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Siddhartha Sen

7 accepted papers

2026

When does predictive inverse dynamics outperform behavior cloning?

ICML 2026poster

Behavior cloning (BC) is a practical offline imitation learning method, but it often fails when expert demonstrations are limited. Recent works have introduced a class of architectures named predictive inverse dynamics models (PIDM) that combine a future state predictor with an inverse dynamics mode…

Cited by 0SourceScholar
2025

Online Reinforcement Learning in Non-Stationary Context-Driven Environments

ICLR 2025spotlight

We study online reinforcement learning (RL) in non-stationary environments, where a time-varying exogenous context process affects the environment dynamics. Online RL is challenging in such environments due to "catastrophic forgetting" (CF). The agent tends to forget prior knowledge as it trains on…

2024

Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification

AAAI 2024technical

Variance in predictions across different trained models is a significant, under-explored source of error in fair binary classification. In practice, the variance on some data examples is so large that decisions can be effectively arbitrary. To investigate this problem, we take an experimental approa…

2024

Designing Skill-Compatible AI: Methodologies and Frameworks in Chess

ICLR 2024poster

Powerful artificial intelligence systems are often used in settings where they must interact with agents that are computationally much weaker, for example when they work alongside humans or operate in complex environments where some tasks are handled by algorithms, heuristics, or other entities of v…

2024

Maia-2: A Unified Model for Human-AI Alignment in Chess

NeurIPS 2024poster

There are an increasing number of domains in which artificial intelligence (AI) systems both surpass human ability and accurately model human behavior. This introduces the possibility of algorithmically-informed teaching in these domains through more relatable AI partners and deeper insights into hu…

2022

Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments

ICML 2022spotlight

We develop a new, principled algorithm for estimating the contribution of training data points to the behavior of a deep learning model, such as a specific prediction it makes. Our algorithm estimates the AME, a quantity that measures the expected (average) marginal effect of adding a data point to…

2021

Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess

NeurIPS 2021poster

The advent of machine learning models that surpass human decision-making ability in complex domains has initiated a movement towards building AI systems that interact with humans. Many building blocks are essential for this activity, with a central one being the algorithmic characterization of human…