7 citations · 16 across the 8 of their papers we have counts for
9 papers
LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!
Dacheng Li, Shiyi Cao, Tyler Griggs +9
Large reasoning models (LRMs) tackle complex reasoning problems by following long chain-of-thoughts (Long CoT) that incorporate reflection, backtracking, and self-validation. Howev…
Semi-Supervised One-Shot Imitation Learning
Philipp Wu, Kourosh Hakhamaneshi, Yuqing Du +3
One-shot Imitation Learning~(OSIL) aims to imbue AI agents with the ability to learn a new task from a single demonstration. To supervise the learning, OSIL typically requires a pr…
Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design
Kourosh Hakhamaneshi, Marcel Nassar, Mariano Phielipp +2
Being able to predict the performance of circuits without running expensive simulations is a desired capability that can catalyze automated design. In this paper, we present a supe…
Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback
Xiaofei Wang, Kimin Lee, Kourosh Hakhamaneshi +2
A promising approach to solving challenging long-horizon tasks has been to extract behavior priors (skills) by fitting generative models to large offline datasets of demonstrations…
Hierarchical Few-Shot Imitation with Skill Transition Models
Kourosh Hakhamaneshi, Ruihan Zhao, Albert Zhan +2
A desirable property of autonomous agents is the ability to both solve long-horizon problems and generalize to unseen tasks. Recent advances in data-driven skill learning have show…
JUMBO: Scalable Multi-task Bayesian Optimization using Offline Data
Kourosh Hakhamaneshi, Pieter Abbeel, Vladimir Stojanovic +1
The goal of Multi-task Bayesian Optimization (MBO) is to minimize the number of queries required to accurately optimize a target black-box function, given access to offline evaluat…
GACEM: Generalized Autoregressive Cross Entropy Method for Multi-Modal Black Box Constraint Satisfaction
Kourosh Hakhamaneshi, Keertana Settaluri, Pieter Abbeel +1
In this work we present a new method of black-box optimization and constraint satisfaction. Existing algorithms that have attempted to solve this problem are unable to consider mul…
AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs
Keertana Settaluri, Ameer Haj-Ali, Qijing Huang +2
Domain specialization under energy constraints in deeply-scaled CMOS has been driving the need for agile development of Systems on a Chip (SoCs). While digital subsystems have desi…
BagNet: Berkeley Analog Generator with Layout Optimizer Boosted with Deep Neural Networks
Kourosh Hakhamaneshi, Nick Werblun, Pieter Abbeel +1
The discrepancy between post-layout and schematic simulation results continues to widen in analog design due in part to the domination of layout parasitics. This paradigm shift is…