2 citations · 4 across the 7 of their papers we have counts for
7 papers
Active learning of neural population dynamics using two-photon holographic optogenetics
Andrew Wagenmaker, Lu Mi, Marton Rozsa +5
Recent advances in techniques for monitoring and perturbing neural populations have greatly enhanced our ability to study circuits in the brain. In particular, two-photon holograph…
Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL
Andrew Wagenmaker, Kevin Huang, Liyiming Ke +3
In order to mitigate the sample complexity of real-world reinforcement learning, common practice is to first train a policy in a simulator where samples are cheap, and then deploy…
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning
Adhyyan Narang, Andrew Wagenmaker, Lillian Ratliff +1
In this paper, we study the non-asymptotic sample complexity for the pure exploration problem in contextual bandits and tabular reinforcement learning (RL): identifying an epsilon-…
Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning
Jifan Zhang, Lalit Jain, Yang Guo +9
We present a novel multimodal preference dataset for creative tasks, consisting of over 250 million human ratings on more than 2.2 million captions, collected through crowdsourcing…
Fair Active Learning in Low-Data Regimes
Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern +2
In critical machine learning applications, ensuring fairness is essential to avoid perpetuating social inequities. In this work, we address the challenges of reducing bias and impr…
Optimal Exploration is no harder than Thompson Sampling
Zhaoqi Li, Kevin Jamieson, Lalit Jain
Given a set of arms and an unknown parameter vector , the pure exploration linear bandit problem aims to return $\arg\max_{…
Nearly Minimax Optimal Submodular Maximization with Bandit Feedback
Artin Tajdini, Lalit Jain, Kevin Jamieson
We consider maximizing an unknown monotonic, submodular set function with cardinality constraint under stochastic bandit feedback. At each time $t=1,…