • Orestis Papadigenopoulos, Constantine Caramanis, Sanjay Shakkottai

Even assuming prior knowledge of the mean payoff functions, computing an optimal planning in the above model is NP-hard, while the state-of-the-art is a $1/4$-approximation algorithm for the case where at most one arm can be played per round.

no code implementations • 27 May 2022 • Liam Collins, Hamed Hassani, Aryan Mokhtari, Sanjay Shakkottai

We show that the reason behind generalizability of the FedAvg's output is its power in learning the common data representation among the clients' tasks, by leveraging the diversity among client data distributions via local updates.

no code implementations • 23 Mar 2022 • Daniel Vial, Sujay Sanghavi, Sanjay Shakkottai, R. Srikant

Cascading bandits is a natural and popular model that frames the task of learning to rank from Bernoulli click feedback in a bandit setting.

no code implementations • 28 Feb 2022 • Daniel Vial, Sanjay Shakkottai, R. Srikant

Thus, we generalize existing regret bounds beyond the complete graph (where $d_{\text{mal}}(i) = m$), and show the effect of malicious agents is entirely local (in the sense that only the $d_{\text{mal}}(i)$ malicious agents directly connected to $i$ affect its long-term regret).

no code implementations • 11 Feb 2022 • Matthew Faw, Isidoros Tziotis, Constantine Caramanis, Aryan Mokhtari, Sanjay Shakkottai, Rachel Ward

We study convergence rates of AdaGrad-Norm as an exemplar of adaptive stochastic gradient methods (SGD), where the step sizes change based on observed stochastic gradients, for minimizing non-convex, smooth objectives.

no code implementations • 7 Feb 2022 • Liam Collins, Aryan Mokhtari, Sewoong Oh, Sanjay Shakkottai

Recent empirical evidence has driven conventional wisdom to believe that gradient-based meta-learning (GBML) methods perform well at few-shot learning because they learn an expressive data representation that is shared across tasks.

no code implementations • 12 Sep 2021 • Daniel Vial, Advait Parulekar, Sanjay Shakkottai, R. Srikant

(P1) Its regret after $K$ episodes scales as $K \max \{ \varepsilon_{\text{mis}}, \varepsilon_{\text{tol}} \}$, where $\varepsilon_{\text{mis}}$ is the degree of misspecification and $\varepsilon_{\text{tol}}$ is a user-specified error tolerance.

no code implementations • 7 Jul 2021 • Nihal Sharma, Soumya Basu, Karthikeyan Shanmugam, Sanjay Shakkottai

The agent interacts with the environment over episodes, with each episode having different context distributions; this results in the `best expert' changing across episodes.

no code implementations • NeurIPS 2021 • Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, Karthikeyan Shanmugam

Our key step is to show that the generalized Bellman operator is simultaneously a contraction mapping with respect to a weighted $\ell_p$-norm for each $p$ in $[1,\infty)$, with a common contraction factor.

1 code implementation • 21 Jun 2021 • Steven Gutstein, Brent Lance, Sanjay Shakkottai

Fine-tuning of pre-trained deep nets is commonly used to improve accuracies and training times for neural nets.

no code implementations • 8 Jun 2021 • Tuhinangshu Choudhury, Gauri Joshi, Weina Wang, Sanjay Shakkottai

In multi-server queueing systems where there is no central queue holding all incoming jobs, job dispatching policies are used to assign incoming jobs to the queue at one of the servers.

no code implementations • 22 May 2021 • Alexia Atsidakou, Orestis Papadigenopoulos, Soumya Basu, Constantine Caramanis, Sanjay Shakkottai

Recent work has considered natural variations of the multi-armed bandit problem, where the reward distribution of each arm is a special function of the time passed since its last pulling.

no code implementations • 4 May 2021 • Daniel Vial, Advait Parulekar, Sanjay Shakkottai, R. Srikant

We propose an algorithm that uses linear function approximation (LFA) for stochastic shortest path (SSP).

no code implementations • 3 Mar 2021 • Shuo Yang, Tongzheng Ren, Sanjay Shakkottai, Eric Price, Inderjit S. Dhillon, Sujay Sanghavi

For sufficiently large $K$, our algorithms have sublinear per-step complexity and $\tilde O(\sqrt{T})$ regret.

4 code implementations • 14 Feb 2021 • Liam Collins, Hamed Hassani, Aryan Mokhtari, Sanjay Shakkottai

Based on this intuition, we propose a novel federated learning framework and algorithm for learning a shared data representation across clients and unique local heads for each client.

no code implementations • 2 Feb 2021 • Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, Karthikeyan Shanmugam

As a by-product, by analyzing the convergence bounds of $n$-step TD and TD$(\lambda)$, we provide theoretical insights into the bias-variance trade-off, i. e., efficiency of bootstrapping in RL.

no code implementations • 4 Dec 2020 • Daniel Vial, Sanjay Shakkottai, R. Srikant

We consider a variant of the traditional multi-armed bandit problem in which each arm is only able to provide one-bit feedback during each pull based on its past history of rewards.

no code implementations • 2 Nov 2020 • Advait Parulekar, Soumya Basu, Aditya Gopalan, Karthikeyan Shanmugam, Sanjay Shakkottai

We study a variant of the stochastic linear bandit problem wherein we optimize a linear objective function but rewards are accrued only orthogonal to an unknown subspace (which we interpret as a \textit{protected space}) given only zero-order stochastic oracle access to both the objective itself and protected subspace.

no code implementations • 27 Oct 2020 • Liam Collins, Aryan Mokhtari, Sanjay Shakkottai

Model-Agnostic Meta-Learning (MAML) has become increasingly popular for training models that can quickly adapt to new tasks via one or few stochastic gradient descent steps.

no code implementations • 14 Sep 2020 • Arghyadip Roy, Sanjay Shakkottai, R. Srikant

rewards are a special case of Markov rewards and it is difficult to design an algorithm that works well independent of whether the underlying model is truly Markovian or i. i. d.

no code implementations • 7 Jul 2020 • Daniel Vial, Sanjay Shakkottai, R. Srikant

Recent works have shown that agents facing independent instances of a stochastic $K$-armed bandit can collaborate to decrease regret.

no code implementations • 2 Jul 2020 • Ronshee Chawla, Abishek Sankararaman, Sanjay Shakkottai

We study a multi-agent stochastic linear bandit with side information, parameterized by an unknown vector $\theta^* \in \mathbb{R}^d$.

no code implementations • 6 Mar 2020 • Soumya Basu, Orestis Papadigenopoulos, Constantine Caramanis, Sanjay Shakkottai

Assuming knowledge of the context distribution and the mean reward of each arm-context pair, we cast the problem as an online bipartite matching problem, where the right-vertices (contexts) arrive stochastically and the left-vertices (arms) are blocked for a finite number of rounds each time they are matched.

no code implementations • 19 Feb 2020 • Nihal Sharma, Soumya Basu, Karthikeyan Shanmugam, Sanjay Shakkottai

We study a variant of the multi-armed bandit problem where side information in the form of bounds on the mean of each arm is provided.

no code implementations • NeurIPS 2020 • Liam Collins, Aryan Mokhtari, Sanjay Shakkottai

Meta-learning methods have shown an impressive ability to train models that rapidly learn new tasks.

no code implementations • NeurIPS 2020 • Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, Karthikeyan Shanmugam

In particular, we use it to establish the first-known convergence rate of the V-trace algorithm for off-policy TD-learning.

no code implementations • 15 Jan 2020 • Ronshee Chawla, Abishek Sankararaman, Ayalvadi Ganesh, Sanjay Shakkottai

Agents use the communication medium to recommend only arm-IDs (not samples), and thus update the set of arms from which they play.

no code implementations • 4 Nov 2019 • Negin Musavi, Dawei Sun, Sayan Mitra, Geir Dullerud, Sanjay Shakkottai

As a consequence, we obtain theoretical regret bounds on sample efficiency of our solution that depends on key problem parameters like smoothness, near-optimality dimension, and batch size.

no code implementations • 4 Oct 2019 • Abishek Sankararaman, Ayalvadi Ganesh, Sanjay Shakkottai

Our setting consists of a large number of agents $n$ that collaboratively and simultaneously solve the same instance of $K$ armed MAB to minimize the average cumulative regret over all agents.

no code implementations • NeurIPS 2019 • Soumya Basu, Rajat Sen, Sujay Sanghavi, Sanjay Shakkottai

We show that with prior knowledge of the rewards and delays of all the arms, the problem of optimizing cumulative reward does not admit any pseudo-polynomial time algorithm (in the number of arms) unless randomized exponential time hypothesis is false, by mapping to the PINWHEEL scheduling problem.

1 code implementation • NeurIPS 2020 • Matthew Faw, Rajat Sen, Karthikeyan Shanmugam, Constantine Caramanis, Sanjay Shakkottai

We consider a covariate shift problem where one has access to several different training datasets for the same learning problem and a small validation set which possibly differs from all the individual training distributions.

1 code implementation • 24 Oct 2018 • Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai

We study the problem of black-box optimization of a noisy function in the presence of low-cost approximations or fidelities, which is motivated by problems like hyper-parameter tuning.

no code implementations • NeurIPS 2020 • Murat Kocaoglu, Sanjay Shakkottai, Alexandros G. Dimakis, Constantine Caramanis, Sriram Vishwanath

We study the problem of discovering the simplest latent variable that can make two observed discrete variables conditionally independent.

no code implementations • ICML 2018 • Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai

Motivated by settings such as hyper-parameter tuning and physical simulations, we consider the problem of black-box optimization of a function.

no code implementations • 7 Jun 2018 • Maurice Diesendruck, Ethan R. Elenberg, Rajat Sen, Guy W. Cole, Sanjay Shakkottai, Sinead A. Williamson

Deep generative networks can simulate from a complex target distribution, by minimizing a loss with respect to samples from that distribution.

1 code implementation • 23 Feb 2018 • Rajat Sen, Karthikeyan Shanmugam, Nihal Sharma, Sanjay Shakkottai

We consider the problem of contextual bandits with stochastic experts, which is a variation of the traditional stochastic contextual bandit with experts problem.

1 code implementation • NeurIPS 2017 • Rajat Sen, Ananda Theertha Suresh, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai

We consider the problem of non-parametric Conditional Independence testing (CI testing) for continuous random variables.

no code implementations • ICML 2017 • Rajat Sen, Karthikeyan Shanmugam, Alexandros G. Dimakis, Sanjay Shakkottai

Motivated by applications in computational advertising and systems biology, we consider the problem of identifying the best out of several possible soft interventions at a source node $V$ in an acyclic causal directed graph, to maximize the expected value of a target node $Y$ (located downstream of $V$).

no code implementations • NeurIPS 2016 • Subhashini Krishnasamy, Rajat Sen, Ramesh Johari, Sanjay Shakkottai

A naive view of this problem would suggest that queue-regret should grow logarithmically: since queue-regret cannot be larger than classical regret, results for the standard MAB problem give algorithms that ensure queue-regret increases no more than logarithmically in time.

no code implementations • 4 Oct 2016 • Avik Ray, Joe Neeman, Sujay Sanghavi, Sanjay Shakkottai

We consider the task of learning the parameters of a {\em single} component of a mixture model, for the case when we are given {\em side information} about that component, we call this the "search problem" in mixture models.

no code implementations • 1 Jun 2016 • Rajat Sen, Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, Sanjay Shakkottai

Our algorithm achieves a regret of $\mathcal{O}\left(L\mathrm{poly}(m, \log K) \log T \right)$ at time $T$, as compared to $\mathcal{O}(LK\log T)$ for conventional contextual bandits, assuming a constant gap between the best arm and the rest for each context.

no code implementations • 7 Nov 2014 • Siddhartha Banerjee, Sujay Sanghavi, Sanjay Shakkottai

We consider this problem under a simple natural model, wherein the number of items and the number of item-views are of the same order, and an `access-graph' constrains which user is allowed to see which item.

no code implementations • 6 Feb 2014 • Eli A. Meirom, Chris Milling, Constantine Caramanis, Shie Mannor, Ariel Orda, Sanjay Shakkottai

Our algorithm requires only local-neighbor knowledge of this graph, and in a broad array of settings that we describe, succeeds even when false negatives and false positives make up an overwhelming fraction of the data available.

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