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Ali Farajzadeh

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3 papers

1 author row

AAMAS Conference 2026 Conference Paper

Stochastically Dominant Preference Optimization: Policy Improvement For All

Ali Farajzadeh
Syed M. Abbas
Aadirupa Saha
Brian D. Ziebart

Reinforcement learning from human feedback (RLHF) optimizes policies based on users’ rankings of output samples rather than using user-provided rewards. These methods typically assume users’ underlying utility functions are homogeneous and their rankings differ only due to noise, ultimately optimizing for the average user. Instead, we seek policies that guarantee improvement for all users with respect to their heterogeneous preferences. We introduce stochastic dominance as a stricter guiding criteria for policy optimization that guarantees improvement under any social welfare function. Our approach, stochastically dominant preference optimization (SDPO), avoids explicit reward function estimation while providing individual performance improvement guarantees for users with diverse preferences.

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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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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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