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Danyal Saeed

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2 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.

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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.

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