no code implementations • ICML 2020 • Shubhanshu Shekhar, Tara Javidi, Mohammad Ghavamzadeh

We consider the problem of allocating a fixed budget of samples to a finite set of discrete distributions to learn them uniformly well (minimizing the maximum error) in terms of four common distance measures: $\ell_2^2$, $\ell_1$, $f$-divergence, and separation distance.

1 code implementation • 2 Apr 2024 • KyuYoung Kim, Jongheon Jeong, Minyong An, Mohammad Ghavamzadeh, Krishnamurthy Dvijotham, Jinwoo Shin, Kimin Lee

To investigate this issue in depth, we introduce the Text-Image Alignment Assessment (TIA2) benchmark, which comprises a diverse collection of text prompts, images, and human annotations.

no code implementations • 15 Jan 2024 • Aldo Pacchiano, Mohammad Ghavamzadeh, Peter Bartlett

In the setting that the constraint is in expectation, we further specialize our results to multi-armed bandits and propose a computationally efficient algorithm for this setting with regret analysis.

no code implementations • 29 Nov 2023 • Amin Rakhsha, Mete Kemertas, Mohammad Ghavamzadeh, Amir-Massoud Farahmand

We propose and theoretically analyze an approach for planning with an approximate model in reinforcement learning that can reduce the adverse impact of model error.

1 code implementation • 27 Oct 2023 • Kishan Panaganti, Zaiyan Xu, Dileep Kalathil, Mohammad Ghavamzadeh

The goal of an offline reinforcement learning (RL) algorithm is to learn optimal polices using historical (offline) data, without access to the environment for online exploration.

no code implementations • 22 Oct 2023 • Erdem Biyik, Fan Yao, Yinlam Chow, Alex Haig, Chih-Wei Hsu, Mohammad Ghavamzadeh, Craig Boutilier

Leveraging concept activation vectors for soft attribute semantics, we develop novel preference elicitation methods that can accommodate soft attributes and bring together both item and attribute-based preference elicitation.

no code implementations • 9 Oct 2023 • Jihwan Jeong, Yinlam Chow, Guy Tennenholtz, Chih-Wei Hsu, Azamat Tulepbergenov, Mohammad Ghavamzadeh, Craig Boutilier

Recommender systems (RSs) play a central role in connecting users to content, products, and services, matching candidate items to users based on their preferences.

no code implementations • 2 Jun 2023 • Mohammad Ghavamzadeh, Marek Petrik, Guy Tennenholtz

We study how to make decisions that minimize Bayesian regret in offline linear bandits.

2 code implementations • 25 May 2023 • Ying Fan, Olivia Watkins, Yuqing Du, Hao liu, MoonKyung Ryu, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, Kangwook Lee, Kimin Lee

We focus on diffusion models, defining the fine-tuning task as an RL problem, and updating the pre-trained text-to-image diffusion models using policy gradient to maximize the feedback-trained reward.

no code implementations • 12 May 2023 • Gecia Bravo-Hermsdorff, Róbert Busa-Fekete, Mohammad Ghavamzadeh, Andres Muñoz Medina, Umar Syed

For a joint distribution over many variables whose conditional independence is given by a tree, we describe algorithms for estimating Shannon entropy that require a number of samples that is linear in the number of variables, compared to the quadratic sample complexity of prior work.

no code implementations • NeurIPS 2023 • Jia Lin Hau, Erick Delage, Mohammad Ghavamzadeh, Marek Petrik

However, we show that these popular decompositions for Conditional-Value-at-Risk (CVaR) and Entropic-Value-at-Risk (EVaR) are inherently suboptimal regardless of the discretization level.

no code implementations • 22 Apr 2023 • Moloud Abdar, Meenakshi Kollati, Swaraja Kuraparthi, Farhad Pourpanah, Daniel McDuff, Mohammad Ghavamzadeh, Shuicheng Yan, Abduallah Mohamed, Abbas Khosravi, Erik Cambria, Fatih Porikli

Video captioning (VC) is a fast-moving, cross-disciplinary area of research that bridges work in the fields of computer vision, natural language processing (NLP), linguistics, and human-computer interaction.

no code implementations • 23 Feb 2023 • Kimin Lee, Hao liu, MoonKyung Ryu, Olivia Watkins, Yuqing Du, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, Shixiang Shane Gu

Our results demonstrate the potential for learning from human feedback to significantly improve text-to-image models.

no code implementations • 9 Dec 2022 • Joey Hong, Branislav Kveton, Sumeet Katariya, Manzil Zaheer, Mohammad Ghavamzadeh

We prove per-task bounds on the suboptimality of the learned policies, which show a clear improvement over not using the hierarchical model.

no code implementations • 25 Nov 2022 • Amin Rakhsha, Andrew Wang, Mohammad Ghavamzadeh, Amir-Massoud Farahmand

We introduce new planning and reinforcement learning algorithms for discounted MDPs that utilize an approximate model of the environment to accelerate the convergence of the value function.

no code implementations • 9 Sep 2022 • Jia Lin Hau, Marek Petrik, Mohammad Ghavamzadeh, Reazul Russel

We propose and analyze a new framework to jointly model the risk associated with epistemic and aleatory uncertainties in finite-horizon and discounted infinite-horizon MDPs.

1 code implementation • 10 Aug 2022 • Kishan Panaganti, Zaiyan Xu, Dileep Kalathil, Mohammad Ghavamzadeh

The goal of robust reinforcement learning (RL) is to learn a policy that is robust against the uncertainty in model parameters.

no code implementations • 3rd Conversational AI Workshop at 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) 2019 • Jorge A. Mendez, Alborz Geramifard, Mohammad Ghavamzadeh, Bing Liu

Learning task-oriented dialog policies via reinforcement learning typically requires large amounts of interaction with users, which in practice renders such methods unusable for real-world applications.

no code implementations • 31 May 2022 • Yinlam Chow, Aza Tulepbergenov, Ofir Nachum, MoonKyung Ryu, Mohammad Ghavamzadeh, Craig Boutilier

Despite recent advancements in language models (LMs), their application to dialogue management (DM) problems and ability to carry on rich conversations remain a challenge.

no code implementations • 12 May 2022 • Ahmadreza Moradipari, Mohammad Ghavamzadeh, Taha Rajabzadeh, Christos Thrampoulidis, Mahnoosh Alizadeh

In this work we investigate meta-learning (or learning-to-learn) approaches in multi-task linear stochastic bandit problems that can originate from multiple environments.

no code implementations • 12 May 2022 • Ahmadreza Moradipari, Mohammad Ghavamzadeh, Mahnoosh Alizadeh

We propose a distributed upper confidence bound (UCB) algorithm and prove a high probability bound on its $T$-round regret in which we include a linear growth of regret associated with each communication round.

2 code implementations • 10 May 2022 • Ido Greenberg, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor

In risk-averse reinforcement learning (RL), the goal is to optimize some risk measure of the returns.

1 code implementation • 25 Feb 2022 • MohammadJavad Azizi, Branislav Kveton, Mohammad Ghavamzadeh, Sumeet Katariya

The Bayesian algorithm has access to a prior distribution over the meta-parameters and its meta simple regret over $m$ bandit tasks with horizon $n$ is mere $\tilde{O}(m / \sqrt{n})$.

1 code implementation • 25 Feb 2022 • MohammadJavad Azizi, Thang Duong, Yasin Abbasi-Yadkori, András György, Claire Vernade, Mohammad Ghavamzadeh

We study a sequential decision problem where the learner faces a sequence of $K$-armed bandit tasks.

2 code implementations • 6 Feb 2022 • Christina Göpfert, Alex Haig, Yinlam Chow, Chih-Wei Hsu, Ivan Vendrov, Tyler Lu, Deepak Ramachandran, Hubert Pham, Mohammad Ghavamzadeh, Craig Boutilier

Interactive recommender systems have emerged as a promising paradigm to overcome the limitations of the primitive user feedback used by traditional recommender systems (e. g., clicks, item consumption, ratings).

no code implementations • 3 Feb 2022 • Joey Hong, Branislav Kveton, Sumeet Katariya, Manzil Zaheer, Mohammad Ghavamzadeh

We use this exact posterior to analyze the Bayes regret of HierTS in Gaussian bandits.

no code implementations • 12 Nov 2021 • Joey Hong, Branislav Kveton, Manzil Zaheer, Mohammad Ghavamzadeh

We provide a unified view of all these problems, as learning to act in a hierarchical Bayesian bandit.

no code implementations • 10 Jun 2021 • Joey Hong, Branislav Kveton, Manzil Zaheer, Mohammad Ghavamzadeh, Craig Boutilier

We study Thompson sampling (TS) in online decision making, where the uncertain environment is sampled from a mixture distribution.

no code implementations • 9 Jun 2021 • Mohammad Javad Azizi, Branislav Kveton, Mohammad Ghavamzadeh

We analyze our algorithm in linear and generalized linear models (GLMs), and propose a practical implementation based on a G-optimal design.

no code implementations • 9 Jun 2021 • Ahmadreza Moradipari, Berkay Turan, Yasin Abbasi-Yadkori, Mahnoosh Alizadeh, Mohammad Ghavamzadeh

In the second setting, the reward parameter of the LB problem is arbitrarily selected from $M$ models represented as (possibly) overlapping balls in $\mathbb R^d$.

no code implementations • NeurIPS 2021 • Shubhanshu Shekhar, Greg Fields, Mohammad Ghavamzadeh, Tara Javidi

Machine learning models trained on uncurated datasets can often end up adversely affecting inputs belonging to underrepresented groups.

no code implementations • 1 Dec 2020 • Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed, Mohammad Ghavamzadeh, Craig Boutilier

The key idea is to frame this problem as a latent bandit, where the prototypical models of user behavior are learned offline and the latent state of the user is inferred online from its interactions with the models.

no code implementations • 30 Nov 2020 • Elita A. Lobo, Mohammad Ghavamzadeh, Marek Petrik

In reinforcement learning, robust policies for high-stakes decision-making problems with limited data are usually computed by optimizing the percentile criterion, which minimizes the probability of a catastrophic failure.

no code implementations • 12 Nov 2020 • Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi

Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes.

no code implementations • 14 Sep 2020 • Daoming Lyu, Qi Qi, Mohammad Ghavamzadeh, Hengshuai Yao, Tianbao Yang, Bo Liu

To achieve variance-reduced off-policy-stable policy optimization, we propose an algorithm family that is memory-efficient, stochastically variance-reduced, and capable of learning from off-policy samples.

1 code implementation • 28 Jun 2020 • Akella Ravi Tej, Kamyar Azizzadenesheli, Mohammad Ghavamzadeh, Anima Anandkumar, Yisong Yue

On the other hand, more sample efficient alternatives like Bayesian quadrature methods have received little attention due to their high computational complexity.

no code implementations • ICLR 2021 • Brandon Cui, Yin-Lam Chow, Mohammad Ghavamzadeh

We first formulate a LCE model to learn representations that are suitable to be used by a policy iteration style algorithm in the latent space.

Model-based Reinforcement Learning
reinforcement-learning
**+2**

no code implementations • 17 Jun 2020 • Aldo Pacchiano, Mohammad Ghavamzadeh, Peter Bartlett, Heinrich Jiang

We propose an upper-confidence bound algorithm for this problem, called optimistic pessimistic linear bandit (OPLB), and prove an $\widetilde{\mathcal{O}}(\frac{d\sqrt{T}}{\tau-c_0})$ bound on its $T$-round regret, where the denominator is the difference between the constraint threshold and the cost of a known feasible action.

no code implementations • 9 Jun 2020 • Yin-Lam Chow, Brandon Cui, MoonKyung Ryu, Mohammad Ghavamzadeh

Model-based reinforcement learning (RL) algorithms allow us to combine model-generated data with those collected from interaction with the real system in order to alleviate the data efficiency problem in RL.

1 code implementation • 6 Jun 2020 • Arash Mehrjou, Mohammad Ghavamzadeh, Bernhard Schölkopf

We provide theoretical results on the class of systems that can be treated with the proposed algorithm and empirically evaluate the effectiveness of our method using an exemplary dynamical system.

1 code implementation • 6 Jun 2020 • Bo Liu, Ian Gemp, Mohammad Ghavamzadeh, Ji Liu, Sridhar Mahadevan, Marek Petrik

In this paper, we introduce proximal gradient temporal difference learning, which provides a principled way of designing and analyzing true stochastic gradient temporal difference learning algorithms.

no code implementations • 6 Jun 2020 • Bo Liu, Ji Liu, Mohammad Ghavamzadeh, Sridhar Mahadevan, Marek Petrik

In this paper, we analyze the convergence rate of the gradient temporal difference learning (GTD) family of algorithms.

1 code implementation • ICLR 2022 • Manan Tomar, Lior Shani, Yonathan Efroni, Mohammad Ghavamzadeh

Overall, MDPO is derived from the MD principles, offers a unified approach to viewing a number of popular RL algorithms, and performs better than or on-par with TRPO, PPO, and SAC in a number of continuous control tasks.

no code implementations • 6 Mar 2020 • Jean Tarbouriech, Shubhanshu Shekhar, Matteo Pirotta, Mohammad Ghavamzadeh, Alessandro Lazaric

Using a number of simple domains with heterogeneous noise in their transitions, we show that our heuristic-based algorithm outperforms both our original algorithm and the maximum entropy algorithm in the small sample regime, while achieving similar asymptotic performance as that of the original algorithm.

1 code implementation • ICML 2020 • Rui Shu, Tung Nguyen, Yin-Lam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung H. Bui

High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks.

1 code implementation • 28 Feb 2020 • Romina Abachi, Mohammad Ghavamzadeh, Amir-Massoud Farahmand

This is in contrast to conventional model learning approaches, such as those based on maximum likelihood estimate, that learn a predictive model of the environment without explicitly considering the interaction of the model and the planner.

no code implementations • 8 Feb 2020 • Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta

While learning in an unknown Markov Decision Process (MDP), an agent should trade off exploration to discover new information about the MDP, and exploitation of the current knowledge to maximize the reward.

no code implementations • 8 Feb 2020 • Evrard Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, Matteo Pirotta

In this case, it is desirable to deploy online learning algorithms (e. g., a multi-armed bandit algorithm) that interact with the system to learn a better/optimal policy under the constraint that during the learning process the performance is almost never worse than the performance of the baseline itself.

no code implementations • 28 Oct 2019 • Shubhanshu Shekhar, Tara Javidi, Mohammad Ghavamzadeh

We consider the problem of allocating samples to a finite set of discrete distributions in order to learn them uniformly well in terms of four common distance measures: $\ell_2^2$, $\ell_1$, $f$-divergence, and separation distance.

no code implementations • ICML 2020 • Manan Tomar, Yonathan Efroni, Mohammad Ghavamzadeh

We derive model-free RL algorithms based on $\kappa$-PI and $\kappa$-VI in which the surrogate problem can be solved by any discrete or continuous action RL method, such as DQN and TRPO.

4 code implementations • 3 Oct 2019 • Scott Fujimoto, Edoardo Conti, Mohammad Ghavamzadeh, Joelle Pineau

Widely-used deep reinforcement learning algorithms have been shown to fail in the batch setting--learning from a fixed data set without interaction with the environment.

no code implementations • 25 Sep 2019 • Yinlam Chow, Ofir Nachum, Aleksandra Faust, Edgar Duenez-Guzman, Mohammad Ghavamzadeh

We study continuous action reinforcement learning problems in which it is crucial that the agent interacts with the environment only through safe policies, i. e.,~policies that keep the agent in desirable situations, both during training and at convergence.

no code implementations • 25 Sep 2019 • Yonathan Efroni, Manan Tomar, Mohammad Ghavamzadeh

In this work, we explore the benefits of multi-step greedy policies in model-free RL when employed in the framework of multi-step Dynamic Programming (DP): multi-step Policy and Value Iteration.

no code implementations • NeurIPS 2020 • Yonathan Efroni, Mohammad Ghavamzadeh, Shie Mannor

This is the first work that proves improved sample complexity as a result of {\em increasing} the lookahead horizon in online planning.

1 code implementation • ICLR 2020 • Nir Levine, Yin-Lam Chow, Rui Shu, Ang Li, Mohammad Ghavamzadeh, Hung Bui

A promising approach is to embed the high-dimensional observations into a lower-dimensional latent representation space, estimate the latent dynamics model, then utilize this model for control in the latent space.

no code implementations • 21 Jun 2019 • Branislav Kveton, Manzil Zaheer, Csaba Szepesvari, Lihong Li, Mohammad Ghavamzadeh, Craig Boutilier

The first, GLM-TSL, samples a generalized linear model (GLM) from the Laplace approximation to the posterior distribution.

no code implementations • 1 Jun 2019 • Shubhanshu Shekhar, Mohammad Ghavamzadeh, Tara Javidi

We construct and analyze active learning algorithms for the problem of binary classification with abstention.

1 code implementation • NeurIPS 2019 • Yonathan Efroni, Nadav Merlis, Mohammad Ghavamzadeh, Shie Mannor

In this paper, we focus on model-based RL in the finite-state finite-horizon MDP setting and establish that exploring with \emph{greedy policies} -- act by \emph{1-step planning} -- can achieve tight minimax performance in terms of regret, $\tilde{\mathcal{O}}(\sqrt{HSAT})$.

Model-based Reinforcement Learning
reinforcement-learning
**+1**

no code implementations • 23 May 2019 • Shubhanshu Shekhar, Mohammad Ghavamzadeh, Tara Javidi

We then propose a plug-in classifier that employs unlabeled samples to decide the region of abstention and derive an upper-bound on the excess risk of our classifier under standard \emph{H\"older smoothness} and \emph{margin} assumptions.

no code implementations • 21 Mar 2019 • Branislav Kveton, Csaba Szepesvari, Mohammad Ghavamzadeh, Craig Boutilier

We evaluate our algorithms empirically and show that they are practical.

no code implementations • 26 Feb 2019 • Branislav Kveton, Csaba Szepesvari, Mohammad Ghavamzadeh, Craig Boutilier

Finally, we empirically evaluate PHE and show that it is competitive with state-of-the-art baselines.

1 code implementation • 28 Jan 2019 • Yin-Lam Chow, Ofir Nachum, Aleksandra Faust, Edgar Duenez-Guzman, Mohammad Ghavamzadeh

We formulate these problems as constrained Markov decision processes (CMDPs) and present safe policy optimization algorithms that are based on a Lyapunov approach to solve them.

no code implementations • 13 Nov 2018 • Branislav Kveton, Csaba Szepesvari, Sharan Vaswani, Zheng Wen, Mohammad Ghavamzadeh, Tor Lattimore

Specifically, it pulls the arm with the highest mean reward in a non-parametric bootstrap sample of its history with pseudo rewards.

no code implementations • 27 Sep 2018 • Yinlam Chow, Ofir Nachum, Mohammad Ghavamzadeh, Edgar Guzman-Duenez

In many reinforcement learning applications, it is crucial that the agent interacts with the environment only through safe policies, i. e.,~policies that do not take the agent to certain undesirable situations.

no code implementations • NeurIPS 2018 • Bo Liu, Tengyang Xie, Yangyang Xu, Mohammad Ghavamzadeh, Yin-Lam Chow, Daoming Lyu, Daesub Yoon

Risk management in dynamic decision problems is a primary concern in many fields, including financial investment, autonomous driving, and healthcare.

no code implementations • 13 Aug 2018 • Jonathan Lacotte, Mohammad Ghavamzadeh, Yin-Lam Chow, Marco Pavone

We then derive two different versions of our RS-GAIL optimization problem that aim at matching the risk profiles of the agent and the expert w. r. t.

1 code implementation • NeurIPS 2018 • Yin-Lam Chow, Ofir Nachum, Edgar Duenez-Guzman, Mohammad Ghavamzadeh

In many real-world reinforcement learning (RL) problems, besides optimizing the main objective function, an agent must concurrently avoid violating a number of constraints.

no code implementations • 26 Feb 2018 • Ershad Banijamali, Yasin Abbasi-Yadkori, Mohammad Ghavamzadeh, Nikos Vlassis

However, under a condition that is akin to the occupancy measures of the base policies having large overlap, we show that there exists an efficient algorithm that finds a policy that is almost as good as the best convex combination of the base policies.

no code implementations • ICML 2018 • Mehrdad Farajtabar, Yin-Lam Chow, Mohammad Ghavamzadeh

In particular, we focus on the doubly robust (DR) estimators that consist of an importance sampling (IS) component and a performance model, and utilize the low (or zero) bias of IS and low variance of the model at the same time.

no code implementations • ICML 2018 • Ofir Nachum, Yin-Lam Chow, Mohammad Ghavamzadeh

In this paper, we follow the work of Nachum et al. (2017) in the soft ERL setting, and propose a class of novel path consistency learning (PCL) algorithms, called {\em sparse PCL}, for the sparse ERL problem that can work with both on-policy and off-policy data.

no code implementations • 24 Nov 2017 • Ershad Banijamali, Ahmad Khajenezhad, Ali Ghodsi, Mohammad Ghavamzadeh

In this paper, We study the problem of learning a controllable representation for high-dimensional observations of dynamical systems.

no code implementations • 15 Oct 2017 • Ershad Banijamali, Rui Shu, Mohammad Ghavamzadeh, Hung Bui, Ali Ghodsi

We also propose a principled variational approximation of the embedding posterior that takes the future observation into account, and thus, makes the variational approximation more robust against the noise.

no code implementations • ICML 2017 • Masrour Zoghi, Tomas Tunys, Mohammad Ghavamzadeh, Branislav Kveton, Csaba Szepesvari, Zheng Wen

In this work, we propose BatchRank, the first online learning to rank algorithm for a broad class of click models.

no code implementations • ICML 2017 • Carlos Riquelme, Mohammad Ghavamzadeh, Alessandro Lazaric

We explore the sequential decision making problem where the goal is to estimate uniformly well a number of linear models, given a shared budget of random contexts independently sampled from a known distribution.

no code implementations • ICML 2017 • Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks Lakshmanan, Mark Schmidt

We consider influence maximization (IM) in social networks, which is the problem of maximizing the number of users that become aware of a product by selecting a set of "seed" users to expose the product to.

1 code implementation • ICML 2017 • Rui Shu, Hung H. Bui, Mohammad Ghavamzadeh

We introduce a new framework for training deep generative models for high-dimensional conditional density estimation.

no code implementations • NeurIPS 2017 • Abbas Kazerouni, Mohammad Ghavamzadeh, Yasin Abbasi-Yadkori, Benjamin Van Roy

We prove an upper-bound on the regret of CLUCB and show that it can be decomposed into two terms: 1) an upper-bound for the regret of the standard linear UCB algorithm that grows with the time horizon and 2) a constant (does not grow with the time horizon) term that accounts for the loss of being conservative in order to satisfy the safety constraint.

no code implementations • 14 Sep 2016 • Mohammad Ghavamzadeh, Shie Mannor, Joelle Pineau, Aviv Tamar

The objective of the paper is to provide a comprehensive survey on Bayesian RL algorithms and their theoretical and empirical properties.

no code implementations • NeurIPS 2016 • Marek Petrik, Yin-Lam Chow, Mohammad Ghavamzadeh

We show that our formulation is NP-hard and propose an approximate algorithm.

no code implementations • 6 Mar 2016 • Sougata Chaudhuri, Georgios Theocharous, Mohammad Ghavamzadeh

We study the problem of personalized advertisement recommendation (PAR), which consist of a user visiting a system (website) and the system displaying one of $K$ ads to the user.

no code implementations • 9 Feb 2016 • Branislav Kveton, Hung Bui, Mohammad Ghavamzadeh, Georgios Theocharous, S. Muthukrishnan, Siqi Sun

Graphical models are a popular approach to modeling structured data but they are unsuitable for high-cardinality variables.

no code implementations • 5 Dec 2015 • Yin-Lam Chow, Mohammad Ghavamzadeh, Lucas Janson, Marco Pavone

In many sequential decision-making problems one is interested in minimizing an expected cumulative cost while taking into account \emph{risk}, i. e., increased awareness of events of small probability and high consequences.

no code implementations • 16 Jul 2015 • Alexandra Carpentier, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos, Peter Auer, András Antos

If the variance of the distributions were known, one could design an optimal sampling strategy by collecting a number of independent samples per distribution that is proportional to their variance.

no code implementations • NeurIPS 2015 • Aviv Tamar, Yin-Lam Chow, Mohammad Ghavamzadeh, Shie Mannor

For static risk measures, our approach is in the spirit of policy gradient algorithms and combines a standard sampling approach with convex programming.

no code implementations • 2 Jul 2014 • Amir-Massoud Farahmand, Doina Precup, André M. S. Barreto, Mohammad Ghavamzadeh

We introduce a general classification-based approximate policy iteration (CAPI) framework, which encompasses a large class of algorithms that can exploit regularities of both the value function and the policy space, depending on what is advantageous.

no code implementations • NeurIPS 2014 • Yin-Lam Chow, Mohammad Ghavamzadeh

In many sequential decision-making problems we may want to manage risk by minimizing some measure of variability in costs in addition to minimizing a standard criterion.

no code implementations • 25 Mar 2014 • Prashanth L. A., Mohammad Ghavamzadeh

For each formulation, we first define a measure of variability for a policy, which in turn gives us a set of risk-sensitive criteria to optimize.

no code implementations • NeurIPS 2013 • Prashanth L. A., Mohammad Ghavamzadeh

For each formulation, we first define a measure of variability for a policy, which in turn gives us a set of risk-sensitive criteria to optimize.

no code implementations • NeurIPS 2013 • Victor Gabillon, Mohammad Ghavamzadeh, Bruno Scherrer

A close look at the literature of this game shows that while ADP algorithms, that have been (almost) entirely based on approximating the value function (value function based), have performed poorly in Tetris, the methods that search directly in the space of policies by learning the policy parameters using an optimization black box, such as the cross entropy (CE) method, have achieved the best reported results.

no code implementations • NeurIPS 2012 • Victor Gabillon, Mohammad Ghavamzadeh, Alessandro Lazaric

We study the problem of identifying the best arm(s) in the stochastic multi-armed bandit setting.

no code implementations • 14 May 2012 • Bruno Scherrer, Victor Gabillon, Mohammad Ghavamzadeh, Matthieu Geist

Modified policy iteration (MPI) is a dynamic programming (DP) algorithm that contains the two celebrated policy and value iteration methods.

no code implementations • 10 May 2012 • Hachem Kadri, Mohammad Ghavamzadeh, Philippe Preux

Finally, we evaluate the performance of our KDE approach using both covariance and conditional covariance kernels on two structured output problems, and compare it to the state-of-the-art kernel-based structured output regression methods.

no code implementations • NeurIPS 2011 • Mohammad Ghavamzadeh, Hilbert J. Kappen, Mohammad G. Azar, Rémi Munos

We introduce a new convergent variant of Q-learning, called speedy Q-learning, to address the problem of slow convergence in the standard form of the Q-learning algorithm.

no code implementations • NeurIPS 2011 • Victor Gabillon, Mohammad Ghavamzadeh, Alessandro Lazaric, Sébastien Bubeck

We first propose an algorithm called Gap-based Exploration (GapE) that focuses on the arms whose mean is close to the mean of the best arm in the same bandit (i. e., small gap).

no code implementations • NeurIPS 2010 • Mohammad Ghavamzadeh, Alessandro Lazaric, Odalric Maillard, Rémi Munos

We provide a thorough theoretical analysis of the LSTD with random projections and derive performance bounds for the resulting algorithm.

no code implementations • NeurIPS 2008 • Amir M. Farahmand, Mohammad Ghavamzadeh, Shie Mannor, Csaba Szepesvári

In this paper we consider approximate policy-iteration-based reinforcement learning algorithms.

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