Abhishek Gupta

Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation

2017 · arXiv (Cornell University)

Imitation learning is an effective approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demonstrations from an expert, typically a human operator. However, standard imitation …

Dynamic Workload Migration Over Backbone Network to Minimize Data Center Electricity Cost

2017 · IEEE Transactions on Green Communications and Networking

As more organizations adopt cloud services, energy consumption in data centers (DCs) keeps increasing. Today, information and communication technology (ICT) has become a major consumer of energy worldwide. A large portion of ICT energy consumption …

Unsupervised Curricula for Visual Meta-Reinforcement Learning

2019 · arXiv (Cornell University)

In principle, meta-reinforcement learning algorithms leverage experience across many tasks to learn fast reinforcement learning (RL) strategies that transfer to similar tasks. However, current meta-RL approaches rely on manually-defined distributions of training tasks, and hand-crafting …

AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

2020 · arXiv (Cornell University)

Reinforcement learning (RL) provides an appealing formalism for learning control policies from experience. However, the classic active formulation of RL necessitates a lengthy active exploration process for each behavior, making it difficult to apply in …

Report prepared by the Montreal AI Ethics Institute In Response to Mila's Proposal for a Contact Tracing App

2020 · arXiv (Cornell University)

Contact tracing has grown in popularity as a promising solution to the COVID-19 pandemic. The benefits of automated contact tracing are two-fold. Contact tracing promises to reduce the number of infections by being able to: …

Teachable Reinforcement Learning via Advice Distillation

2022 · arXiv (Cornell University)

Training automated agents to complete complex tasks in interactive environments is challenging: reinforcement learning requires careful hand-engineering of reward functions, imitation learning requires specialized infrastructure and access to a human expert, and learning from intermediate …

Characterizing Acceptance in Post-Selection One-Shot Quantum Hypothesis Testing

2023

In post-selection hypothesis testing, a third outcome is added, corresponding to not selecting any of the hypotheses. In this paradigm, minimum error for various cases is characterized in literature conditioned on the fact that one …

Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised Learning

2023 · arXiv (Cornell University)

Off-policy dynamic programming (DP) techniques such as $Q$-learning have proven to be important in sequential decision-making problems. In the presence of function approximation, however, these techniques often diverge due to the absence of Bellman completeness …

Automated Detection of Deceptive Online Product Reviews Using Supervised Learning Techniques

2025 · Iconic Research and Engineering Journals

This paper presents a machine learning–based system for detecting fake product reviews using Natural Language Processing (NLP) techniques. With the rapid growth of e-commerce, online reviews significantly influence consumer purchasing behavior, but the rise of …

Automatically Composing Representation Transformations as a Means for Generalization

2019 · International Conference on Learning Representations

A generally intelligent learner should generalize to more complex tasks than it has previously encountered, but the two common paradigms in machine learning -- either training a separate learner per task or training a single …