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no code implementations • 27 Jul 2023 • Zhihan Xiong, Romain Camilleri, Maryam Fazel, Lalit Jain, Kevin Jamieson

For robust identification, it is well-known that if arms are chosen randomly and non-adaptively from a G-optimal design over $\mathcal{X}$ at each time then the error probability decreases as $\exp(-T\Delta^2_{(1)}/d)$, where $\Delta_{(1)} = \min_{x \neq x^*} (x^* - x)^\top \frac{1}{T}\sum_{t=1}^T \theta_t$.

no code implementations • 12 Jun 2023 • Haozhe Jiang, Qiwen Cui, Zhihan Xiong, Maryam Fazel, Simon S. Du

Specifically, we focus on games with bandit feedback, where testing an equilibrium can result in substantial regret even when the gap to be tested is small, and the existence of multiple optimal solutions (equilibria) in stationary games poses extra challenges.

no code implementations • 24 May 2023 • Omid Sadeghi, Maryam Fazel

Our goal is to design algorithms that satisfy the following two requirements: 1) $\textit{Incentive-compatible}$: Incentivize the experts to report their beliefs truthfully, and 2) $\textit{No-regret}$: Achieve sublinear regret with respect to the true beliefs of the best fixed set of $m$ experts in hindsight.

no code implementations • 2 Feb 2023 • Yuzhen Qin, Yingcong Li, Fabio Pasqualetti, Maryam Fazel, Samet Oymak

The growing interest in complex decision-making and language modeling problems highlights the importance of sample-efficient learning over very long horizons.

no code implementations • 24 Oct 2022 • Haozhe Jiang, Qiwen Cui, Zhihan Xiong, Maryam Fazel, Simon S. Du

Starting from the facility-level (a. k. a., semi-bandit) feedback, we propose a novel one-unit deviation coverage condition and give a pessimism-type algorithm that can recover an approximate NE.

no code implementations • 10 Oct 2022 • Bin Hu, Kaiqing Zhang, Na Li, Mehran Mesbahi, Maryam Fazel, Tamer Başar

Gradient-based methods have been widely used for system design and optimization in diverse application domains.

1 code implementation • 13 Jul 2022 • Vincent Roulet, Siddhartha Srinivasa, Maryam Fazel, Zaid Harchaoui

We present the implementation of nonlinear control algorithms based on linear and quadratic approximations of the objective from a functional viewpoint.

no code implementations • 7 Jul 2022 • Adhyyan Narang, Omid Sadeghi, Lillian J Ratliff, Maryam Fazel, Jeff Bilmes

At round $i$, a user with unknown utility $h_q$ arrives; the optimizer selects a new item to add to $S_q$, and receives a noisy marginal gain.

1 code implementation • 6 Jun 2022 • Sarah Dean, Mihaela Curmei, Lillian J. Ratliff, Jamie Morgenstern, Maryam Fazel

We study the participation and retraining dynamics that arise when both the learners and sub-populations of users are \emph{risk-reducing}, which cover a broad class of updates including gradient descent, multiplicative weights, etc.

no code implementations • 4 Jun 2022 • Qiwen Cui, Zhihan Xiong, Maryam Fazel, Simon S. Du

We propose a centralized algorithm for Markov congestion games, whose sample complexity again has only polynomial dependence on all relevant problem parameters, but not the size of the action set.

no code implementations • 8 Apr 2022 • Mitas Ray, Dmitriy Drusvyatskiy, Maryam Fazel, Lillian J. Ratliff

This paper studies the problem of expected loss minimization given a data distribution that is dependent on the decision-maker's action and evolves dynamically in time according to a geometric decay process.

1 code implementation • 30 Mar 2022 • Yue Sun, Samet Oymak, Maryam Fazel

Hankel regularization encourages the low-rankness of the Hankel matrix, which maps to the low-orderness of the system.

no code implementations • 7 Mar 2022 • Lijun Ding, Dmitriy Drusvyatskiy, Maryam Fazel, Zaid Harchaoui

Empirical evidence suggests that for a variety of overparameterized nonlinear models, most notably in neural network training, the growth of the loss around a minimizer strongly impacts its performance.

1 code implementation • NeurIPS 2021 • Yue Sun, Adhyyan Narang, Halil Ibrahim Gulluk, Samet Oymak, Maryam Fazel

Specifically, for (1), we first show that learning the optimal representation coincides with the problem of designing a task-aware regularization to promote inductive bias.

no code implementations • 10 Jan 2022 • Adhyyan Narang, Evan Faulkner, Dmitriy Drusvyatskiy, Maryam Fazel, Lillian J. Ratliff

We show that under mild assumptions, the performatively stable equilibria can be found efficiently by a variety of algorithms, including repeated retraining and the repeated (stochastic) gradient method.

no code implementations • 15 Nov 2021 • Omid Sadeghi, Maryam Fazel

Then, we study $L$-smooth monotone strongly DR-submodular functions that have bounded curvature, and we show how to exploit such additional structure to obtain algorithms with improved approximation guarantees and faster convergence rates for the maximization problem.

no code implementations • NeurIPS 2021 • Romain Camilleri, Zhihan Xiong, Maryam Fazel, Lalit Jain, Kevin Jamieson

The main results of this work precisely characterize this trade-off between labeled samples and stopping time and provide an algorithm that nearly-optimally achieves the minimal label complexity given a desired stopping time.

no code implementations • 15 Jun 2021 • Omid Sadeghi, Prasanna Raut, Maryam Fazel

For $(1)$, we obtain the first logarithmic regret bounds.

no code implementations • 19 Feb 2021 • Zhihan Xiong, Ruoqi Shen, Qiwen Cui, Maryam Fazel, Simon S. Du

To achieve the desired result, we develop 1) a new clipping operation to ensure both the probability of being optimistic and the probability of being pessimistic are lower bounded by a constant, and 2) a new recursive formula for the absolute value of estimation errors to analyze the regret.

no code implementations • 14 Feb 2021 • Halil Ibrahim Gulluk, Yue Sun, Samet Oymak, Maryam Fazel

We prove that subspace-based representations can be learned in a sample-efficient manner and provably benefit future tasks in terms of sample complexity.

no code implementations • 23 Dec 2020 • Mitas Ray, Omid Sadeghi, Lillian J. Ratliff, Maryam Fazel

We study the problem of online resource allocation, where multiple customers arrive sequentially and the seller must irrevocably allocate resources to each incoming customer while also facing a procurement cost for the total allocation.

no code implementations • NeurIPS 2020 • Omid Sadeghi, Prasanna Raut, Maryam Fazel

In this paper, we consider an online optimization problem in which the reward functions are DR-submodular, and in addition to maximizing the total reward, the sequence of decisions must satisfy some convex constraints on average.

no code implementations • L4DC 2020 • Yue Sun, Samet Oymak, Maryam Fazel

This paper studies low-order linear system identification via regularized regression.

no code implementations • 29 May 2020 • Prasanna Sanjay Raut, Omid Sadeghi, Maryam Fazel

Stochastic long-term constraints arise naturally in applications where there is a limited budget or resource available and resource consumption at each step is governed by stochastically time-varying environments.

no code implementations • 30 Jun 2019 • Omid Sadeghi, Reza Eghbali, Maryam Fazel

In this paper, we study a certain class of online optimization problems, where the goal is to maximize a function that is not necessarily concave and satisfies the Diminishing Returns (DR) property under budget constraints.

no code implementations • 30 Jun 2019 • Omid Sadeghi, Maryam Fazel

In this paper, we study a class of online optimization problems with long-term budget constraints where the objective functions are not necessarily concave (nor convex) but they instead satisfy the Diminishing Returns (DR) property.

no code implementations • NeurIPS 2019 • Yue Sun, Nicolas Flammarion, Maryam Fazel

We consider minimizing a nonconvex, smooth function $f$ on a Riemannian manifold $\mathcal{M}$.

no code implementations • 10 Apr 2019 • Amin Jalali, Adel Javanmard, Maryam Fazel

Prior knowledge on properties of a target model often come as discrete or combinatorial descriptions.

no code implementations • ICML 2018 • Maryam Fazel, Rong Ge, Sham M. Kakade, Mehran Mesbahi

Direct policy gradient methods for reinforcement learning and continuous control problems are a popular approach for a variety of reasons: 1) they are easy to implement without explicit knowledge of the underlying model 2) they are an "end-to-end" approach, directly optimizing the performance metric of interest 3) they inherently allow for richly parameterized policies.

no code implementations • ICLR 2018 • Maryam Fazel, Rong Ge, Sham M. Kakade, Mehran Mesbahi

Direct policy gradient methods for reinforcement learning and continuous control problems are a popular approach for a variety of reasons: 1) they are easy to implement without explicit knowledge of the underlying model; 2) they are an "end-to-end" approach, directly optimizing the performance metric of interest; 3) they inherently allow for richly parameterized policies.

no code implementations • NeurIPS 2016 • Reza Eghbali, Maryam Fazel

Online optimization covers problems such as online resource allocation, online bipartite matching, adwords (a central problem in e-commerce and advertising), and adwords with separable concave returns.

no code implementations • NeurIPS 2016 • Amin Jalali, Qiyang Han, Ioana Dumitriu, Maryam Fazel

The Stochastic Block Model (SBM) is a widely used random graph model for networks with communities.

no code implementations • 15 Dec 2015 • Amin Jalali, Qiyang Han, Ioana Dumitriu, Maryam Fazel

For instance, $\log n$ is considered to be the standard lower bound on the cluster size for exact recovery via convex methods, for homogenous SBM.

no code implementations • 16 Jul 2015 • Amin Jalali, Maryam Fazel, Lin Xiao

We propose a new class of convex penalty functions, called \emph{variational Gram functions} (VGFs), that can promote pairwise relations, such as orthogonality, among a set of vectors in a vector space.

no code implementations • 27 Oct 2014 • Reza Eghbali, Jon Swenson, Maryam Fazel

Online optimization problems arise in many resource allocation tasks, where the future demands for each resource and the associated utility functions change over time and are not known apriori, yet resources need to be allocated at every point in time despite the future uncertainty.

no code implementations • 3 Jun 2014 • Krishnamurthy Dvijotham, Maryam Fazel, Emanuel Todorov

We develop a framework for convexifying a fairly general class of optimization problems.

no code implementations • 28 Feb 2014 • Kean Ming Tan, Palma London, Karthik Mohan, Su-In Lee, Maryam Fazel, Daniela Witten

We consider the problem of learning a high-dimensional graphical model in which certain hub nodes are highly-connected to many other nodes.

no code implementations • 21 Mar 2013 • Karthik Mohan, Palma London, Maryam Fazel, Daniela Witten, Su-In Lee

We consider estimation under two distinct assumptions: (1) differences between the K networks are due to individual nodes that are perturbed across conditions, or (2) similarities among the K networks are due to the presence of common hub nodes that are shared across all K networks.

no code implementations • NeurIPS 2012 • Karthik Mohan, Mike Chung, Seungyeop Han, Daniela Witten, Su-In Lee, Maryam Fazel

We consider estimation of multiple high-dimensional Gaussian graphical models corresponding to a single set of nodes under several distinct conditions.

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