Fetching the paper…
Reading the bibliography…
Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of AI technologies on society.
“Sur les applications de la théorie des probabilités aux experiences agricoles: Essai des principes”
Jersey Neyman · 1923
Earlier work this paper cites.
“Estimation of the parameters of a single equation in a complete system of stochastic equations”
Theodore˜W Anderson and Herman Rubin · 1949
Earlier work this paper cites.
“The asymptotic properties of estimates of the parameters of a single equation in a complete system of stochastic equations”
Theodore˜W Anderson and Herman Rubin · 1950
Earlier work this paper cites.
“The estimation of economic relationships using instrumental variables”
John˜D Sargan · 1958
Earlier work this paper cites.
“The estimation of relationships with autocorrelated residuals by the use of instrumental variables”
J˜Denis Sargan · 1959
Earlier work this paper cites.
“A formal theory of inductive inference. Part I”
Ray˜J Solomonoff · 1964
Earlier work this paper cites.
“A formal theory of inductive inference. Part II”
Ray˜J Solomonoff · 1964
Earlier work this paper cites.
“Some philosophical problems from the standpoint of artificial intelligence”
John McCarthy and Patrick˜J Hayes · 1969
Earlier work this paper cites.
“Estimating causal effects of treatments in randomized and nonrandomized studies.”
Donald˜B Rubin · 1974
Earlier work this paper cites.
“Maximum likelihood estimation of observer error-rates using the EM algorithm”
Alexander˜Philip Dawid and Allan˜M Skene · 1979
Earlier work this paper cites.
“Computers and the theory of statistics: thinking the unthinkable”
Bradley Efron · 1979
Earlier work this paper cites.
“Large sample properties of generalized method of moments estimators”
Lars˜Peter Hansen · 1982
Earlier work this paper cites.
“Problems of monetary management: the UK experience”
Charles˜AE Goodhart · 1984
Earlier work this paper cites.
“Detecting changes in signals and systems—a survey”
Mich“‘ele Basseville · 1988
Earlier work this paper cites.
“The design for the Wall Street Journal-based CSR corpus”
Douglas˜B Paul and Janet˜M Baker · 1992
Earlier work this paper cites.
“Feudal reinforcement learning”
Peter Dayan and Geoffrey˜E Hinton · 1993
Earlier work this paper cites.
“Tagging English text with a probabilistic model”
Bernard Merialdo · 1994
Earlier work this paper cites.
“Networks and economic life”
Walter˜W Powell and Laurel Smith-Doerr · 1994
Earlier work this paper cites.
“The first law of robotics (a call to arms)”
Daniel Weld and Oren Etzioni · 1994
Earlier work this paper cites.
“Artificial evolution in the physical world”
Adrian Thompson · 1997
Earlier work this paper cites.
“Planning and acting in partially observable stochastic domains”
Leslie˜Pack Kaelbling, Michael˜L Littman and Anthony˜R Cassandra · 1998
Earlier work this paper cites.
“Learning to classify text from labeled and unlabeled documents”
Kamal Nigam, Andrew McCallum, Sebastian Thrun and Tom Mitchell · 1998
Earlier work this paper cites.
“Reinforcement learning: An introduction”
Richard˜S Sutton and Andrew˜G Barto · 1998
Earlier work this paper cites.
“Controllers for reachability specifications for hybrid systems”
John Lygeros, Claire Tomlin and Shankar Sastry · 1999
Earlier work this paper cites.
“Algorithms for inverse reinforcement learning.”
Andrew˜Y Ng and Stuart˜J Russell · 2000
Earlier work this paper cites.
“Improving predictive inference under covariate shift by weighting the log-likelihood function”
Hidetoshi Shimodaira · 2000
Earlier work this paper cites.
“Tracking the best linear predictor”
Mark Herbster and Manfred˜K Warmuth · 2001
Earlier work this paper cites.
“The evolved radio and its implications for modelling the evolution of novel sensors”
Jon Bird and Paul Layzell · 2002
Earlier work this paper cites.
“R-max-a general polynomial time algorithm for near-optimal reinforcement learning”
Ronen˜I Brafman and Moshe Tennenholtz · 2003
Earlier work this paper cites.
“Learning with drift detection”
Joao Gama, Pedro Medas, Gladys Castillo and Pedro Rodrigues · 2004
Earlier work this paper cites.
“Exploration and apprenticeship learning in reinforcement learning”
Pieter Abbeel and Andrew˜Y Ng · 2005
Earlier work this paper cites.
“Robust dynamic programming”
Garud˜N Iyengar · 2005
Earlier work this paper cites.
“A time-dependent Hamilton-Jacobi formulation of reachable sets for continuous dynamic games”
Ian˜M Mitchell, Alexandre˜M Bayen and Claire˜J Tomlin · 2005
Earlier work this paper cites.
“Robust control of Markov decision processes with uncertain transition matrices”
Arnab Nilim and Laurent El˜Ghaoui · 2005
Earlier work this paper cites.
“Risks of semi-supervised learning”
Fabio Cozman and Ira Cohen · 2006
Earlier work this paper cites.
“EL Lehmann, JP Romano: Testing statistical hypotheses”
J Steinebach · 2006
Earlier work this paper cites.
“Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification”
John Blitzer, Mark Dredze and Fernando Pereira · 2007
Earlier work this paper cites.
“Self-taught learning: transfer learning from unlabeled data”
Rajat Raina et al · 2007
Earlier work this paper cites.
“H-infinity optimal control and related minimax design problems: a dynamic game approach”
Tamer Basar and Pierre Bernhard · 2008
Earlier work this paper cites.
“Learning from labeled features using generalized expectation criteria”
Gregory Druck, Gideon Mann and Andrew McCallum · 2008
Earlier work this paper cites.
“Kernel methods in machine learning”
Thomas Hofmann, Bernhard Sch“”olkopf and Alexander˜J Smola · 2008
Earlier work this paper cites.
“Analyzing the Errors of Unsupervised Learning.”
Percy Liang and Dan Klein · 2008
Earlier work this paper cites.
“Discrimination-aware data mining”
Dino Pedreshi, Salvatore Ruggieri and Franco Turini · 2008
Earlier work this paper cites.
“A tutorial on conformal prediction”
Glenn Shafer and Vladimir Vovk · 2008
Earlier work this paper cites.
“Artificial intelligence as a positive and negative factor in global risk”
Eliezer Yudkowsky · 2008
Earlier work this paper cites.
“Twitter sentiment classification using distant supervision”
Alec Go, Richa Bhayani and Lei Huang · 2009
Earlier work this paper cites.
“Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation.”
Yoshinobu Kawahara and Masashi Sugiyama · 2009
Earlier work this paper cites.
“Distant supervision for relation extraction without labeled data”
Mike Mintz, Steven Bills, Rion Snow and Dan Jurafsky · 2009
Earlier work this paper cites.
“Causal inference in statistics: An overview”
Judea Pearl · 2009
Earlier work this paper cites.
“Dataset shift in machine learning, ser. Neural information processing series”
Joaquin Quinonero-Candela, Masashi Sugiyama, Anton Schwaighofer and Neil˜D Lawrence · 2009
Earlier work this paper cites.
“Transfer learning for reinforcement learning domains: A survey”
Matthew˜E Taylor and Peter Stone · 2009
Earlier work this paper cites.
“Utility indifference”, 2010
Stuart Armstrong · 2010
Earlier work this paper cites.
“The security of machine learning”
Marco Barreno, Blaine Nelson, Anthony˜D Joseph and JD Tygar · 2010
Earlier work this paper cites.
“Unsupervised supervised learning i: Estimating classification and regression errors without labels”
Pinar Donmez, Guy Lebanon and Krishnakumar Balasubramanian · 2010
Earlier work this paper cites.
“Generalized expectation criteria for semi-supervised learning with weakly labeled data”
Gideon˜S Mann and Andrew McCallum · 2010
Earlier work this paper cites.
“A reduction of imitation learning and structured prediction to no-regret online learning”
St“’ephane Ross, Geoffrey˜J Gordon and J˜Andrew Bagnell · 2010
Earlier work this paper cites.
“Unsupervised supervised learning ii: Margin-based classification without labels”
Krishnakumar Balasubramanian, Pinar Donmez and Guy Lebanon · 2011
Earlier work this paper cites.
“Domain adaptation with coupled subspaces”
John Blitzer, Sham Kakade and Dean˜P Foster · 2011
Cited alongside, same era.
“Open robotics”
Ryan Calo · 2011
Cited alongside, same era.
“Learning what to value”
Daniel Dewey · 2011
Cited alongside, same era.
“Towards fully autonomous driving: Systems and algorithms”
Jesse Levinson et al · 2011
Cited alongside, same era.
“Knows what it knows: a framework for self-aware learning”
Lihong Li, Michael˜L Littman, Thomas˜J Walsh and Alexander˜L Strehl · 2011
Cited alongside, same era.
“Delusion, survival, and intelligent agents”
Mark Ring and Laurent Orseau · 2011
Cited alongside, same era.
“Unbiased look at dataset bias”
Antonio Torralba and Alexei˜A Efros · 2011
“Distantly Supervised Information Extraction Using Bootstrapped Patterns”, 2015
Sonal Gupta · 2015
Later among the works it cites.
“A formally verified hybrid system for the next-generation airborne collision avoidance system”
Jean-Baptiste Jeannin et al · 2015
Later among the works it cites.
“Neural GPUs learn algorithms”
ukasz Kaiser and Ilya Sutskever · 2015
Later among the works it cites.
“Calibrated Structured Prediction”
Volodymyr Kuleshov and Percy˜S Liang · 2015
Later among the works it cites.
“Towards making unlabeled data never hurt”
Yu-Feng Li and Zhi-Hua Zhou · 2015
Later among the works it cites.
“On the Elusiveness of a Specification for AI” NIPS 2015, Symposium: Algorithms Among Us, 2015
Percy Liang · 2015
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
“A method of moments for mixture models and hidden Markov models”
Animashree Anandkumar, Daniel Hsu and Sham˜M Kakade · 2012
Cited alongside, same era.
“The mathematics of reduced impact: help needed”, 2012
Stuart Armstrong · 2012
Cited alongside, same era.
“Counterfactual Reasoning and Learning Systems”
L“’eon Bottou et al · 2012
Cited alongside, same era.
“Fairness through awareness”
Cynthia Dwork et al · 2012
Cited alongside, same era.
“Model-based utility functions”
Bill Hibbard · 2012
Cited alongside, same era.
Later among the works it cites.
“The Security of Latent Dirichlet Allocation.”
Shike Mei and Xiaojin Zhu · 2015
Later among the works it cites.
“Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners.”
Shike Mei and Xiaojin Zhu · 2015
Later among the works it cites.
“Human-level control through deep reinforcement learning”
Volodymyr Mnih et al · 2015
Later among the works it cites.
“Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning”
Shakir Mohamed and Danilo˜Jimenez Rezende · 2015
Later among the works it cites.
“Inceptionism: Going deeper into neural networks”
Alexander Mordvintsev, Christopher Olah and Mike Tyka · 2015
Later among the works it cites.
“Deep neural networks are easily fooled: High confidence predictions for unrecognizable images”
Anh Nguyen, Jason Yosinski and Jeff Clune · 2015
Later among the works it cites.
“Visualizing Representations: Deep Learning and Human Beings”, 2015
Christopher Olah · 2015
Later among the works it cites.
“Estimating accuracy from unlabeled data”, 2015
Emmanouil˜Antonios Platanios · 2015
Later among the works it cites.
“Massively multitask networks for drug discovery”
Bharath Ramsundar et al · 2015
Later among the works it cites.
“Research priorities for robust and beneficial artificial intelligence”
Stuart Russell et al · 2015
Later among the works it cites.
“High-dimensional continuous control using generalized advantage estimation”
John Schulman et al · 2015
Later among the works it cites.
“Incremental knowledge base construction using deepdive”
Jaeho Shin et al · 2015
Later among the works it cites.
“Toward idealized decision theory”
Nate Soares and Benja Fallenstein · 2015
Later among the works it cites.
“Long-Term and Short-Term Challenges to Ensuring the Safety of AI Systems” [Online; accessed 13-June-2016], 2015
Jacob Steinhardt · 2015
Later among the works it cites.
“Counterfactual risk minimization: Learning from logged bandit feedback”
Adith Swaminathan and Thorsten Joachims · 2015
Later among the works it cites.
“High-Confidence Off-Policy Evaluation.”
Philip˜S Thomas, Georgios Theocharous and Mohammad Ghavamzadeh · 2015
Later among the works it cites.
“Estimation and Inference of Heterogeneous Treatment Effects using Random Forests”
Stefan Wager and Susan Athey · 2015
Later among the works it cites.
“On-the-job learning with bayesian decision theory”
Keenon Werling, Arun˜Tejasvi Chaganty, Percy˜S Liang and Christopher˜D Manning · 2015
Later among the works it cites.
“Towards ai-complete question answering: A set of prerequisite toy tasks”
Jason Weston, Antoine Bordes, Sumit Chopra and Tomas Mikolov · 2015
Later among the works it cites.
“Understanding neural networks through deep visualization”
Jason Yosinski et al · 2015
Later among the works it cites.
“Learning Fair Classifiers”
Muhammad˜Bilal Zafar et al · 2015
Later among the works it cites.
“Deep Learning with Differential Privacy”, in press (2016)
Martin Abadi et al · 2016
Closest in time.
“Hiring by algorithm: predicting and preventing disparate impact”
Ifeoma Ajunwa, Sorelle Friedler, Carlos˜E Scheidegger and Suresh Venkatasubramanian · 2016
Closest in time.
“The Risk of Automation for Jobs in OECD Countries”
Melanie Arntz, Terry Gregory and Ulrich Zierahn · 2016
Closest in time.
“The AGI Containment Problem”
James Babcock, Janos Kramar and Roman Yampolskiy · 2016
Closest in time.
F Berkenkamp, A Krause and Angela˜P Schoellig · 2016
Closest in time.
“Parametric Bounded Löb’s Theorem and Robust Cooperation of Bounded Agents”, 2016
Andrew Critch · 2016
Closest in time.
“Avoiding wireheading with value reinforcement learning”
Tom Everitt and Marcus Hutter · 2016
Closest in time.
“Self-Modification of Policy and Utility Function in Rational Agents”
Tom Everitt, Daniel Filan, Mayank Daswani and Marcus Hutter · 2016
Closest in time.
“Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization”
Chelsea Finn, Sergey Levine and Pieter Abbeel · 2016
Closest in time.
“Asymptotic Convergence in Online Learning with Unbounded Delays”
Scott Garrabrant, Nate Soares and Jessica Taylor · 2016
Closest in time.
Scott Garrabrant, Benya Fallenstein, Abram Demski and Nate Soares · 2016
Closest in time.
“Trusted Machine Learning for Probabilistic Models”
Shalini Ghosh, Patrick Lincoln, Ashish Tiwari and Jerry Zhu · 2016
Closest in time.
“Cooperative Inverse Reinforcement Learning”
Dylan Hadfield-Menell, Anca Dragan, Pieter Abbeel and Stuart Russell · 2016
Closest in time.
“The Off-Switch”, 2016
Dylan Hadfield-Menell, Anca Dragan, Pieter Abbeel and Stuart Russell · 2016
Closest in time.
“Learning Representations for Counterfactual Inference”
Fredrik˜D Johansson, Uri Shalit and David Sontag · 2016
Closest in time.
“Unanimous Prediction for 100Learning Semantic Parsers”
F. Khani, M. Rinard and P. Liang · 2016
Closest in time.
Tejas˜D Kulkarni, Karthik˜R Narasimhan, Ardavan Saeedi and Joshua˜B Tenenbaum · 2016
Closest in time.
“Discussion of ’Superintelligence: Paths, Dangers, Strategies”’, 2016
Neil Lawrence · 2016
Closest in time.
“Synthesizing the preferred inputs for neurons in neural networks via deep generator networks”
Anh Nguyen et al · 2016
Closest in time.
“Safely Interruptible Agents”, 2016
Laurent Orseau and Stuart Armstrong · 2016
Closest in time.
“Deep Exploration via Bootstrapped DQN”
Ian Osband, Charles Blundell, Alexander Pritzel and Benjamin Van˜Roy · 2016
Closest in time.
“Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples”
Nicolas Papernot et al · 2016
Closest in time.
“Bounding and Minimizing Counterfactual Error”
Uri Shalit, Fredrik Johansson and David Sontag · 2016
Closest in time.
“Mastering the game of Go with deep neural networks and tree search”
David Silver et al · 2016
Closest in time.
“Unsupervised Risk Estimation with only Structural Assumptions”, 2016
Jacob Steinhardt and Percy Liang · 2016
Closest in time.
“Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction”
Jacob Steinhardt, Gregory Valiant and Moses Charikar · 2016
Closest in time.
“Quantilizers: A Safer Alternative to Maximizers for Limited Optimization”
Jessica Taylor · 2016
Closest in time.
“Safe Exploration in Finite Markov Decision Processes with Gaussian Processes”
Matteo Turchetta, Felix Berkenkamp and Andreas Krause · 2016
Closest in time.
“Causal discovery with continuous additive noise models”
Jonas Peters, Joris˜M Mooij, Dominik Janzing and Bernhard Sch“”olkopf · 2053
Closest in time.