Akshay Krishnamurthy

Artificial Intelligence · 2014–2021

A researcher in the Science corpus: 20 papers; trusts 239 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

Artificial Intelligence — 260 citations into it; #5,283 of 35,345 by trust here, #3,998 by citations
Reinforcement Learning — 201 citations into it; #657 of 2,242 by trust here, #436 by citations
Management Science and Operations Research — 136 citations into it; #2,862 of 11,946 by trust here, #1,012 by citations
Statistics and Probability — 51 citations into it
Statistical Learning Theory — 47 citations into it; #460 of 1,038 by trust here, #371 by citations
Optimization & Training — 38 citations into it
Computer Networks and Communications — 33 citations into it
Computer Vision and Pattern Recognition — 33 citations into it; #21,004 of 21,575 by trust here, #18,782 by citations

Whom Akshay Krishnamurthy trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

Who trusts Akshay Krishnamurthy overall

Papers from Akshay Krishnamurthy's perspective

Ordered by how strongly the researchers Akshay Krishnamurthy trusts endorse each paper. Same corpus, a different order for every researcher.

ImageNet classification with deep convolutional neural networks
Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
Gradient-based learning applied to document recognition
Long Short-Term Memory
Attention is All you Need (Neural Information Processing Systems)
ImageNet: A large-scale hierarchical image database
Learning Multiple Layers of Features from Tiny Images
Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

Learning to Search Better than Your Teacher (2015) — 238 citations
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning (2017) — 615 citations
Provably efficient RL with Rich Observations via Latent State Decoding (2019) — 254 citations
Contextual Decision Processes with low Bellman rank are PAC-Learnable (2016) — 462 citations
Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains (2016) — 43 citations
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds (2019) — 1,024 citations
Reward-Free Exploration for Reinforcement Learning (2020) — 237 citations
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs (2020) — 267 citations
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling (2017) — 32 citations
Off-policy evaluation for slate recommendation (2016) — 255 citations
On Oracle-Efficient PAC RL with Rich Observations (2018) — 107 citations
Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability (2021) — 49 citations
On Polynomial Time PAC Reinforcement Learning with Rich Observations (2018) — 9 citations
Parallelised Bayesian Optimisation via Thompson Sampling (2018) — 291 citations
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches (2018) — 245 citations
PAC Reinforcement Learning with Rich Observations (2016) — 172 citations
Efficient Contextual Semi-Bandit Learning (2015) — 9 citations
Nonparametric Estimation of Renyi Divergence and Friends (2014) — 96 citations
On Estimating $L_2^2$ Divergence (2014) — 7 citations
On Estimating L 22 Divergence (2014)