50 citations · 275 across the 56 of their papers we have counts for
13 papers · 1 filter
Neural Pseudo-Label Optimism for the Bank Loan Problem
Aldo Pacchiano, Shaun Singh, Edward Chou +2
We study a class of classification problems best exemplified by the \emph{bank loan} problem, where a lender decides whether or not to issue a loan. The lender only observes whethe…
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit
Aldo Pacchiano, Peter Bartlett, Michael I. Jordan
We study the problem of information sharing and cooperation in Multi-Player Multi-Armed bandits. We propose the first algorithm that achieves logarithmic regret for this problem wh…
Dueling RL: Reinforcement Learning with Trajectory Preferences
Aldo Pacchiano, Aadirupa Saha, Jonathan Lee
We consider the problem of preference based reinforcement learning (PbRL), where, unlike traditional reinforcement learning, an agent receives feedback only in terms of a 1 bit (0/…
Towards an Understanding of Default Policies in Multitask Policy Optimization
Ted Moskovitz, Michael Arbel, Jack Parker-Holder +1
Much of the recent success of deep reinforcement learning has been driven by regularized policy optimization (RPO) algorithms with strong performance across multiple domains. In th…
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection
Matteo Papini, Andrea Tirinzoni, Aldo Pacchiano +3
We study the role of the representation of state-action value functions in regret minimization in finite-horizon Markov Decision Processes (MDPs) with linear structure. We first de…
Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity
Dhruv Malik, Aldo Pacchiano, Vishwak Srinivasan +1
Reinforcement learning (RL) is empirically successful in complex nonlinear Markov decision processes (MDPs) with continuous state spaces. By contrast, the majority of theoretical R…
On the Theory of Reinforcement Learning with Once-per-Episode Feedback
Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett +1
We study a theory of reinforcement learning (RL) in which the learner receives binary feedback only once at the end of an episode. While this is an extreme test case for theory, it…
Parallelizing Contextual Bandits
Jeffrey Chan, Aldo Pacchiano, Nilesh Tripuraneni +3
Standard approaches to decision-making under uncertainty focus on sequential exploration of the space of decisions. However, \textit{simultaneously} proposing a batch of decisions,…
Near Optimal Policy Optimization via REPS
Aldo Pacchiano, Jonathan Lee, Peter Bartlett +1
Since its introduction a decade ago, \emph{relative entropy policy search} (REPS) has demonstrated successful policy learning on a number of simulated and real-world robotic domain…
Unlocking Pixels for Reinforcement Learning via Implicit Attention
Krzysztof Marcin Choromanski, Deepali Jain, Wenhao Yu +9
There has recently been significant interest in training reinforcement learning (RL) agents in vision-based environments. This poses many challenges, such as high dimensionality an…
Tactical Optimism and Pessimism for Deep Reinforcement Learning
Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano +2
In recent years, deep off-policy actor-critic algorithms have become a dominant approach to reinforcement learning for continuous control. One of the primary drivers of this improv…
Fairness with Continuous Optimal Transport
Silvia Chiappa, Aldo Pacchiano
Whilst optimal transport (OT) is increasingly being recognized as a powerful and flexible approach for dealing with fairness issues, current OT fairness methods are confined to the…
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search Spaces
Xingyou Song, Krzysztof Choromanski, Jack Parker-Holder +8
In this paper, we approach the problem of optimizing blackbox functions over large hybrid search spaces consisting of both combinatorial and continuous parameters. We demonstrate t…