You can also find my articles on my Google Scholar profile.
2023
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Transactions on Machine Learning Research (TMLR), 2024
We propose a test-time augmentation approach that leverages LLMs to transform out-of-distribution data into in-distribution to improve robustness of task-specific natural language classifiers.
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Transactions on Machine Learning Research (TMLR), 2023
We survey the mostly disjoint fields of lifelong or continual learning and compositional learning, and draw connections between them.
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Neural Networks, 2023
We propose a unified approach to assess the performance of lifelong learning approaches, agnostic to the specific domain or technique used for learning.
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Journal on Machine Learning Research (JMLR), 2023
We extended the notion of performance gap for measuring domain discrepancy (NeurIPS-19) to a variety of transfer and multi-task learning settings, and introduced two new algorithms based on this notion for improving transfer and multi-task learning performance.
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Conference on Robot Learning (CoRL), 2024
We learn a heuristic to optimize backtracking in sampling-based approaches to Task and Motion Planning, by looking ahead at future actions in a task plan before making decisions about how to parameterize the current action.
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Reinforcement Learning Conference (RLC), 2024
We release four datasets of simulated robotic manipulation trajectories for offline compositional reinforcement learning.
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Conference on Robot Learning (CoRL), 2023
We formulate a realistic variant of the problem of lifelong learning for TAMP, and devise a mixture of generative models for generating samples for efficient planning.
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Conference on Lifelong Learning Agents (CoLLAs), 2022
We introduce SHELS, a combined framework that supports class-incremental continual learning without the specification of explicit class boundaries by performing out-of-distribution detection.
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Conference on Lifelong Learning Agents (CoLLAs), 2022
We introduce CompoSuite, a robotic manipulation benchmark with hundreds of tasks for evaluating the functional compositionality of RL algorithms.
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International Conference on Learning Representations (ICLR), 2022
We explore the problem of lifelong RL of functionally compositional knowledge, and develop an algorithm that demonstrates zero-shot and forward transfer, avoidance of forgetting, and backward transfer in discrete 2-D and robotic manipulation domains.
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International Conference on Learning Representations (ICLR), 2021
We study the question of how to learn compositional parameterized structures from an empirical standpoint, and propose a general-purpose framework that can learn with various forms of knowledge representations and base algorithms.
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Advances in Neural Information Processing Systems (NeurIPS), 2020
We introduced an algorithm for directly optimizing factored policies via policy gradients in a lifelong learning setting, and showed theoretically and empirically that our approach avoids catastrophic forgetting.
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Advances in Neural Information Processing Systems (NeurIPS), 2019
We introduced the notion of performance gap as a label-dependent notion of domain discrepancy, and developed an boosting-based algorithm, gapBoost, that exploits the insights from gap minimization.
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Advances in Neural Information Processing Systems (NeurIPS), 2018
We introduced the problem of lifelong learning from demonstrations, and created an efficient lifelong inverse reinforcement learning (ELIRL) algorithm.
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CoLLAs Workshop Track, 2023
We introduce a simple cross-validation-based procedure to automatically tune the threshold for continual novelty detection using SHELS.
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CoRL Workshop on Pre-training Robot Learning, 2022
We propose and release a variety of data sets for compositional off-line RL on CompoSuite
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3rd Conversational AI Workshop at NeurIPS, 2019
We developed an architecture for learning multi-domain task oriented dialog policies, based on the notion of action embeddings, which capture domain agnostic representations of how to respond to user's queries.
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University of Pennsylvania, 2022
This dissertation presents an in-depth treatment of the problems of lifelong or continual learning and compositional knowledge representations, which had so far been studied separately.