Imitation Learning (IL) is an important paradigm within the broader
rein...
"Non-Malleable Randomness Encoder"(NMRE) was introduced by Kanukurthi,
O...
Non-malleable codes are fundamental objects at the intersection of
crypt...
Autonomous systems often have logical constraints arising, for example, ...
Over the past decade, neural network (NN)-based controllers have demonst...
The Common Information (CI) approach provides a systematic way to transf...
In this paper, we address the following problem: Given an offline
demons...
A graph is 2-degenerate if every subgraph contains a vertex of degree at...
We present a linear program for the one-way version of the partition bou...
We investigate the extent to which offline demonstration data can improv...
Reinforcement Learning (RL) with constraints is becoming an increasingly...
Constrained Markov decision processes (CMDPs) model scenarios of sequent...
Given a natural language instruction, and an input and an output scene, ...
Recent advances in quantum computing and in particular, the introduction...
Non-malleable-codes introduced by Dziembowski, Pietrzak and Wichs [DPW18...
We study regret minimization for infinite-horizon average-reward Markov
...
We introduce two new no-regret algorithms for the stochastic shortest pa...
We present a model-free reinforcement learning algorithm to find an opti...
In this paper, we propose Posterior Sampling Reinforcement Learning for
...
We introduce a framework for decentralized online learning for multi-arm...
We construct several explicit quantum secure non-malleable-extractors. A...
Reachability, distance, and matching are some of the most fundamental gr...
Let f: X × Y →{0,1,} be a partial function and μ
be a distribution with ...
We introduce a generic template for developing regret minimization algor...
We consider the problem of online reinforcement learning for the Stochas...
We give a direct product theorem for the entanglement-assisted interacti...
Multi-source-extractors are functions that extract uniform randomness fr...
The problem of controlling multi-agent systems under different models of...
We revisit the task of quantum state redistribution in the one-shot sett...
We construct in Logspace non-zero circulations for H-minor free graphs w...
Solving Partially Observable Markov Decision Processes (POMDPs) is hard....
The problem of graph Reachability is to decide whether there is a path f...
In an ever expanding set of research and application areas, deep neural
...
Constrained Markov Decision Processes (CMDPs) formalize sequential
decis...
We prove a direct product theorem for the one-way entanglement-assisted
...
We show a near optimal direct-sum theorem for the two-party randomized
c...
We develop several new algorithms for learning Markov Decision Processes...
Recently, model-free reinforcement learning has attracted research atten...
Deep reinforcement learning methods have achieved state-of-the-art resul...
One of the classical line of work in graph algorithms has been the
Repla...
A graph separator is a subset of vertices of a graph whose removal divid...
Model-free reinforcement learning is known to be memory and computation
...
We consider a two stage market mechanism for trading electricity includi...
The reachability problem is to determine if there exists a path from one...
Reachability is the problem of deciding whether there is a path from one...
Over the past few years, the use of camera-equipped robotic platforms fo...
We present a two stage auction mechanism that renewable generators (or
a...
We study the communication capabilities of a quantum channel under the m...
Smooth entropies are a tool for quantifying resource trade-offs in (quan...
Consider a contraction operator T over a Banach space X with a fixed
po...