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Aditya Grover
Researcher at Stanford University
Publications - 85
Citations - 12305
Aditya Grover is an academic researcher from Stanford University. The author has contributed to research in topics: Computer science & Inference. The author has an hindex of 22, co-authored 62 publications receiving 6774 citations. Previous affiliations of Aditya Grover include Indian Institute of Technology Delhi & University of California, Berkeley.
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PiRank: Scalable Learning To Rank via Differentiable Sorting
TL;DR: PiRank as mentioned in this paper employs a continuous, temperature-controlled relaxation to the sorting operator based on NeuralSort and shows that it exactly recovers the desired metrics in the limit of zero temperature and further proposes a divide and conquer extension that scales favorably to large list sizes, both in theory and practice.
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JUMBO: Scalable Multi-task Bayesian Optimization using Offline Data
TL;DR: JUMBO as mentioned in this paper proposes to use a combination of acquisition signals derived from training two Gaussian Processes (GP): a cold GP operating directly in the input domain and a warm GP operating in the feature space of a deep neural network pretrained using the offline data.
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Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models
Siyan Zhao,Aditya Grover +1 more
TL;DR: In this paper , the authors decompose goal-conditioned policy agents into three generative modules to simulate the temporal evolution of observations, rewards, and actions via independent generative models that can be learned in parallel via teacher forcing.
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Frame Averaging for Invariant and Equivariant Network Design.
TL;DR: Frame Averaging (FA) as discussed by the authors is a general purpose and systematic framework for adapting known (backbone) architectures to become invariant or equivariant to new symmetry types.
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Generative Decision Making Under Uncertainty
TL;DR: This paper casted the problem of sequential decision making as an inverse problem that can be solved via deep generative models and instantiate these algorithms in the context of reinforcement learning and black-box optimization, and demonstrated that these approaches perform exceedingly well on high-dimensional benchmarks outperforming the current state-of-the-art approaches based on forward models.