Ofer Meshi

NeurIPS Conference 2024 Conference Paper

Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval

• Haolun Wu
• Ofer Meshi
• Masrour Zoghi
• Fernando Diaz
• Xue Liu
• Craig Boutilier
• Maryam Karimzadehgan

Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, like single-point and multi-point representations, have limitations w. r. t. \ accuracy, diversity, and adaptability. To overcome these deficiencies, we introduce density-based user representations (DURs), a novel method that leverages Gaussian process regression (GPR) for effective multi-interest recommendation and retrieval. Our approach, GPR4DUR, exploits DURs to capture user interest variability without manual tuning, incorporates uncertainty-awareness, and scales well to large numbers of users. Experiments using real-world offline datasets confirm the adaptability and efficiency of GPR4DUR, while online experiments with simulated users demonstrate its ability to address the exploration-exploitation trade-off by effectively utilizing model uncertainty.

IJCAI Conference 2024 Conference Paper

Model-Free Preference Elicitation

• Carlos Martin
• Craig Boutilier
• Ofer Meshi
• Tuomas Sandholm

In recommender systems, preference elicitation (PE) is an effective way to learn about a user's preferences to improve recommendation quality. Expected value of information (EVOI), a Bayesian technique that computes expected gain in user utility, has proven to be effective in selecting useful PE queries. Most EVOI methods use probabilistic models of user preferences and query responses to compute posterior utilities. By contrast, we develop model-free variants of EVOI that rely on function approximation to obviate the need for specific modeling assumptions. Specifically, we learn user response and utility models from existing data (often available in real-world recommender systems), which are used to estimate EVOI rather than relying on explicit probabilistic inference. We augment our approach by using online planning, specifically, Monte Carlo tree search, to further enhance our elicitation policies. We show that our approach offers significant improvement in recommendation quality over standard baselines on several PE tasks.

IJCAI Conference 2019 Conference Paper

Advantage Amplification in Slowly Evolving Latent-State Environments

• Martin Mladenov
• Ofer Meshi
• Jayden Ooi
• Dale Schuurmans
• Craig Boutilier

Latent-state environments with long horizons, such as those faced by recommender systems, pose significant challenges for reinforcement learning (RL). In this work, we identify and analyze several key hurdles for RL in such environments, including belief state error and small action advantage. We develop a general principle called advantage amplification that an overcome these hurdles through the use of temporal abstraction. We propose several aggregation methods and prove they induce amplification in certain settings. We also bound the loss in optimality incurred by our methods in environments where latent state evolves slowly and demonstrate their performance empirically in a stylized user-modeling task.

JMLR Journal 2019 Journal Article

Train and Test Tightness of LP Relaxations in Structured Prediction

• Ofer Meshi
• Ben London
• Adrian Weller
• David Sontag

Structured prediction is used in areas including computer vision and natural language processing to predict structured outputs such as segmentations or parse trees. In these settings, prediction is performed by MAP inference or, equivalently, by solving an integer linear program. Because of the complex scoring functions required to obtain accurate predictions, both learning and inference typically require the use of approximate solvers. We propose a theoretical explanation for the striking observation that approximations based on linear programming (LP) relaxations are often tight (exact) on real-world instances. In particular, we show that learning with LP relaxed inference encourages integrality of training instances, and that this training tightness generalizes to test data. [abs] [ pdf ][ bib ] &copy JMLR 2019. ( edit, beta )

NeurIPS Conference 2018 Conference Paper

Deep Structured Prediction with Nonlinear Output Transformations

• Colin Graber
• Ofer Meshi
• Alexander Schwing

Deep structured models are widely used for tasks like semantic segmentation, where explicit correlations between variables provide important prior information which generally helps to reduce the data needs of deep nets. However, current deep structured models are restricted by oftentimes very local neighborhood structure, which cannot be increased for computational complexity reasons, and by the fact that the output configuration, or a representation thereof, cannot be transformed further. Very recent approaches which address those issues include graphical model inference inside deep nets so as to permit subsequent non-linear output space transformations. However, optimization of those formulations is challenging and not well understood. Here, we develop a novel model which generalizes existing approaches, such as structured prediction energy networks, and discuss a formulation which maintains applicability of existing inference techniques.

IJCAI Conference 2018 Conference Paper

Planning and Learning with Stochastic Action Sets

• Craig Boutilier
• Alon Cohen
• Avinatan Hassidim
• Yishay Mansour
• Ofer Meshi
• Martin Mladenov
• Dale Schuurmans

In many practical uses of reinforcement learning (RL) the set of actions available at a given state is a random variable, with realizations governed by an exogenous stochastic process. Somewhat surprisingly, the foundations for such sequential decision processes have been unaddressed. In this work, we formalize and investigate MDPs with stochastic action sets (SAS-MDPs) to provide these foundations. We show that optimal policies and value functions in this model have a structure that admits a compact representation. From an RL perspective, we show that Q-learning with sampled action sets is sound. In model-based settings, we consider two important special cases: when individual actions are available with independent probabilities, and a sampling-based model for unknown distributions. We develop polynomial-time value and policy iteration methods for both cases, and provide a polynomial-time linear programming solution for the first case.

NeurIPS Conference 2017 Conference Paper

Asynchronous Parallel Coordinate Minimization for MAP Inference

• Ofer Meshi
• Alexander Schwing

Finding the maximum a-posteriori (MAP) assignment is a central task in graphical models. Since modern applications give rise to very large problem instances, there is increasing need for efficient solvers. In this work we propose to improve the efficiency of coordinate-minimization-based dual-decomposition solvers by running their updates asynchronously in parallel. In this case message-passing inference is performed by multiple processing units simultaneously without coordination, all reading and writing to shared memory. We analyze the convergence properties of the resulting algorithms and identify settings where speedup gains can be expected. Our numerical evaluations show that this approach indeed achieves significant speedups in common computer vision tasks.

IJCAI Conference 2017 Conference Paper

Logistic Markov Decision Processes

• Martin Mladenov
• Craig Boutilier
• Dale Schuurmans
• Ofer Meshi
• Gal Elidan
• Tyler Lu

User modeling in advertising and recommendation has typically focused on myopic predictors of user responses. In this work, we consider the long-term decision problem associated with user interaction. We propose a concise specification of long-term interaction dynamics by combining factored dynamic Bayesian networks with logistic predictors of user responses, allowing state-of-the-art prediction models to be seamlessly extended. We show how to solve such models at scale by providing a constraint generation approach for approximate linear programming that overcomes the variable coupling and non-linearity induced by the logistic regression predictor. The efficacy of the approach is demonstrated on advertising domains with up to 2^54 states and 2^39 actions.

NeurIPS Conference 2016 Conference Paper

Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes

• Dan Garber
• Ofer Meshi

Recently, several works have shown that natural modifications of the classical conditional gradient method (aka Frank-Wolfe algorithm) for constrained convex optimization, provably converge with a linear rate when the feasible set is a polytope, and the objective is smooth and strongly-convex. However, all of these results suffer from two significant shortcomings: i) large memory requirement due to the need to store an explicit convex decomposition of the current iterate, and as a consequence, large running-time overhead per iteration ii) the worst case convergence rate depends unfavorably on the dimension In this work we present a new conditional gradient variant and a corresponding analysis that improves on both of the above shortcomings. In particular, both memory and computation overheads are only linear in the dimension, and in addition, in case the optimal solution is sparse, the new convergence rate replaces a factor which is at least linear in the dimension in previous works, with a linear dependence on the number of non-zeros in the optimal solution At the heart of our method, and corresponding analysis, is a novel way to compute decomposition-invariant away-steps. While our theoretical guarantees do not apply to any polytope, they apply to several important structured polytopes that capture central concepts such as paths in graphs, perfect matchings in bipartite graphs, marginal distributions that arise in structured prediction tasks, and more. Our theoretical findings are complemented by empirical evidence that shows that our method delivers state-of-the-art performance.

ICML Conference 2016 Conference Paper

Train and Test Tightness of LP Relaxations in Structured Prediction

• Ofer Meshi
• Mehrdad Mahdavi
• Adrian Weller
• David A. Sontag

Structured prediction is used in areas such as computer vision and natural language processing to predict structured outputs such as segmentations or parse trees. In these settings, prediction is performed by MAP inference or, equivalently, by solving an integer linear program. Because of the complex scoring functions required to obtain accurate predictions, both learning and inference typically require the use of approximate solvers. We propose a theoretical explanation to the striking observation that approximations based on linear programming (LP) relaxations are often tight on real-world instances. In particular, we show that learning with LP relaxed inference encourages integrality of training instances, and that tightness generalizes from train to test data.

NeurIPS Conference 2015 Conference Paper

Smooth and Strong: MAP Inference with Linear Convergence

• Ofer Meshi
• Mehrdad Mahdavi
• Alex Schwing

Maximum a-posteriori (MAP) inference is an important task for many applications. Although the standard formulation gives rise to a hard combinatorial optimization problem, several effective approximations have been proposed and studied in recent years. We focus on linear programming (LP) relaxations, which have achieved state-of-the-art performance in many applications. However, optimization of the resulting program is in general challenging due to non-smoothness and complex non-separable constraints. Therefore, in this work we study the benefits of augmenting the objective function of the relaxation with strong convexity. Specifically, we introduce strong convexity by adding a quadratic term to the LP relaxation objective. We provide theoretical guarantees for the resulting programs, bounding the difference between their optimal value and the original optimum. Further, we propose suitable optimization algorithms and analyze their convergence.

UAI Conference 2013 Conference Paper

Learning Max-Margin Tree Predictors

• Ofer Meshi
• Elad Eban
• Gal Elidan
• Amir Globerson

Structured prediction is a powerful framework for coping with joint prediction of interacting outputs. A central difficulty in using this framework is that often the correct label dependence structure is unknown. At the same time, we would like to avoid an overly complex structure that will lead to intractable prediction. In this work we address the challenge of learning tree structured predictive models that achieve high accuracy while at the same time facilitate efficient (linear time) inference. We start by proving that this task is in general NP-hard, and then suggest an approximate alternative. Our CRANK approach relies on a novel Circuit- RANK regularizer that penalizes non-tree structures and can be optimized using a convex-concave procedure. We demonstrate the effectiveness of our approach on several domains and show that its accuracy matches that of fully connected models, while performing prediction substantially faster.

NeurIPS Conference 2012 Conference Paper

Convergence Rate Analysis of MAP Coordinate Minimization Algorithms

• Ofer Meshi
• Amir Globerson
• Tommi Jaakkola

Finding maximum aposteriori (MAP) assignments in graphical models is an important task in many applications. Since the problem is generally hard, linear programming (LP) relaxations are often used. Solving these relaxations efficiently is thus an important practical problem. In recent years, several authors have proposed message passing updates corresponding to coordinate descent in the dual LP. However, these are generally not guaranteed to converge to a global optimum. One approach to remedy this is to smooth the LP, and perform coordinate descent on the smoothed dual. However, little is known about the convergence rate of this procedure. Here we perform a thorough rate analysis of such schemes and derive primal and dual convergence rates. We also provide a simple dual to primal mapping that yields feasible primal solutions with a guaranteed rate of convergence. Empirical evaluation supports our theoretical claims and shows that the method is highly competitive with state of the art approaches that yield global optima.

JMLR Journal 2010 Journal Article

FastInf: An Efficient Approximate Inference Library

• Ariel Jaimovich
• Ofer Meshi
• Ian McGraw
• Gal Elidan

The FastInf C++ library is designed to perform memory and time efficient approximate inference in large-scale discrete undirected graphical models. The focus of the library is propagation based approximate inference methods, ranging from the basic loopy belief propagation algorithm to propagation based on convex free energies. Various message scheduling schemes that improve on the standard synchronous or asynchronous approaches are included. Also implemented are a clique tree based exact inference, Gibbs sampling, and the mean field algorithm. In addition to inference, FastInf provides parameter estimation capabilities as well as representation and learning of shared parameters. It offers a rich interface that facilitates extension of the basic classes to other inference and learning methods. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2010. ( edit, beta )

ICML Conference 2010 Conference Paper

Learning Efficiently with Approximate Inference via Dual Losses

• Ofer Meshi
• David A. Sontag
• Tommi S. Jaakkola
• Amir Globerson

NeurIPS Conference 2010 Conference Paper

More data means less inference: A pseudo-max approach to structured learning

• David Sontag
• Ofer Meshi
• Amir Globerson
• Tommi Jaakkola

The problem of learning to predict structured labels is of key importance in many applications. However, for general graph structure both learning and inference in this setting are intractable. Here we show that it is possible to circumvent this difficulty when the input distribution is rich enough via a method similar in spirit to pseudo-likelihood. We show how our new method achieves consistency, and illustrate empirically that it indeed performs as well as exact methods when sufficiently large training sets are used.

UAI Conference 2009 Conference Paper

Convexifying the Bethe Free Energy

• Ofer Meshi
• Ariel Jaimovich
• Amir Globerson
• Nir Friedman

The introduction of loopy belief propagation (LBP) revitalized the application of graphical models in many domains. Many recent works present improvements on the basic LBP algorithm in an attempt to overcome convergence and local optima problems. Notable among these are convexified free energy approximations that lead to inference procedures with provable convergence and quality properties. However, empirically LBP still outperforms most of its convex variants in a variety of settings, as we also demonstrate here. Motivated by this fact we seek convexified free energies that directly approximate the Bethe free energy. We show that the proposed approximations compare favorably with state-of-the art convex free energy approximations.

UAI Conference 2007 Conference Paper

Template Based Inference in Symmetric Relational Markov Random Fields

• Ariel Jaimovich
• Ofer Meshi
• Nir Friedman

Relational Markov Random Fields are a general and flexible framework for reasoning about the joint distribution over attributes of a large number of interacting entities. The main computational difficulty in learning such models is inference. Even when dealing with complete data, where one can summarize a large domain by sufficient statistics, learning requires one to compute the expectation of the sufficient statistics given different parameter choices. The typical solution to this problem is to resort to approximate inference procedures, such as loopy belief propagation. Although these procedures are quite efficient, they still require computation that is on the order of the number of interactions (or features) in the model. When learning a large relational model over a complex domain, even such approximations require unrealistic running time. In this paper we show that for a particular class of relational MRFs, which have inherent symmetry, we can perform the inference needed for learning procedures using a template-level belief propagation. This procedure's running time is proportional to the size of the relational model rather than the size of the domain. Moreover, we show that this computational procedure is equivalent to sychronous loopy belief propagation. This enables a dramatic speedup in inference and learning time. We use this procedure to learn relational MRFs for capturing the joint distribution of large protein-protein interaction networks.