106 citations · 250 across the 18 of their papers we have counts for
18 papers
Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence
András György, Tor Lattimore, Nevena Lazić +1
Sound deductive reasoning -- the ability to derive new knowledge from existing facts and rules -- is an indisputably desirable aspect of general intelligence. Despite the major adv…
Achieving Human Level Competitive Robot Table Tennis
David B. D'Ambrosio, Saminda Abeyruwan, Laura Graesser +24
Achieving human-level speed and performance on real world tasks is a north star for the robotics research community. This work takes a step towards that goal and presents the first…
Robotic Table Tennis: A Case Study into a High Speed Learning System
David B. D'Ambrosio, Jonathan Abelian, Saminda Abeyruwan +32
We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the abilit…
Towards practical reinforcement learning for tokamak magnetic control
Brendan D. Tracey, Andrea Michi, Yuri Chervonyi +15
Reinforcement learning (RL) has shown promising results for real-time control systems, including the domain of plasma magnetic control. However, there are still significant drawbac…
Sample Efficient Deep Reinforcement Learning via Local Planning
Dong Yin, Sridhar Thiagarajan, Nevena Lazic +3
The focus of this work is sample-efficient deep reinforcement learning (RL) with a simulator. One useful property of simulators is that it is typically easy to reset the environmen…
A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits
Yasin Abbasi-Yadkori, Andras Gyorgy, Nevena Lazic
We study the non-stationary stochastic multi-armed bandit problem, where the reward statistics of each arm may change several times during the course of learning. The performance o…
Improved Regret Bound and Experience Replay in Regularized Policy Iteration
Nevena Lazic, Dong Yin, Yasin Abbasi-Yadkori +1
In this work, we study algorithms for learning in infinite-horizon undiscounted Markov decision processes (MDPs) with function approximation. We first show that the regret analysis…
Optimization Issues in KL-Constrained Approximate Policy Iteration
Nevena Lazić, Botao Hao, Yasin Abbasi-Yadkori +2
Many reinforcement learning algorithms can be seen as versions of approximate policy iteration (API). While standard API often performs poorly, it has been shown that learning can…
Neural Rate Control for Video Encoding using Imitation Learning
Hongzi Mao, Chenjie Gu, Miaosen Wang +9
In modern video encoders, rate control is a critical component and has been heavily engineered. It decides how many bits to spend to encode each frame, in order to optimize the rat…
A maximum-entropy approach to off-policy evaluation in average-reward MDPs
Nevena Lazic, Dong Yin, Mehrdad Farajtabar +4
This work focuses on off-policy evaluation (OPE) with function approximation in infinite-horizon undiscounted Markov decision processes (MDPs). For MDPs that are ergodic and linear…
Robotic Table Tennis with Model-Free Reinforcement Learning
Wenbo Gao, Laura Graesser, Krzysztof Choromanski +5
We propose a model-free algorithm for learning efficient policies capable of returning table tennis balls by controlling robot joints at a rate of 100Hz. We demonstrate that evolut…
Adaptive Approximate Policy Iteration
Botao Hao, Nevena Lazic, Yasin Abbasi-Yadkori +2
Model-free reinforcement learning algorithms combined with value function approximation have recently achieved impressive performance in a variety of application domains. However,…
Exploration-Enhanced POLITEX
Yasin Abbasi-Yadkori, Nevena Lazic, Csaba Szepesvari +1
We study algorithms for average-cost reinforcement learning problems with value function approximation. Our starting point is the recently proposed POLITEX algorithm, a version of…
Hierarchical Policy Design for Sample-Efficient Learning of Robot Table Tennis Through Self-Play
Reza Mahjourian, Risto Miikkulainen, Nevena Lazic +2
Training robots with physical bodies requires developing new methods and action representations that allow the learning agents to explore the space of policies efficiently. This wo…
Online Linear Quadratic Control
Alon Cohen, Avinatan Hassidim, Tomer Koren +3
We study the problem of controlling linear time-invariant systems with known noisy dynamics and adversarially chosen quadratic losses. We present the first efficient online learnin…
Model-Free Linear Quadratic Control via Reduction to Expert Prediction
Yasin Abbasi-Yadkori, Nevena Lazic, Csaba Szepesvari
Model-free approaches for reinforcement learning (RL) and continuous control find policies based only on past states and rewards, without fitting a model of the system dynamics. Th…
Sketching and Neural Networks
Amit Daniely, Nevena Lazic, Yoram Singer +1
High-dimensional sparse data present computational and statistical challenges for supervised learning. We propose compact linear sketches for reducing the dimensionality of the inp…
Context-Dependent Fine-Grained Entity Type Tagging
Dan Gillick, Nevena Lazic, Kuzman Ganchev +2
Entity type tagging is the task of assigning category labels to each mention of an entity in a document. While standard systems focus on a small set of types, recent work (Ling and…