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Brian Ziebart

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18 papers

1 author row

NeurIPS Conference 2025 Conference Paper

Imitation Beyond Expectation Using Pluralistic Stochastic Dominance

Ali Farajzadeh
Danyal Saeed
Syed M Abbas
Rushit Shah
Aadirupa Saha
Brian Ziebart

Imitation learning seeks policies reflecting the values of demonstrated behaviors. Prevalent approaches learn to match or exceed the demonstrator's performance in expectation without knowing the demonstrator’s reward function. Unfortunately, this does not induce pluralistic imitators that learn to support qualitatively distinct demonstrations. We reformulate imitation learning using stochastic dominance over the demonstrations' reward distribution across a range of reward functions as our foundational aim. Our approach matches imitator policy samples (or support) with demonstrations using optimal transport theory to define an imitation learning objective over trajectory pairs. We demonstrate the benefits of pluralistic stochastic dominance (PSD) for imitation in both theory and practice.

IJCAI Conference 2025 Conference Paper

Imitation Learning via Focused Satisficing

Rushit N. Shah
Nikolaos Agadakos
Synthia Sasulski
Ali Farajzadeh
Sanjiban Choudhury
Brian Ziebart

Imitation learning often assumes that demonstrations are close to optimal according to some fixed, but unknown, cost function. However, according to satisficing theory, humans often choose acceptable behavior based on their personal (and potentially dynamic) levels of aspiration, rather than achieving (near-) optimality. For example, a lunar lander demonstration that successfully lands without crashing might be acceptable to a novice despite being slow or jerky. Using a margin-based objective to guide deep reinforcement learning, our focused satisficing approach to imitation learning seeks a policy that surpasses the demonstrator's aspiration levels---defined over trajectories or portions of trajectories---on unseen demonstrations without explicitly learning those aspirations. We show experimentally that this focuses the policy to imitate the highest quality (portions of) demonstrations better than existing imitation learning methods, providing much higher rates of guaranteed acceptability to the demonstrator, and competitive true returns on a range of environments.

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NeurIPS Conference 2023 Conference Paper

Distributionally Robust Skeleton Learning of Discrete Bayesian Networks

Yeshu Li
Brian Ziebart

We consider the problem of learning the exact skeleton of general discrete Bayesian networks from potentially corrupted data. Building on distributionally robust optimization and a regression approach, we propose to optimize the most adverse risk over a family of distributions within bounded Wasserstein distance or KL divergence to the empirical distribution. The worst-case risk accounts for the effect of outliers. The proposed approach applies for general categorical random variables without assuming faithfulness, an ordinal relationship or a specific form of conditional distribution. We present efficient algorithms and show the proposed methods are closely related to the standard regularized regression approach. Under mild assumptions, we derive non-asymptotic guarantees for successful structure learning with logarithmic sample complexities for bounded-degree graphs. Numerical study on synthetic and real datasets validates the effectiveness of our method.

NeurIPS Conference 2022 Conference Paper

Moment Distributionally Robust Tree Structured Prediction

Yeshu Li
Danyal Saeed
Xinhua Zhang
Brian Ziebart
Kevin Gimpel

Structured prediction of tree-shaped objects is heavily studied under the name of syntactic dependency parsing. Current practice based on maximum likelihood or margin is either agnostic to or inconsistent with the evaluation loss. Risk minimization alleviates the discrepancy between training and test objectives but typically induces a non-convex problem. These approaches adopt explicit regularization to combat overfitting without probabilistic interpretation. We propose a moment-based distributionally robust optimization approach for tree structured prediction, where the worst-case expected loss over a set of distributions within bounded moment divergence from the empirical distribution is minimized. We develop efficient algorithms for arborescences and other variants of trees. We derive Fisher consistency, convergence rates and generalization bounds for our proposed method. We evaluate its empirical effectiveness on dependency parsing benchmarks.

NeurIPS Conference 2021 Conference Paper

Distributionally Robust Imitation Learning

Mohammad Ali Bashiri
Brian Ziebart
Xinhua Zhang

We consider the imitation learning problem of learning a policy in a Markov Decision Process (MDP) setting where the reward function is not given, but demonstrations from experts are available. Although the goal of imitation learning is to learn a policy that produces behaviors nearly as good as the experts’ for a desired task, assumptions of consistent optimality for demonstrated behaviors are often violated in practice. Finding a policy that is distributionally robust against noisy demonstrations based on an adversarial construction potentially solves this problem by avoiding optimistic generalizations of the demonstrated data. This paper studies Distributionally Robust Imitation Learning (DRoIL) and establishes a close connection between DRoIL and Maximum Entropy Inverse Reinforcement Learning. We show that DRoIL can be seen as a framework that maximizes a generalized concept of entropy. We develop a novel approach to transform the objective function into a convex optimization problem over a polynomial number of variables for a class of loss functions that are additive over state and action spaces. Our approach lets us optimize both stationary and non-stationary policies and, unlike prevalent previous methods, it does not require repeatedly solving an inner reinforcement learning problem. We experimentally show the significant benefits of DRoIL’s new optimization method on synthetic data and a highway driving environment.

AAAI Conference 2020 Conference Paper

Fairness for Robust Log Loss Classification

Ashkan Rezaei
Rizal Fathony
Omid Memarrast
Brian Ziebart

Developing classiﬁcation methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in social applications. Many existing methods enforce fairness constraints on a selected classiﬁer (e. g. , logistic regression) by directly forming constrained optimizations. We instead rederive a new classiﬁer from the ﬁrst principles of distributional robustness that incorporates fairness criteria into a worst-case logarithmic loss minimization. This construction takes the form of a minimax game and produces a parametric exponential family conditional distribution that resembles truncated logistic regression. We present the theoretical beneﬁts of our approach in terms of its convexity and asymptotic convergence. We then demonstrate the practical advantages of our approach on three benchmark fairness datasets.

AAMAS Conference 2019 Conference Paper

Discriminatively Learning Inverse Optimal Control Models for Predicting Human Intentions

Sanket Gaurav
Brian Ziebart

More accurately inferring human intentions/goals can help robots complete collaborative human-robot tasks more safely and efficiently. Bayesian reasoning has become a popular approach for predicting the intention or goal of a partial sequence of actions/controls using a trajectory likelihood model. However, the mismatch between the training objective for these models (maximizing trajectory likelihood) and the application objective (maximizing intention likelihood) can be detrimental. In this paper, we seek to improve the goal prediction of maximum entropy inverse reinforcement learning (MaxEnt IRL) models by training to maximize goal likelihood. We demonstrate the benefits of our method on pointing task goal prediction with multiple possible goals and predicting goal based activities in the Cornell Activity Dataset (CAD-120).

AAAI Conference 2018 Conference Paper

ARC: Adversarial Robust Cuts for Semi-Supervised and Multi-Label Classification

Sima Behpour
Wei Xing
Brian Ziebart

Many structured prediction tasks arising in computer vision and natural language processing tractably reduce to making minimum cost cuts in graphs with edge weights learned using maximum margin methods. Unfortunately, the hinge loss used to construct these methods often provides a particularly loose bound on the loss function of interest (e. g. , the Hamming loss). We develop Adversarial Robust Cuts (ARC), an approach that poses the learning task as a minimax game between predictor and “label approximator” based on minimum cost graph cuts. Unlike maximum margin methods, this game-theoretic perspective always provides meaningful bounds on the Hamming loss. We conduct multi-label and semi-supervised binary prediction experiments that demonstrate the beneﬁts of our approach.

NeurIPS Conference 2018 Conference Paper

Distributionally Robust Graphical Models

Rizal Fathony
Ashkan Rezaei
Mohammad Ali Bashiri
Xinhua Zhang
Brian Ziebart

In many structured prediction problems, complex relationships between variables are compactly defined using graphical structures. The most prevalent graphical prediction methods---probabilistic graphical models and large margin methods---have their own distinct strengths but also possess significant drawbacks. Conditional random fields (CRFs) are Fisher consistent, but they do not permit integration of customized loss metrics into their learning process. Large-margin models, such as structured support vector machines (SSVMs), have the flexibility to incorporate customized loss metrics, but lack Fisher consistency guarantees. We present adversarial graphical models (AGM), a distributionally robust approach for constructing a predictor that performs robustly for a class of data distributions defined using a graphical structure. Our approach enjoys both the flexibility of incorporating customized loss metrics into its design as well as the statistical guarantee of Fisher consistency. We present exact learning and prediction algorithms for AGM with time complexity similar to existing graphical models and show the practical benefits of our approach with experiments.

NeurIPS Conference 2018 Conference Paper

Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes

Andrea Tirinzoni
Marek Petrik
Xiangli Chen
Brian Ziebart

What policy should be employed in a Markov decision process with uncertain parameters? Robust optimization answer to this question is to use rectangular uncertainty sets, which independently reflect available knowledge about each state, and then obtains a decision policy that maximizes expected reward for the worst-case decision process parameters from these uncertainty sets. While this rectangularity is convenient computationally and leads to tractable solutions, it often produces policies that are too conservative in practice, and does not facilitate knowledge transfer between portions of the state space or across related decision processes. In this work, we propose non-rectangular uncertainty sets that bound marginal moments of state-action features defined over entire trajectories through a decision process. This enables generalization to different portions of the state space while retaining appropriate uncertainty of the decision process. We develop algorithms for solving the resulting robust decision problems, which reduce to finding an optimal policy for a mixture of decision processes, and demonstrate the benefits of our approach experimentally.

NeurIPS Conference 2017 Conference Paper

Adversarial Surrogate Losses for Ordinal Regression

Rizal Fathony
Mohammad Ali Bashiri
Brian Ziebart

Ordinal regression seeks class label predictions when the penalty incurred for mistakes increases according to an ordering over the labels. The absolute error is a canonical example. Many existing methods for this task reduce to binary classification problems and employ surrogate losses, such as the hinge loss. We instead derive uniquely defined surrogate ordinal regression loss functions by seeking the predictor that is robust to the worst-case approximations of training data labels, subject to matching certain provided training data statistics. We demonstrate the advantages of our approach over other surrogate losses based on hinge loss approximations using UCI ordinal prediction tasks.

NeurIPS Conference 2016 Conference Paper

Adversarial Multiclass Classification: A Risk Minimization Perspective

Rizal Fathony
Anqi Liu
Kaiser Asif
Brian Ziebart

Recently proposed adversarial classification methods have shown promising results for cost sensitive and multivariate losses. In contrast with empirical risk minimization (ERM) methods, which use convex surrogate losses to approximate the desired non-convex target loss function, adversarial methods minimize non-convex losses by treating the properties of the training data as being uncertain and worst case within a minimax game. Despite this difference in formulation, we recast adversarial classification under zero-one loss as an ERM method with a novel prescribed loss function. We demonstrate a number of theoretical and practical advantages over the very closely related hinge loss ERM methods. This establishes adversarial classification under the zero-one loss as a method that fills the long standing gap in multiclass hinge loss classification, simultaneously guaranteeing Fisher consistency and universal consistency, while also providing dual parameter sparsity and high accuracy predictions in practice.

NeurIPS Conference 2015 Conference Paper

Adversarial Prediction Games for Multivariate Losses

Hong Wang
Wei Xing
Kaiser Asif
Brian Ziebart

Multivariate loss functions are used to assess performance in many modern prediction tasks, including information retrieval and ranking applications. Convex approximations are typically optimized in their place to avoid NP-hard empirical risk minimization problems. We propose to approximate the training data instead of the loss function by posing multivariate prediction as an adversarial game between a loss-minimizing prediction player and a loss-maximizing evaluation player constrained to match specified properties of training data. This avoids the non-convexity of empirical risk minimization, but game sizes are exponential in the number of predicted variables. We overcome this intractability using the double oracle constraint generation method. We demonstrate the efficiency and predictive performance of our approach on tasks evaluated using the precision at k, the F-score and the discounted cumulative gain.

IJCAI Conference 2015 Conference Paper

Graph-Based Inverse Optimal Control for Robot Manipulation

Arunkumar Byravan
Mathew Monfort
Brian Ziebart
Byron Boots
Dieter Fox

Inverse optimal control (IOC) is a powerful approach for learning robotic controllers from demonstration that estimates a cost function which rationalizes demonstrated control trajectories. Unfortunately, its applicability is difficult in settings where optimal control can only be solved approximately. While local IOC approaches have been shown to successfully learn cost functions in such settings, they rely on the availability of good reference trajectories, which might not be available at test time. We address the problem of using IOC in these computationally challenging control tasks by using a graph-based discretization of the trajectory space. Our approach projects continuous demonstrations onto this discrete graph, where a cost function can be tractably learned via IOC. Discrete control trajectories from the graph are then projected back to the original space and locally optimized using the learned cost function. We demonstrate the effectiveness of the approach with experiments conducted on two 7-degree of freedom robotic arms.

AAAI Conference 2015 Conference Paper

Intent Prediction and Trajectory Forecasting via Predictive Inverse Linear-Quadratic Regulation

Mathew Monfort
Anqi Liu
Brian Ziebart

To facilitate interaction with people, robots must not only recognize current actions, but also infer a person’s intentions and future behavior. Recent advances in depth camera technology have significantly improved human motion tracking. However, the inherent high dimensionality of interacting with the physical world makes efficiently forecasting human intention and future behavior a challenging task. Predictive methods that estimate uncertainty are therefore critical for supporting appropriate robotic responses to the many ambiguities posed within the human-robot interaction setting. We address these two challenges, high dimensionality and uncertainty, by employing predictive inverse optimal control methods to estimate a probabilistic model of human motion trajectories. Our inverse optimal control formulation estimates quadratic cost functions that best rationalize observed trajectories framed as solutions to linear-quadratic regularization problems. The formulation calibrates its uncertainty from observed motion trajectories, and is efficient in highdimensional state spaces with linear dynamics. We demonstrate its effectiveness on a task of anticipating the future trajectories, target locations and activity intentions of hand motions.

AAAI Conference 2015 Conference Paper

Shift-Pessimistic Active Learning Using Robust Bias-Aware Prediction

Anqi Liu
Lev Reyzin
Brian Ziebart

Existing approaches to active learning are generally optimistic about their certainty with respect to data shift between labeled and unlabeled data. They assume that unknown datapoint labels follow the inductive biases of the active learner. As a result, the most useful datapoint labels—ones that refute current inductive biases— are rarely solicited. We propose a shift-pessimistic approach to active learning that assumes the worst-case about the unknown conditional label distribution. This closely aligns model uncertainty with generalization error, enabling more useful label solicitation. We investigate the theoretical benefits of this approach and demonstrate its empirical advantages on probabilistic binary classification tasks.

NeurIPS Conference 2015 Conference Paper

Softstar: Heuristic-Guided Probabilistic Inference

Mathew Monfort
Brenden Lake
Brian Ziebart
Patrick Lucey
Josh Tenenbaum

Recent machine learning methods for sequential behavior prediction estimate the motives of behavior rather than the behavior itself. This higher-level abstraction improves generalization in different prediction settings, but computing predictions often becomes intractable in large decision spaces. We propose the Softstar algorithm, a softened heuristic-guided search technique for the maximum entropy inverse optimal control model of sequential behavior. This approach supports probabilistic search with bounded approximation error at a significantly reduced computational cost when compared to sampling based methods. We present the algorithm, analyze approximation guarantees, and compare performance with simulation-based inference on two distinct complex decision tasks.

NeurIPS Conference 2014 Conference Paper

Robust Classification Under Sample Selection Bias

Anqi Liu
Brian Ziebart

In many important machine learning applications, the source distribution used to estimate a probabilistic classifier differs from the target distribution on which the classifier will be used to make predictions. Due to its asymptotic properties, sample-reweighted loss minimization is a commonly employed technique to deal with this difference. However, given finite amounts of labeled source data, this technique suffers from significant estimation errors in settings with large sample selection bias. We develop a framework for robustly learning a probabilistic classifier that adapts to different sample selection biases using a minimax estimation formulation. Our approach requires only accurate estimates of statistics under the source distribution and is otherwise as robust as possible to unknown properties of the conditional label distribution, except when explicit generalization assumptions are incorporated. We demonstrate the behavior and effectiveness of our approach on synthetic and UCI binary classification tasks.