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no code implementations • 17 Nov 2021 • Bruce D. Lee, Thomas T. C. K. Zhang, Hamed Hassani, Nikolai Matni

Adversarially robust training has been shown to reduce the susceptibility of learned models to targeted input data perturbations.

no code implementations • 1 Nov 2021 • Arman Adibi, Aryan Mokhtari, Hamed Hassani

Prior literature has thus far mainly focused on studying such problems in the continuous domain, e. g., convex-concave minimax optimization is now understood to a significant extent.

no code implementations • NeurIPS 2021 • Alexander Robey, Luiz F. O. Chamon, George J. Pappas, Hamed Hassani, Alejandro Ribeiro

In particular, we leverage semi-infinite optimization and non-convex duality theory to show that adversarial training is equivalent to a statistical problem over perturbation distributions, which we characterize completely.

no code implementations • 13 Oct 2021 • Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti

In recent years, deep neural network (DNN) compression systems have proved to be highly effective for designing source codes for many natural sources.

no code implementations • 13 Sep 2021 • Aritra Mitra, Hamed Hassani, George Pappas

We study a federated variant of the best-arm identification problem in stochastic multi-armed bandits: a set of clients, each of whom can sample only a subset of the arms, collaborate via a server to identify the best arm (i. e., the arm with the highest mean reward) with prescribed confidence.

no code implementations • 28 Jun 2021 • Francisco Barreras, Mikhail Hayhoe, Hamed Hassani, Victor M. Preciado

The likelihood of the observations is estimated recursively using an Extended Kalman Filter and can be easily optimized using gradient-based methods to compute maximum likelihood estimators.

no code implementations • 5 Apr 2021 • Payam Delgosha, Hamed Hassani, Ramtin Pedarsani

Under the assumption that data is distributed according to the Gaussian mixture model, our goal is to characterize the optimal robust classifier and the corresponding robust classification error as well as a variety of trade-offs between robustness, accuracy, and the adversary's budget.

no code implementations • 11 Mar 2021 • Zebang Shen, Hamed Hassani, Satyen Kale, Amin Karbasi

First, in the semi-heterogeneous setting, when the marginal distributions of the feature vectors on client machines are identical, we develop the federated functional gradient boosting (FFGB) method that provably converges to the global minimum.

1 code implementation • NeurIPS 2021 • Alexander Robey, George J. Pappas, Hamed Hassani

Despite remarkable success in a variety of applications, it is well-known that deep learning can fail catastrophically when presented with out-of-distribution data.

no code implementations • NeurIPS 2021 • Aritra Mitra, Rayana Jaafar, George J. Pappas, Hamed Hassani

We consider a standard federated learning (FL) architecture where a group of clients periodically coordinate with a central server to train a statistical model.

1 code implementation • 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 • 28 Dec 2020 • Amirhossein Reisizadeh, Isidoros Tziotis, Hamed Hassani, Aryan Mokhtari, Ramtin Pedarsani

Federated Learning is a novel paradigm that involves learning from data samples distributed across a large network of clients while the data remains local.

no code implementations • NeurIPS 2020 • Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani

In this regard, we propose a novel Sinkhorn Natural Gradient (SiNG) algorithm which acts as a steepest descent method on the probability space endowed with the Sinkhorn divergence.

no code implementations • NeurIPS 2020 • Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani

In this paper, we consider the problem of computing the barycenter of a set of probability distributions under the Sinkhorn divergence.

1 code implementation • NeurIPS 2020 • Arman Adibi, Aryan Mokhtari, Hamed Hassani

Motivated by this terminology, we propose a novel meta-learning framework in the discrete domain where each task is equivalent to maximizing a set function under a cardinality constraint.

no code implementations • 23 Jun 2020 • Mohammad Fereydounian, Zebang Shen, Aryan Mokhtari, Amin Karbasi, Hamed Hassani

More precisely, by assuming that Reliable-FW has access to a (stochastic) gradient oracle of the objective function and a noisy feasibility oracle of the safety polytope, it finds an $\epsilon$-approximate first-order stationary point with the optimal ${\mathcal{O}}({1}/{\epsilon^2})$ gradient oracle complexity (resp.

1 code implementation • 17 Jun 2020 • Heejin Jeong, Hamed Hassani, Manfred Morari, Daniel D. Lee, George J. Pappas

In particular, we introduce Active Tracking Target Network (ATTN), a unified RL policy that is capable of solving major sub-tasks of active target tracking -- in-sight tracking, navigation, and exploration.

no code implementations • 9 Jun 2020 • Edgar Dobriban, Hamed Hassani, David Hong, Alexander Robey

It is well known that machine learning methods can be vulnerable to adversarially-chosen perturbations of their inputs.

1 code implementation • 20 May 2020 • Alexander Robey, Hamed Hassani, George J. Pappas

Indeed, natural variation such as lighting or weather conditions can significantly degrade the accuracy of trained neural networks, proving that such natural variation presents a significant challenge for deep learning.

no code implementations • 24 Feb 2020 • Adel Javanmard, Mahdi Soltanolkotabi, Hamed Hassani

Furthermore, we precisely characterize the standard/robust accuracy and the corresponding tradeoff achieved by a contemporary mini-max adversarial training approach in a high-dimensional regime where the number of data points and the parameters of the model grow in proportion to each other.

no code implementations • ICML 2020 • Hossein Taheri, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani

We consider a decentralized stochastic learning problem where data points are distributed among computing nodes communicating over a directed graph.

no code implementations • NeurIPS 2019 • Amin Karbasi, Hamed Hassani, Aryan Mokhtari, Zebang Shen

Concretely, for a monotone and continuous DR-submodular function, \SCGPP achieves a tight $[(1-1/e)\OPT -\epsilon]$ solution while using $O(1/\epsilon^2)$ stochastic gradients and $O(1/\epsilon)$ calls to the linear optimization oracle.

no code implementations • NeurIPS 2019 • Mingrui Zhang, Lin Chen, Hamed Hassani, Amin Karbasi

In this paper, we propose three online algorithms for submodular maximisation.

2 code implementations • 23 Oct 2019 • Heejin Jeong, Brent Schlotfeldt, Hamed Hassani, Manfred Morari, Daniel D. Lee, George J. Pappas

In this paper, we propose a novel Reinforcement Learning approach for solving the Active Information Acquisition problem, which requires an agent to choose a sequence of actions in order to acquire information about a process of interest using on-board sensors.

no code implementations • 10 Oct 2019 • Mingrui Zhang, Zebang Shen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi

One of the beauties of the projected gradient descent method lies in its rather simple mechanism and yet stable behavior with inexact, stochastic gradients, which has led to its wide-spread use in many machine learning applications.

no code implementations • 30 Sep 2019 • Alexander Robey, Arman Adibi, Brent Schlotfeldt, George J. Pappas, Hamed Hassani

Given this distributed setting, we develop Constraint-Distributed Continuous Greedy (CDCG), a message passing algorithm that converges to the tight $(1-1/e)$ approximation factor of the optimum global solution using only local computation and communication.

no code implementations • 28 Sep 2019 • Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, Ramtin Pedarsani

Federated learning is a distributed framework according to which a model is trained over a set of devices, while keeping data localized.

1 code implementation • NeurIPS 2019 • Amirhossein Reisizadeh, Hossein Taheri, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani

We consider a decentralized learning problem, where a set of computing nodes aim at solving a non-convex optimization problem collaboratively.

1 code implementation • NeurIPS 2019 • Mahyar Fazlyab, Alexander Robey, Hamed Hassani, Manfred Morari, George J. Pappas

The resulting SDP can be adapted to increase either the estimation accuracy (by capturing the interaction between activation functions of different layers) or scalability (by decomposition and parallel implementation).

no code implementations • 19 Feb 2019 • Hamed Hassani, Amin Karbasi, Aryan Mokhtari, Zebang Shen

It is known that this rate is optimal in terms of stochastic gradient evaluations.

no code implementations • 17 Feb 2019 • Mingrui Zhang, Lin Chen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi

How can we efficiently mitigate the overhead of gradient communications in distributed optimization?

no code implementations • 28 Jan 2019 • Lin Chen, Mingrui Zhang, Hamed Hassani, Amin Karbasi

In this paper, we consider the problem of black box continuous submodular maximization where we only have access to the function values and no information about the derivatives is provided.

1 code implementation • ICLR 2019 • Yogesh Balaji, Hamed Hassani, Rama Chellappa, Soheil Feizi

Building on the success of deep learning, two modern approaches to learn a probability model from the data are Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs).

no code implementations • 4 Jul 2018 • Alkis Gotovos, Hamed Hassani, Andreas Krause, Stefanie Jegelka

We consider the problem of inference in discrete probabilistic models, that is, distributions over subsets of a finite ground set.

no code implementations • 29 Jun 2018 • Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani

We consider the problem of decentralized consensus optimization, where the sum of $n$ smooth and strongly convex functions are minimized over $n$ distributed agents that form a connected network.

no code implementations • 24 Apr 2018 • Aryan Mokhtari, Hamed Hassani, Amin Karbasi

Further, for a monotone and continuous DR-submodular function and subject to a general convex body constraint, we prove that our proposed method achieves a $((1-1/e)OPT-\eps)$ guarantee with $O(1/\eps^3)$ stochastic gradient computations.

no code implementations • ICML 2018 • Lin Chen, Christopher Harshaw, Hamed Hassani, Amin Karbasi

We also propose One-Shot Frank-Wolfe, a simpler algorithm which requires only a single stochastic gradient estimate in each round and achieves an $O(T^{2/3})$ stochastic regret bound for convex and continuous submodular optimization.

no code implementations • 16 Feb 2018 • Lin Chen, Hamed Hassani, Amin Karbasi

For such settings, we then propose an online stochastic gradient ascent algorithm that also achieves a regret bound of $O(\sqrt{T})$ regret, albeit against a weaker $1/2$-approximation to the best feasible solution in hindsight.

no code implementations • NeurIPS 2017 • Mohammad Reza Karimi, Mario Lucic, Hamed Hassani, Andreas Krause

By exploiting that common extensions act linearly on the class of submodular functions, we employ projected stochastic gradient ascent and its variants in the continuous domain, and perform rounding to obtain discrete solutions.

no code implementations • 5 Nov 2017 • Aryan Mokhtari, Hamed Hassani, Amin Karbasi

More precisely, for a monotone and continuous DR-submodular function and subject to a \textit{general} convex body constraint, we prove that \alg achieves a $[(1-1/e)\text{OPT} -\eps]$ guarantee (in expectation) with $\mathcal{O}{(1/\eps^3)}$ stochastic gradient computations.

no code implementations • NeurIPS 2017 • Hamed Hassani, Mahdi Soltanolkotabi, Amin Karbasi

Despite the apparent lack of convexity in such functions, we prove that stochastic projected gradient methods can provide strong approximation guarantees for maximizing continuous submodular functions with convex constraints.

no code implementations • 13 Jun 2017 • Hadi Daneshmand, Hamed Hassani, Thomas Hofmann

Gradient descent and coordinate descent are well understood in terms of their asymptotic behavior, but less so in a transient regime often used for approximations in machine learning.

no code implementations • 16 Feb 2017 • Adish Singla, Hamed Hassani, Andreas Krause

In our setting, the feedback at any time $t$ is limited in a sense that it is only available to the expert $i^t$ that has been selected by the central algorithm (forecaster), \emph{i. e.}, only the expert $i^t$ receives feedback from the environment and gets to learn at time $t$.

no code implementations • NeurIPS 2016 • Olivier Bachem, Mario Lucic, Hamed Hassani, Andreas Krause

Seeding - the task of finding initial cluster centers - is critical in obtaining high-quality clusterings for k-Means.

no code implementations • 11 Mar 2016 • Lin Chen, Hamed Hassani, Amin Karbasi

This problem has recently gained a lot of interest in automated science and adversarial reverse engineering for which only heuristic algorithms are known.

no code implementations • NeurIPS 2015 • Alkis Gotovos, Hamed Hassani, Andreas Krause

Submodular and supermodular functions have found wide applicability in machine learning, capturing notions such as diversity and regularity, respectively.

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