no code implementations • 27 Jun 2024 • Shayan Kiyani, George Pappas, Hamed Hassani

Conditional validity and length efficiency are two crucial aspects of conformal prediction (CP).

no code implementations • 16 Jun 2024 • Behrad Moniri, Hamed Hassani, Edgar Dobriban

Large Language Models (LLMs) are rapidly evolving and impacting various fields, necessitating the development of effective methods to evaluate and compare their performance.

no code implementations • 12 Jun 2024 • Patrick Chao, Edgar Dobriban, Hamed Hassani

Recent progress in large language models enables the creation of realistic machine-generated content.

no code implementations • 4 Jun 2024 • Mahdi Sabbaghi, George Pappas, Hamed Hassani, Surbhi Goel

Empirically, our method allows a Transformer trained on numbers with at most 5-digits for addition and multiplication to generalize up to 50-digit numbers, without using additional data for longer sequences.

no code implementations • 29 May 2024 • Xinmeng Huang, Shuo Li, Edgar Dobriban, Osbert Bastani, Hamed Hassani, Dongsheng Ding

The growing safety concerns surrounding Large Language Models (LLMs) raise an urgent need to align them with diverse human preferences to simultaneously enhance their helpfulness and safety.

no code implementations • 28 May 2024 • Behrad Moniri, Hamed Hassani

In this paper, we study a nonlinear spiked random matrix model where a nonlinear function is applied element-wise to a noise matrix perturbed by a rank-one signal.

no code implementations • 26 Apr 2024 • Shayan Kiyani, George Pappas, Hamed Hassani

In this paper, we focus on the problem of conformal prediction with conditional guarantees.

no code implementations • 4 Apr 2024 • Xinmeng Huang, Shuo Li, Mengxin Yu, Matteo Sesia, Hamed Hassani, Insup Lee, Osbert Bastani, Edgar Dobriban

Language Models (LMs) have shown promising performance in natural language generation.

no code implementations • 28 Mar 2024 • Yutong He, Alexander Robey, Naoki Murata, Yiding Jiang, Joshua Williams, George J. Pappas, Hamed Hassani, Yuki Mitsufuji, Ruslan Salakhutdinov, J. Zico Kolter

Prompt engineering is effective for controlling the output of text-to-image (T2I) generative models, but it is also laborious due to the need for manually crafted prompts.

2 code implementations • 28 Mar 2024 • Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J. Pappas, Florian Tramer, Hamed Hassani, Eric Wong

To address these challenges, we introduce JailbreakBench, an open-sourced benchmark with the following components: (1) an evolving repository of state-of-the-art adversarial prompts, which we refer to as jailbreak artifacts; (2) a jailbreaking dataset comprising 100 behaviors -- both original and sourced from prior work -- which align with OpenAI's usage policies; (3) a standardized evaluation framework at https://github. com/JailbreakBench/jailbreakbench that includes a clearly defined threat model, system prompts, chat templates, and scoring functions; and (4) a leaderboard at https://jailbreakbench. github. io/ that tracks the performance of attacks and defenses for various LLMs.

no code implementations • 12 Mar 2024 • Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti

On general vector sources, LTC improves upon standard neural compressors in one-shot coding performance.

1 code implementation • 25 Feb 2024 • Jiabao Ji, Bairu Hou, Alexander Robey, George J. Pappas, Hamed Hassani, Yang Zhang, Eric Wong, Shiyu Chang

Aligned large language models (LLMs) are vulnerable to jailbreaking attacks, which bypass the safeguards of targeted LLMs and fool them into generating objectionable content.

no code implementations • 19 Feb 2024 • Arman Adibi, Nicolo Dal Fabbro, Luca Schenato, Sanjeev Kulkarni, H. Vincent Poor, George J. Pappas, Hamed Hassani, Aritra Mitra

Motivated by applications in large-scale and multi-agent reinforcement learning, we study the non-asymptotic performance of stochastic approximation (SA) schemes with delayed updates under Markovian sampling.

no code implementations • 7 Feb 2024 • Kevin Kögler, Alexander Shevchenko, Hamed Hassani, Marco Mondelli

For the prototypical case of the 1-bit compression of sparse Gaussian data, we prove that gradient descent converges to a solution that completely disregards the sparse structure of the input.

no code implementations • 5 Feb 2024 • Payam Delgosha, Hamed Hassani, Ramtin Pedarsani

In this paper, we focus on the $\ell_0$-bounded adversarial attacks, and aim to theoretically characterize the performance of adversarial training for an important class of truncated classifiers.

no code implementations • 15 Oct 2023 • Eric Lei, Arman Adibi, Hamed Hassani

One class of these problems involve objective functions which depend on neural networks, but optimization variables which are discrete.

1 code implementation • 12 Oct 2023 • Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, Eric Wong

PAIR -- which is inspired by social engineering attacks -- uses an attacker LLM to automatically generate jailbreaks for a separate targeted LLM without human intervention.

no code implementations • 11 Oct 2023 • Behrad Moniri, Donghwan Lee, Hamed Hassani, Edgar Dobriban

It is rigorously known that in two-layer fully-connected neural networks under certain conditions, one step of gradient descent on the first layer can lead to feature learning; characterized by the appearance of a separated rank-one component -- spike -- in the spectrum of the feature matrix.

1 code implementation • 5 Oct 2023 • Alexander Robey, Eric Wong, Hamed Hassani, George J. Pappas

Despite efforts to align large language models (LLMs) with human intentions, widely-used LLMs such as GPT, Llama, and Claude are susceptible to jailbreaking attacks, wherein an adversary fools a targeted LLM into generating objectionable content.

1 code implementation • 11 Sep 2023 • Zebang Shen, Jiayuan Ye, Anmin Kang, Hamed Hassani, Reza Shokri

Repeated parameter sharing in federated learning causes significant information leakage about private data, thus defeating its main purpose: data privacy.

no code implementations • 13 Jul 2023 • Arman Adibi, Aritra Mitra, Hamed Hassani

Motivated by this gap, we examine the performance of standard min-max optimization algorithms with delayed gradient updates.

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

An increasingly popular machine learning paradigm is to pretrain a neural network (NN) on many tasks offline, then adapt it to downstream tasks, often by re-training only the last linear layer of the network.

1 code implementation • 4 Jul 2023 • Eric Lei, Yiğit Berkay Uslu, Hamed Hassani, Shirin Saeedi Bidokhti

Recent advances in text-to-image generative models provide the ability to generate high-quality images from short text descriptions.

no code implementations • 1 Jul 2023 • Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti

We discuss a relationship between rate-distortion and optimal transport (OT) theory, even though they seem to be unrelated at first glance.

no code implementations • 19 Jun 2023 • Alexander Robey, Fabian Latorre, George J. Pappas, Hamed Hassani, Volkan Cevher

One prominent approach toward resolving the adversarial vulnerability of deep neural networks is the two-player zero-sum paradigm of adversarial training, in which predictors are trained against adversarially chosen perturbations of data.

no code implementations • 9 Jun 2023 • Xinmeng Huang, Kan Xu, Donghwan Lee, Hamed Hassani, Hamsa Bastani, Edgar Dobriban

MOLAR improves the dependence of the estimation error on the data dimension, compared to independent least squares estimates.

no code implementations • 25 May 2023 • Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti

We discuss a federated learned compression problem, where the goal is to learn a compressor from real-world data which is scattered across clients and may be statistically heterogeneous, yet share a common underlying representation.

no code implementations • 25 May 2023 • Bruce D. Lee, Thomas T. C. K. Zhang, Hamed Hassani, Nikolai Matni

In these special cases, we demonstrate that the severity of the tradeoff depends in an interpretable manner upon system-theoretic properties such as the spectrum of the controllability gramian, the spectrum of the observability gramian, and the stability of the system.

no code implementations • 4 Feb 2023 • Han Wang, Aritra Mitra, Hamed Hassani, George J. Pappas, James Anderson

We initiate the study of federated reinforcement learning under environmental heterogeneity by considering a policy evaluation problem.

1 code implementation • 31 Jan 2023 • Donghwan Lee, Behrad Moniri, Xinmeng Huang, Edgar Dobriban, Hamed Hassani

Evaluating the performance of machine learning models under distribution shift is challenging, especially when we only have unlabeled data from the shifted (target) domain, along with labeled data from the original (source) domain.

no code implementations • 3 Jan 2023 • Aritra Mitra, George J. Pappas, Hamed Hassani

These works have collectively revealed that stochastic gradient descent (SGD) is robust to structured perturbations such as quantization, sparsification, and delays.

no code implementations • 27 Dec 2022 • Alexander Shevchenko, Kevin Kögler, Hamed Hassani, Marco Mondelli

Autoencoders are a popular model in many branches of machine learning and lossy data compression.

2 code implementations • 20 Jul 2022 • Cian Eastwood, Alexander Robey, Shashank Singh, Julius von Kügelgen, Hamed Hassani, George J. Pappas, Bernhard Schölkopf

By minimizing the $\alpha$-quantile of predictor's risk distribution over domains, QRM seeks predictors that perform well with probability $\alpha$.

1 code implementation • 8 Jun 2022 • Haoze Wu, Teruhiro Tagomori, Alexander Robey, Fengjun Yang, Nikolai Matni, George Pappas, Hamed Hassani, Corina Pasareanu, Clark Barrett

We consider the problem of certifying the robustness of deep neural networks against real-world distribution shifts.

no code implementations • 6 Jun 2022 • Aritra Mitra, Arman Adibi, George J. Pappas, Hamed Hassani

We consider a linear stochastic bandit problem involving $M$ agents that can collaborate via a central server to minimize regret.

1 code implementation • 5 Jun 2022 • Isidoros Tziotis, Zebang Shen, Ramtin Pedarsani, Hamed Hassani, Aryan Mokhtari

Federated Learning is an emerging learning paradigm that allows training models from samples distributed across a large network of clients while respecting privacy and communication restrictions.

1 code implementation • 2 Jun 2022 • Zebang Shen, Zhenfu Wang, Satyen Kale, Alejandro Ribeiro, Amin Karbasi, Hamed Hassani

In this paper, we exploit this concept to design a potential function of the hypothesis velocity fields, and prove that, if such a function diminishes to zero during the training procedure, the trajectory of the densities generated by the hypothesis velocity fields converges to the solution of the FPE in the Wasserstein-2 sense.

no code implementations • 1 Jun 2022 • Xinmeng Huang, Donghwan Lee, Edgar Dobriban, Hamed Hassani

In modern machine learning, users often have to collaborate to learn the distribution of the data.

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 • 7 Apr 2022 • Arman Adibi, Aritra Mitra, George J. Pappas, Hamed Hassani

Recent years have witnessed a growing interest in the topic of min-max optimization, owing to its relevance in the context of generative adversarial networks (GANs), robust control and optimization, and reinforcement learning.

1 code implementation • 4 Apr 2022 • Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti

Motivated by the empirical success of deep neural network (DNN) compressors on large, real-world data, we investigate methods to estimate the rate-distortion function on such data, which would allow comparison of DNN compressors with optimality.

1 code implementation • 2 Apr 2022 • Anton Xue, Lars Lindemann, Alexander Robey, Hamed Hassani, George J. Pappas, Rajeev Alur

Lipschitz constants of neural networks allow for guarantees of robustness in image classification, safety in controller design, and generalizability beyond the training data.

no code implementations • 21 Mar 2022 • Bruce D. Lee, Thomas T. C. K. Zhang, Hamed Hassani, Nikolai Matni

Though this fundamental tradeoff between nominal performance and robustness is known to exist, it is not well-characterized in quantitative terms.

1 code implementation • ICLR 2022 • Allan Zhou, Fahim Tajwar, Alexander Robey, Tom Knowles, George J. Pappas, Hamed Hassani, Chelsea Finn

Based on this analysis, we show how a generative approach for learning the nuisance transformations can help transfer invariances across classes and improve performance on a set of imbalanced image classification benchmarks.

Ranked #22 on Long-tail Learning on CIFAR-10-LT (ρ=100)

no code implementations • 9 Mar 2022 • Payam Delgosha, Hamed Hassani, Ramtin Pedarsani

We introduce a classification method which employs a nonlinear component called truncation, and show in an asymptotic scenario, as long as the adversary is restricted to perturb no more than $\sqrt{d}$ data samples, we can almost achieve the optimal classification error in the absence of the adversary, i. e. we can completely neutralize adversary's effect.

1 code implementation • 3 Mar 2022 • Donghwan Lee, Xinmeng Huang, Hamed Hassani, Edgar Dobriban

We find that detecting mis-calibration is only possible when the conditional probabilities of the classes are sufficiently smooth functions of the predictions.

no code implementations • 2 Mar 2022 • Aritra Mitra, Hamed Hassani, George J. Pappas

Specifically, in our setup, an agent interacting with an environment transmits encoded estimates of an unknown model parameter to a server over a communication channel of finite capacity.

no code implementations • 17 Feb 2022 • Mohammad Fereydounian, Hamed Hassani, Amin Karbasi

We prove that: (i) a GNN, as a graph function, is necessarily permutation compatible; (ii) conversely, any permutation compatible function, when restricted on input graphs with distinct node features, can be generated by a GNN; (iii) for arbitrary node features (not necessarily distinct), a simple feature augmentation scheme suffices to generate a permutation compatible function by a GNN; (iv) permutation compatibility can be verified by checking only quadratically many functional constraints, rather than an exhaustive search over all the permutations; (v) GNNs can generate \textit{any} graph function once we augment the node features with node identities, thus going beyond graph isomorphism and permutation compatibility.

1 code implementation • 2 Feb 2022 • Alexander Robey, Luiz F. O. Chamon, George J. Pappas, Hamed Hassani

From a theoretical point of view, this framework overcomes the trade-offs between the performance and the sample-complexity of worst-case and average-case learning.

no code implementations • 23 Jan 2022 • Mark Beliaev, Payam Delgosha, Hamed Hassani, Ramtin Pedarsani

In the past two decades we have seen the popularity of neural networks increase in conjunction with their classification accuracy.

no code implementations • 13 Jan 2022 • Hamed Hassani, Adel Javanmard

Our developed theory reveals the nontrivial effect of overparametrization on robustness and indicates that for adversarially trained random features models, high overparametrization can hurt robust generalization.

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

We provide an algorithm to find this perturbation given data realizations, and develop upper and lower bounds on the adversarial state estimation error in terms of the standard (non-adversarial) estimation error and the spectral properties of the resulting observer.

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 • ICLR 2022 • Zebang Shen, Juan Cervino, Hamed Hassani, Alejandro Ribeiro

Federated Learning (FL) has emerged as the tool of choice for training deep models over heterogeneous and decentralized datasets.

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.

3 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 • 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 • 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 • 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 • 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 $t$.

1 code implementation • 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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