Parnian Kassraie

Research Scientist, Google DeepMind

pkassraie [AT] google.com

Research

Ph.D. Research: This body of work focuses on intelligent agents capable of making discoveries in domains where objectives are ambiguous, and data is scarce. Instead of relying on fixed, expert-defined representation of such environments, we study how agents can curate their own data and learn useful representations of the problem space while solving it. We develop a theoretical framework for optimization under unknown representations and proposed two complementary approaches: [LIN], which offers efficient and provably optimal strategies in linear problem settings, and [NN], which leverages the flexibility of neural networks to handle more complex structures, e.g. graph domains. Together, these methods shed light on how we can move from trial-and-error exploration toward autonomous systems capable of genuine discovery.

LITE: Efficiently Estimating Gaussian Probability of Maximality

Nicolas Menet, Jonas Hübotter, Parnian Kassraie, Andreas Krause

In International Conference on Artificial Intelligence and Statistics (AISTATS), 2025

Bandits with Preference Feedback: A Stackelberg Game Perspective

Barna Pásztor*, Parnian Kassraie*, Andreas Krause

In Conference on Neural Information Processing Systems (NeurIPS), 2024

Progressive Entropic Optimal Transport Solvers

Parnian Kassraie, Aram-Alexandre Pooladian, Michal Klein, James Thornton, Jonathan Niles-Weed, Marco Cuturi

In Conference on Neural Information Processing Systems (NeurIPS), 2024

Instance-Dependent Generalization Bounds via Optimal Transport

Songyan Hou*, Parnian Kassraie*, Anastasis Kratsios*, Jonas Rothfuss*, Andreas Krause

Journal of Machine Learning Research, 2023

Anytime Model Selection in Linear Bandits

Parnian Kassraie, Nicolas Emmenegger, Andreas Krause, Aldo Pacchiano

In Conference on Neural Information Processing Systems (NeurIPS), 2023
Also in PAC-Bayes Meets Interactive Learning Workshop at ICML, 2023 (Oral)

Model-Based Optimization over Large Molecular Spaces

Miles Wang-Henderson*, Bartu Soyuer*, Parnian Kassraie, Andreas Krause, Ilija Bogunovic

In ICML Workshop on Structured Probabilistic Inference & Generative Modeling, 2023
Also In NeurIPS Workshop on AI for Drug Discovery, 2023

Hallucinated Adversarial Control for Conservative Offline Policy Evaluation

Jonas Rothfuss*, Bhavya Sukhija*, Tobias Birchler*, Parnian Kassraie, Andreas Krause

In Conference on Uncertainty in Artificial Intelligence (UAI), 2023

Lifelong Bandit Optimization: No prior and No Regret

Felix Schur*, Parnian Kassraie*, Jonas Rothfuss, Andreas Krause

In Conference on Uncertainty in Artificial Intelligence (UAI), 2023

Graph Neural Network Bandits

Parnian Kassraie, Andreas Krause, Ilija Bogunovic

In Conference on Neural Information Processing Systems (NeurIPS), 2022

Meta-Learning Hypothesis Spaces for Sequential Decision-making

Parnian Kassraie, Jonas Rothfuss, Andreas Krause

In International Conference on Machine Learning (ICML), 2022

Also in Safe, Anytime-Valid Inference workshop (SAVI), 2022
Also in Adaptive Experimental Design and Active Learning Workshop (ReALML), 2022

Neural Contextual Bandits without Regret

Parnian Kassraie, Andreas Krause

In International Conference on Artificial Intelligence and Statistics (AISTATS), 2022

Anytime Model Selection in Linear Bandits

Parnian Kassraie, Nicolas Emmenegger, Andreas Krause, Aldo Pacchiano

In Conference on Neural Information Processing Systems (NeurIPS), 2023
Also in PAC-Bayes Meets Interactive Learning Workshop at ICML, 2023 (Oral)

Lifelong Bandit Optimization: No prior and No Regret

Felix Schur*, Parnian Kassraie*, Jonas Rothfuss, Andreas Krause

In Conference on Uncertainty in Artificial Intelligence (UAI), 2023

Meta-Learning Hypothesis Spaces for Sequential Decision-making

Parnian Kassraie, Jonas Rothfuss, Andreas Krause

In International Conference on Machine Learning (ICML), 2022

Also in Safe, Anytime-Valid Inference workshop (SAVI), 2022
Also in Adaptive Experimental Design and Active Learning Workshop (ReALML), 2022

Model-Based Optimization over Large Molecular Spaces

Miles Wang-Henderson*, Bartu Soyuer*, Parnian Kassraie, Andreas Krause, Ilija Bogunovic

In ICML Workshop on Structured Probabilistic Inference & Generative Modeling, 2023
Also In NeurIPS Workshop on AI for Drug Discovery, 2023

Hallucinated Adversarial Control for Conservative Offline Policy Evaluation

Jonas Rothfuss*, Bhavya Sukhija*, Tobias Birchler*, Parnian Kassraie, Andreas Krause

In Conference on Uncertainty in Artificial Intelligence (UAI), 2023

Graph Neural Network Bandits

Parnian Kassraie, Andreas Krause, Ilija Bogunovic

In Conference on Neural Information Processing Systems (NeurIPS), 2022

Neural Contextual Bandits without Regret

Parnian Kassraie, Andreas Krause

In International Conference on Artificial Intelligence and Statistics (AISTATS), 2022

Progressive Entropic Optimal Transport Solvers

Parnian Kassraie, Aram-Alexandre Pooladian, Michal Klein, James Thornton, Jonathan Niles-Weed, Marco Cuturi

In Conference on Neural Information Processing Systems (NeurIPS), 2024

Instance-Dependent Generalization Bounds via Optimal Transport

Songyan Hou*, Parnian Kassraie*, Anastasis Kratsios*, Jonas Rothfuss*, Andreas Krause

Journal of Machine Learning Research, 2023

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