Awesome Best Papers

本项目收集了各大顶会的最佳论文（自 2013 年起），包括论文名、论文链接、作者名以及机构名（多个机构并列时记录第一个）。所有的数据都是手工输入的，所以 如果你发现有任何错误，欢迎开个 issue 提醒我。你也可以通过 创建 pull requests 的方式参与到这个项目中来，加入机器人、优化、图形学等本项目未包含领域的顶会最佳论文信息（非常欢迎）。项目中的部分数据参考了 Jeff Huang 的网站，他收集了自 1996 年起计算机科学领域部分顶会的最佳论文，如果你感兴趣，也可以访问他的网站。

This repo collects best papers from various top conferences (since 2013), including paper names and links, author names and organization names (use the first one when the author works for multiple organizations). All the data are collected and entered by hand, so feel free to open an issue if you find anything wrong. You can also contribute to this repo by creating pull requests and add best papers from top conferences in research areas that are not covered in this repo, e.g., robotics, optimization, computer graphics and etc (very welcome). Part of the data is collected by Jeff Huang. If you are interested in more best papers in computer science (since 1996), you can visit his website.

Table of Content

Domain	Conferences
Cross-domain	AAAI, IJCAI, NeurIPS, ICML, ICLR, WWW
Data Mining and Information Retrieval	KDD, SIGIR, CIKM, ICDM, WSDM, RecSys
Computer Vision	CVPR, ICCV, ECCV
Natural Language Processing	ACL, EMNLP, NAACL, EACL

Format

This repo is writen in Markdown. Each entry follows the following format.

| Year | Paper |
| :-:  |  :-   |
| 2020 | **[Paper title](Link to pdf)**<br>AuthorName (Organization); AuthorName (Organization) |

When multiple best papers are available:

| Year | Paper |
| :-:  |  :-   |
| 2020 | 1. **[Paper title](Link to pdf)**<br>AuthorName (Organization); AuthorName (Organization)<br>2. **[Paper title](Link to pdf)**<br>AuthorName (Organization); AuthorName (Organization) |

Deadlines

Thanks to Abhishek Das and Jackie Tseng, the exact dates of AI conferences are available on:

1. AI Conference Deadlines
2. jackietseng.github.io/conference_call_for_paper

To make it easier to find the most recent conferences, the following table sorts each conference by therir deadlines.

Conference	Date	Deadline
CVPR	Late Jun	Mid Jan
IJCAI	Late Aug	Late Jan
SIGIR	Mid Jul	Early Feb
ICML	Late Jul	Early Feb
KDD	Mid Aug	Early Feb
ECCV	Late Aug	Early Mar
ICCV	Mid Oct	Mid Mar
RecSys	Late Sep	Early May
EMNLP	Mid Nov	Mid May
NeurIPS	Early Dec	Late May
CIKM	Early Nov	Late May
ICDM	Early Dec	Mid Jun
ACL	Early Feb	Early Aug
WSDM	Late Feb	Early Aug
AAAI	Late Feb	Early Sep
EACL	Late Apr	Early Oct
ICLR	Late Apr	Early Oct
WWW	Late Apr	Late Oct
NAACL	Early Jun	Late Nov

List of Best Papers

AAAI

The full list of AAAI outstanding papers (including best student papers and their Honorable Mentions) is presented on this website.

Year	Paper
2021	1. Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting Haoyi Zhou (Beihang University); Shanghang Zhang (UC Berkeley); Jieqi Peng (Beihang University); Shuai Zhang (Beihang University); Jianxin Li (Beihang University); Hui Xiong (Rutgers University); Wancai Zhang (Beijing Guowang Fuda Science & Technology Development Company) 2. Exploration-Exploitation in Multi-Agent Learning: Catastrophe Theory Meets Game Theory Stefanos Leonardos (Singapore University of Technology and Design); Georgios Piliouras (Singapore University of Technology and Design) 3. Mitigating Political Bias in Language Models Through Reinforced Calibration Ruibo Liu (Dartmouth College); Chenyan Jia (University of Texas at Austin); Jason Wei (Google AI); Guangxuan Xu (Dartmouth College); Lili Wang (Dartmouth College); Soroush Vosoughi (Dartmouth College)
2020	WinoGrande: An Adversarial Winograd Schema Challenge at Scale Keisuke Sakaguchi (Allen Institute for Artificial Intelligence); Ronan Le Bras (Allen Institute for Artificial Intelligence); Chandra Bhagavatula (Allen Institute for Artificial Intelligence); Yejin Choi (University of Washington)
2019	How to Combine Tree-Search Methods in Reinforcement Learning Yonathan Efroni (Technion); Gal Dala (Technion); Bruno Scherrer (INRIA); Shie Mannor (Technion)
2018	Memory-Augmented Monte Carlo Tree Search Chenjun Xiao (University of Alberta); Jincheng Mei (University of Alberta); Martin Müller (University of Alberta)
2017	Label-Free Supervision of Neural Networks with Physics and Domain Knowledge Russell Stewart (Stanford University); Stefano Ermon (Stanford University)
2016	Bidirectional Search That Is Guaranteed to Meet in the Middle Robert C. Holte (University of Alberta); Ariel Felner (Ben-Gurion University); Guni Sharon (Ben-Gurion University); Nathan R. Sturtevant (University of Denver)
2015	From Non-Negative to General Operator Cost Partitioning Florian Pommerening (University of Basel); Malte Helmert (University of Basel); Gabriele Röger (University of Basel); Jendrik Seipp (University of Basel)
2014	Recovering from Selection Bias in Causal and Statistical Inference Elias Bareinboim (UCLA); Jin Tian (Iowa State University); Judea Pearl (UCLA)
2013	1. HC-Search: Learning Heuristics and Cost Functions for Structured Prediction Janardhan Rao Doppa (Oregon State University); Alan Fern (Oregon State University); Prasad Tadepalli (Oregon State University) 2. SMILe: Shuffled Multiple-Instance Learning Gary Doran (Case Western Reserve University); Soumya Ray (Case Western Reserve University)

IJCAI

IJCAI Distinguished papers.

Year	Paper
2019	Boosting for Comparison-Based Learning Michaël Perrot (Max-Planck-Institute for Intelligent Systems); Ulrike von Luxburg (University of Tubingen)
2018	1. Reasoning about Consensus when Opinions Diffuse through Majority Dynamics Vincenzo Auletta (University of Salerno); Diodato Ferraioli (University of Salerno); Gianluigi Greco (University of Calabria) 2. SentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks Ke Wang (Peking University); Xiaojun Wan (Peking University) 3. From Conjunctive Queries to Instance Queries in Ontology-Mediated Querying Cristina Feier (University of Bremen); Carsten Lutz (University of Bremen); Frank Wolte (University of Liverpool) 4. What game are we playing? End-to-end learning in normal and extensive form games Chun Kai Ling (Carnegie Mellon University); Fei Fang (Carnegie Mellon University); J. Zico Kolter (Carnegie Mellon University) 5. Commonsense Knowledge Aware Conversation Generation with Graph Attention Hao Zhou (Tsinghua University); Tom Young (Beijing Institute of Technology); Minlie Huang (Tsinghua University); Haizhou Zhao, Sogou Inc.; Jingfang Xu, Sogou Inc.; Xiaoyan Zhu (Tsinghua University) 6. R-SVM+: Robust Learning with Privileged Information Xue Li (Wuhan University); Bo Du (Wuhan University); Chang Xu (University of Sydney); Yipeng Zhang (Wuhan University); Lefei Zhang (Wuhan University); Dacheng Tao (University of Sydney) 7. A Degeneracy Framework for Graph Similarity Giannis Nikolentzos, École Polytechnique; Polykarpos Meladianos (Athens University of Economics and Business); Stratis Limnios, École Polytechnique; Michalis Vazirgiannis (École Polytechnique)
2017	Foundations of Declarative Data Analysis Using Limit Datalog Programs Mark Kaminski (University of Oxford); Bernardo Cuenca Grau (University of Oxford); Egor V. Kostylev (University of Oxford); Boris Motik (University of Oxford); Ian Horrocks (University of Oxford)
2016	Hierarchical Finite State Controllers for Generalized Planning Javier Segovia (Universitat Pompeu Fabra); Sergio Jiménez (Universitat Pompeu Fabra); Anders Jonsson (Universitat Pompeu Fabra)
2015	1. Bayesian Active Learning for Posterior Estimation Kirthevasan Kandasamy (Carnegie Mellon University); Jeff Schneider (Carnegie Mellon University); Barnabas Poczos (Carnegie Mellon University) 2. Recursive Decomposition for Nonconvex Optimization Abram L. Friesen (University of Washington); Pedro Domingos (University of Washington)
2013	1. Bayesian Optimization in High Dimensions via Random Embeddings Ziyu Wang (University of British Columbia); Masrour Zoghi (University of Amsterdam); Frank Hutter (Freiberg University); David Matheson (University of British Columbia); Nando de Freitas (University of British Columbia) 2. Flexibility and Decoupling in the Simple Temporal Problem Michel Wilson (Delft University of Technology); Tomas Klos (Delft University of Technology); Cees Witteveen (Delft University of Technology); Bob Huisman (Delft University of Technology)

NeurIPS

Year	Paper
2020	1. No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium Andrea Celli (Politecnico di Milano); Alberto Marchesi (Politecnico di Milano); Gabriele Farina (Carnegie Mellon University); Nicola Gatti (Politecnico di Milano) 2. Improved Guarantees and a Multiple-Descent Curve for Column Subset Selection and the Nyström Method Michal Derezinski (UC Berkeley); Rajiv Khanna (UC Berkeley); Michael W. Mahoney (UC Berkeley) 3. Language Models are Few-Shot Learners Tom B. Brown (OpenAI); Benjamin Mann (OpenAI); Nick Ryder (OpenAI); Melanie Subbiah (OpenAI); Jared D. Kaplan (Johns Hopkins University); Prafulla Dhariwal (OpenAI); Arvind Neelakantan (OpenAI); Pranav Shyam (OpenAI); Girish Sastry (OpenAI); Amanda Askell (OpenAI); Sandhini Agarwal (OpenAI); Ariel Herbert-Voss (OpenAI); Gretchen M. Krueger (OpenAI); Tom Henighan (OpenAI); Rewon Child (OpenAI); Aditya Ramesh (OpenAI); Daniel Ziegler (OpenAI); Jeffrey Wu (OpenAI); Clemens Winter (OpenAI); Chris Hesse (OpenAI); Mark Chen (OpenAI); Eric Sigler (OpenAI); Mateusz Litwin (OpenAI); Scott Gray (OpenAI); Benjamin Chess (OpenAI); Jack Clark (OpenAI); Christopher Berner (OpenAI); Sam McCandlish (OpenAI); Alec Radford (OpenAI); Ilya Sutskever (OpenAI); Dario Amodei (OpenAI)
2019	Distribution-Independent PAC Learning of Halfspaces with Massart Noise Ilias Diakonikolas (University of Wisconsin-Madison); Themis Gouleakis (Max Planck Institute for Informatics)
2018	1. Non-delusional Q-learning and Value-iteration Tyler Lu; Dale Schuurmans; Craig Boutilier 2. Optimal Algorithms for Non-Smooth Distributed Optimization in Networks Kevin Scaman ; Francis Bach ; Sebastien Bubeck ; Laurent Massoulié ; Yin Tat Lee 3. Nearly Tight Sample Complexity Bounds for Learning Mixtures of Gaussians via Sample Compression Schemes Hassan Ashtiani ; Shai Ben-David ; Nick Harvey ; Christopher Liaw ; Abbas Mehrabian ; Yaniv Plan 4. Neural Ordinary Differential Equations Tian Qi Chen ; Yulia Rubanova ; Jesse Bettencourt ; David Duvenaud
2017	1. Safe and Nested Subgame Solving for Imperfect-Information Games Noam Brown (Carnegie Mellon University); Tuomas Sandholm(Carnegie Mellon University) 2. Variance-based Regularization with Convex Objectives Hongseok Namkoong (Stanford University); John Duchi (Stanford University) 3. A Linear-Time Kernel Goodness-of-Fit Test Wittawat Jitkrittum (University College London), Wenkai Xu (University College London), Zoltan Szabo (École Polytechnique), Kenji Fukumizu (The Institute of Statistical Mathematics), Arthur Gretton (University College London)
2016	Value Iteration Networks Aviv Tamar (UC Berkeley); Yi Wu (UC Berkeley); Garrett Thomas (UC Berkeley); Sergey Levine (UC Berkeley); Pieter Abbeel (UC Berkeley)
2015	1. Competitive Distribution Estimation: Why is Good-Turing Good Alon Orlitsky (UC San Diego); Ananda Suresh (UC San Diego) 2. Fast Convergence of Regularized Learning in Games Vasilis Syrgkanis (Microsoft Research); Alekh Agarwal (Microsoft Research); Haipeng Luo (Princeton University); Robert Schapire (Microsoft Research)
2014	1. Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS) Anshumali Shrivastava (Cornell University); Ping Li (Rutgers University) 2. A* Sampling Christopher Maddison (University of Toronto); Daniel Tarlow (Microsoft Research); Tom Minka (Microsoft Research)
2013	1. A memory frontier for complex synapses Subhaneil Lahiri (Stanford University); Surya Ganguli (Stanford University) 2. Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints Rishabh Iyer (University of Washington, Seattle); Jeff Bilmes (University of Washington, Seattle) 3. Scalable Influence Estimation in Continuous-Time Diffusion Networks Nan Du (Georgia Tech); Le Song (Georgia Tech); Manuel Gomez-Rodriguez (MPI for Intelligent Systems); Hongyuan Zha (Georgia Tech)

ICML

ICML Outstanding Papers.

Year	Paper
2021	Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies Paul Vicol (University of Toronto); Luke Metz (Google Brain); Jascha Sohl-Dickstein (Google Brain)
2020	1. On Learning Sets of Symmetric Elements Haggai Maron (NVIDIA Research); Or Litany (Stanford University); Gal Chechik (Stanford University); Ethan Fetaya (Bar Ilan University) 2. Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems Kaixuan Wei (Beijing Institute of Technology); Angelica I Aviles-Rivero (University of Cambridge); Jingwei Liang (University of Cambridge); Ying Fu (Beijing Institute of Technology); Carola-Bibiane Schönlieb (University of Cambridge); Hua Huang (Beijing Institute of Technology)
2019	1. Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations Francesco Locatello (ETH Zurich); Stefan Bauer (MaxPlanck Institute for Intelligent Systems); Mario Lucic (Google Brain); Gunnar Rätsch (Google Brain); Sylvain Gelly (ETH Zurich); Bernhard Schölkopf (MaxPlanck Institute for Intelligent Systems); Olivier Bachem (Google Brain) 2. Rates of Convergence for Sparse Variational Gaussian Process Regression David R. Burt (University of Cambridge); Carl Edward Rasmussen (University of Cambridge); Mark van der Wilk (PROWLER.io)
2018	1. Delayed Impact of Fair Machine Learning Lydia T. Liu (University of California Berkeley); Sarah Dean (University of California Berkeley); Esther Rolf (University of California Berkeley); Max Simchowitz (University of California Berkeley); Moritz Hardt (University of California Berkeley) 2. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye (Massachusetts Institute of Technology); Nicholas Carlini (University of California Berkeley); David Wagner (University of California Berkeley)
2017	Understanding Black-box Predictions via Influence Functions Pang Wei Koh (Stanford University); Percy Liang (Stanford University)
2016	1. Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling Christopher De Sa (Stanford University); Chris Re (Stanford University); Kunle Olukotun (Stanford University) 2. Pixel Recurrent Neural Networks Aaron Van den Oord (Google); Nal Kalchbrenner (Google); Koray Kavukcuoglu (Google) 3. Dueling Network Architectures for Deep Reinforcement Learning Ziyu Wang (Google); Tom Schaul (Google); Matteo Hessel (Google); Hado van Hasselt (Google); Marc Lanctot (Google); Nando de Freitas (University of Oxford)
2015	1. A Nearly-Linear Time Framework for Graph-Structured Sparsity Chinmay Hegde (Massachusetts Institute of Technology); Piotr Indyk (Massachusetts Institute of Technology); Ludwig Schmid (Massachusetts Institute of Technology) 2. Optimal and Adaptive Algorithms for Online Boosting Alina Beygelzimer (Yahoo! Research); Satyen Kale (Yahoo! Research); Haipeng Luo (Princeton University)
2014	Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis Jian Tang (Peking University); Zhaoshi Meng (University of Michigan); XuanLong Nguyen (University of Michigan); Qiaozhu Mei (University of Michigan); Ming Zhang (Peking University)
2013	1. Vanishing Component Analysis Roi Livni (The Hebrew University of Jerusalum); David Lehavi (Hewlett-Packard Labs); Sagi Schein (Hewlett-Packard Labs); Hila Nachlieli (Hewlett-Packard Labs); Shai Shalev Shwartz (The Hebrew University of Jerusalum); Amir Globerson (The Hebrew University of Jerusalum) 2. Fast Semidifferential-based Submodular Function Optimization Rishabh Iyer (University of Washington); Stefanie Jegelka (University of California Berkeley); Jeff Bilmes (University of Washington)

ICLR

ICLR Outstanding Papers.

Year

Paper

2021

1. Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with 1/n Parameters Aston Zhang (Amazon); Yi Tay (Google Research); Shuai Zhang (ETH ZURICH); Alvin Chan (Nanyang Technological University);, Anh Tuan Luu (Nanyang Technological University);, Siu Hui (Nanyang Technological University);, Jie Fu (Mila, Universite de Montreal) 2. Complex Query Answering with Neural Link Predictors Erik Arakelyan (University College London); Daniel Daza (Vrije Universiteit Amsterdam); Pasquale Minervini (University College London); Michael Cochez (Vrije Universiteit Amsterdam) 3. EigenGame: PCA as a Nash Equilibrium Ian Gemp (DeepMind); Brian McWilliams (DeepMind); Claire Vernade (DeepMind); Thore Graepel (DeepMind) 4. Learning Mesh-Based Simulation with Graph Networks Tobias Pfaff (DeepMind); Meire Fortunato (DeepMind); Alvaro Sanchez-Gonzalez (DeepMind); Peter Battaglia (DeepMind) 5. Neural Synthesis of Binaural Speech from Mono Audio Alexander Richard (Facebook); Dejan Markovic (Facebook); Israel D. Gebru (Facebook); Steven Krenn (Facebook); Gladstone Alexander Butler (Facebook); Fernando Torre (Facebook); Yaser Sheikh (Facebook) 6. Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime Atsushi Nitanda (The University of Tokyo); Taiji Suzuki (The University of Tokyo) 7. Rethinking Architecture Selection in Differentiable NAS Ruochen Wang (UCLA); Minhao Cheng (UCLA); Xiangning Chen (UCLA); Xiaocheng Tang (DiDi); Cho-Jui Hsieh (UCLA) 8. Score-Based Generative Modeling through Stochastic Differential Equations Yang Song (Stanford University); Jascha Sohl-Dickstein (Google Brain); Diederik P Kingma (Google Brain); Abhishek Kumar (Google Brain); Stefano Ermon (Stanford University); Ben Poole (Google Brain)

WWW

The Web Conference.

Year	Paper
2021	Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach Ashish Sharma (University of Washington); Inna Lin (University of Washington); Adam Miner (Stanford University); David Atkins (University of Washington); Tim Althoff (University of Washington)
2020	Open Intent Extraction from Natural Language Interactions Nikhita Vedula (The Ohio State University); Nedim Lipka (Adobe); Pranav Maneriker (The Ohio State University); Srinivasan Parthasarathy (The Ohio State University)
2019	1. Ermes: Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification Zhenpeng Chen (Peking University); Sheng Shen (University of California, Berkeley); Ziniu Hu (University of California, Berkeley); Xuan Lu (Peking University); Qiaozhu Mei (University of Michigan); Xuanzhe Liu (Peking University) 2. OUTGUARD: Detecting In-Browser Covert Cryptocurrency Mining in the Wild Amin Kharraz (University of Illinois at Urbana-Champaign); Zane Ma (University of Illinois at Urbana-Champaign); Paul Murley (University of Illinois at Urbana-Champaign); Charles Lever (Georgia Institute of Technology); Joshua Mason (University of Illinois at Urbana-Champaign); Andrew Miller (University of Illinois at Urbana-Champaign); Nikita Borisov (University of Illinois at Urbana-Champaign); Manos Antonakakis (Georgia Institute of Technology); Michael Bailey (University of Illinois at Urbana-Champaign)
2018	HighLife: Higher-arity Fact Harvesting Patrick Ernst (Saarland Informatics Campus in Saarbrücken); Amy Siu (Saarland Informatics Campus in Saarbrücken); Gerhard Weikum (Saarland Informatics Campus in Saarbrücken)
2017	Currently missing.
2016	Social Networks Under Stress Daniel Romero (University of Michigan); Brian Uzzi (Northwestern University); Jon Kleinberg (Cornell University)
2015	HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web Philipp Singer (GESIS - Leibniz Institute for the Social Sciences); Denis Helic (Graz University of Technology); Andreas Hotho, University of Würzburg; Markus Strohmaier (GESIS - Leibniz Institute for the Social Sciences）
2014	Efficient Estimation for High Similarities using Odd Sketches Michael Mitzenmacher (Harvard University); Rasmus Pagh (IT University of Copenhagen); Ninh Pham (IT University of Copenhagen)
2013	No Country for Old Members: User Lifecycle and Linguistic Change in Online Communities Cristian Danescu-Niculescu-Mizil (Stanford University); Robert West (Stanford University); Dan Jurafsky (Stanford University); Jure Leskovec (Stanford University); Christopher Potts (Stanford University)

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KDD

KDD has two paper tracks, i.e., the Research Track and the Applied Data Science Track. I only report the best papers for the research track in this repo but the ADS track is remarkable as well.

Year	Paper
2020	On Sampled Metrics for Item Recommendation Walid Krichene (Google Research); Steffen Rendle (Google Research)
2019	Network Density of States Kun Dong (Cornell University); Austin Benson (Cornell University); David Bindel (Cornell University)
2018	Adversarial Attacks on Neural Networks for Graph Data Daniel Zügner (Technical University of Munich); Amir Akbarnejad (Technical University of Munich); Stephan Günnemann (Technical University of Munich)
2017	Accelerating Innovation Through Analogy Mining Tom Hope (Hebrew University of Jerusalem); Joel Chan (Carnegie Mellon University); Aniket Kittur (Carnegie Mellon University); Dafna Shahaf (Hebrew University of Jerusalem)
2016	FRAUDAR: Bounding Graph Fraud in the Face of Camouflage Bryan Hooi (Carnegie Mellon University); Hyun Ah Song (Carnegie Mellon University); Alex Beutel (Carnegie Mellon University); Neil Shah (Carnegie Mellon University); Kijung Shin (Carnegie Mellon University); Christos Faloutsos (Carnegie Mellon University)
2015	Efficient Algorithms for Public-Private Social Networks Flavio Chierichetti (Sapienza University of Rome); Alessandro Epasto (Brown University); Ravi Kumar (Google); Silvio Lattanzi (Google); Vahab Mirrokni (Google)
2014	Reducing the Sampling Complexity of Topic Models Aaron Li (Carnegie Mellon University); Amr Ahmed (Google); Sujith Ravi (Google); Alexander Smola (Carnegie Mellon University)
2013	Simple and Deterministic Matrix Sketching Edo Liberty (Yahoo! Research)

SIGIR

The full list of SIGIR best papers is presented on this website.

Year	Paper
2021	Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness Harrie Oosterhuis (Radboud University)
2020	Controlling Fairness and Bias in Dynamic Learning-to-Rank Marco Morik (Technische Univerität Berlin); Ashudeep Singh (Cornell University); Jessica Hong (Cornell University); Thorsten Joachims (Cornell University)
2019	Variance Reduction in Gradient Exploration for Online Learning to Rank Huazheng Wang (University of Virginia); Sonwoo Kim (University of Virginia); Eric McCord-Snook (University of Virginia); Qingyun Wu (University of Virginia); Hongning Wang (University of Virginia)
2018	Should I Follow the Crowd? A Probabilistic Analysis of the Effectiveness of Popularity in Recommender Systems Rocío Cañamares (Universidad Autónoma de Madrid); Pablo Castells (Universidad Autónoma de Madrid)
2017	BitFunnel: Revisiting Signatures for Search Bob Goodwin (Microsoft); Michael Hopcroft (Microsoft); Dan Luu (Microsoft); Alex Clemmer (Heptio); Mihaela Curmei (Microsoft); Sameh Elnikety (Microsoft); Yuxiong He (Microsoft)
2016	Understanding Information Need: An fMRI Study Yashar Moshfeghi (University of Glasgow); Peter Triantafillou (University of Glasgow); Frank E. Pollick (University of Glasgow)
2015	QuickScorer: A Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees Claudio Lucchese (Istituto di Scienza e Tecnologie dell'Informazione); Franco Maria Nardini (Istituto di Scienza e Tecnologie dell'Informazione); Salvatore Orlando, Università di Venezia; Raffaele Perego (Istituto di Scienza e Tecnologie dell'Informazione); Nicola Tonellotto (Istituto di Scienza e Tecnologie dell'Informazione); Rossano Venturini (Istituto di Scienza e Tecnologie dell'Informazione)
2014	Partitioned Elias-Fano Indexes Giuseppe Ottaviano (Istituto di Scienza e Tecnologie dell'Informazione); Rossano Venturini (Università di Pisa)
2013	Beliefs and Biases in Web Search Ryen W. White (Microsoft Research)

CIKM

Year	Paper
2020	1. Do People and Neural Networks Pay Attention to the Same Words? Studying Eye-tracking Data for Non-factoid QA Evaluation Valeriya Bolotova-Baranova (RMIT University); Vladislav Blinov (Ural Federal University); Yukun Zheng (Tsinghua University); Mark Sanderson (University of Massachusetts Amherst); Falk Scholer (RMIT University); Bruce Croft (RMIT University) 2. FANG: Leveraging Social Context for Fake News Detection Using Graph Representation Van-Hoang Nguyen (National University of Singapore); Kazunari Sugiyama (Kyoto University); Preslav Nakov (Qatar Computing Research Institute; HBKU); Min-Yen Kan (National University of Singapore)
2019	AutoGRD: Model Recommendation Through Graphical Dataset Representation Noy Cohen-Shapira (Ben Gurion University of the Negev); Lior Rokach (Ben Gurion University of the Negev); Bracha Shapira (Ben Gurion University of the Negev); Gilad Katz (Ben Gurion University of the Negev); Roman Vainshtein (Ben Gurion University of the Negev)
2018	Relevance estimation with multiple information sources on search engine result pages Junqi Zhang (Tsinghua University); Yiqun Liu (Tsinghua University); Shaoping Ma (Tsinghua University); Qi Tian (University of Texas at San Antonio)
2017	Hike: A Hybrid Human-Machine Method for Entity Alignment in Large-Scale Knowledge Bases Yan Zhuang (Tsinghua University); Guoliang Li (Tsinghua University); Zhuojian Zhong (Tsinghua University); Jianhua Feng (Tsinghua University)
2016	Vandalism Detection in Wikidata Stefan Heindorf (Paderborn University); Martin Potthast (Bauhaus-Universität Weimar); Benno Stein (Bauhaus-Universität Weimar); Gregor Engels (Paderborn University)
2015	Assessing the Impact of Syntactic and Semantic Structures for Answer Passages Reranking Kateryna Tymoshenko (University of Trento); Alessandro Moschitti (Qatar Computing Research Institute)
2014	Cross-Device Search George Montanez (Carnegie Mellon University); Ryen White (Microsoft Research); Xiao Huang (Microsoft)
2013	Penguins in Sweaters, or Serendipitous Entity Search on User-generated Content Ilaria Bordino (Yahoo! Research); Yelena Mejova (Yahoo! Research); Mounia Lalmas (Yahoo! Research)

ICDM

The full list of ICDM best papers and best student papers is presented on this website.

Year	Paper
2020	Co-Embedding Network Nodes and Hierarchical Labels with Taxonomy Based Generative Adversarial Networks Carl Yang (University of Illinois, Urbana Champaign); Jieyu Zhang (University of Illinois, Urbana Champaign); Jiawei Han (University of Illinois, Urbana Champaign)
2019	Deep Multi-attributed Graph Translation with Node-Edge Co-evolution Xiaojie Guo (George Mason University); Liang Zhao (George Mason University), Cameron Nowzari (George Mason University); Setareh Rafatirad (George Mason University); Houman Homayoun (George Mason University); Sai Manoj Pudukotai Dinakarrao (George Mason University)
2018	Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms Panagiotis Mandros (Max Planck Institute for Informatics); Mario Boley (Max Planck Institute for Informatics); Jilles Vreeken (Max Planck Institute for Informatics)
2017	TensorCast: Forecasting with Context using Coupled Tensors Miguel Ramos de Araujo (Carnegie Mellon University); Pedro Manuel Pinto Ribeiro (University of Porto); Christos Faloutsos (Carnegie Mellon University)
2016	KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift Viktor Losing (Bielefeld University); Barbara Hammer (Bielefeld University); Heiko Wersing (HONDA Research Institute Europe)
2015	Diamond Sampling for Approximate Maximum All-pairs Dot-product (MAD) Search Grey Ballard (Sandia National Laboratories); Seshadhri Comandur (University of California, Santa Cruz); Tamara Kolda (Sandia National Laboratories); Ali Pinar (Sandia National Laboratories)
2014	Ternary Matrix Factorization Sam Maurus (Technische Universität München); Claudia Plant (Technische Universität München)
2013	Reconstructing Individual Mobility from Smart Card Transactions: A Space Alignment Approach Nicholas Jing Yuan (Microsoft Research); Yingzi Wang (University of Science and Technology of China); Fuzheng Zhang (University of Science and Technology of China); Xing Xie (Microsoft Research); Guang-Zhong Sun (University of Science and Technology of China)

WSDM

Year	Paper
2020	The Power of Pivoting for Exact Clique Counting Shweta Jain (University of California, Santa Cruz); C. Seshadhri (University of California, Santa Cruz)
2019	Slice: Scalable Linear Extreme Classifiers trained on 100 Million Labels for Related Searches Himanshu Jain (Indian Institute of Technology Delhi); Venkatesh Balasubramanian (Microsoft AI & Research); Bhanu Chunduri (Microsoft AI & Research); Manik Varma (Microsoft AI & Research)
2018	Index Compression Using Byte-Aligned ANS Coding and Two-Dimensional Contexts Alistair Moffat (The University of Melbourne); Matthias Petri (The University of Melbourne)
2017	Unbiased Learning-to-Rank with Biased Feedback Thorsten Joachims (Cornell University); Adith Swaminathan (Cornell University); Tobias Schnabel (Cornell University)
2016	Beyond Ranking: Optimizing Whole-Page Presentation Yue Wang (University of Michigan); Dawei Yin (Yahoo Labs); Luo Jie (Snapchat); Pengyuan Wang (Yahoo Labs); Makoto Yamada (Yahoo Labs); Yi Chang (Yahoo Labs); Qiaozhu Mei (University of Michigan)
2015	Inverting a Steady-State Ravi Kumar (Google); Andrew Tomkins (Google); Sergei Vassilvitskii (Google); Erik Vee (Google)
2014	Scalable Hierarchical Multitask Learning Algorithms for Conversion Optimization in Display Advertising Amr Ahmed (Google); Abhimanyu Das (Microsoft Research); Alex J. Smola (Carnegie Mellon University)
2013	Optimized Interleaving for Online Retrieval Evaluation Filip Radlinski (Microsoft); Nick Craswell (Microsoft)

RecSys

Year	Paper
2020	Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations Hongyan Tang (Tencent PCG); Junning Liu (Tencent PCG); Ming Zhao (Tencent PCG); Xudong Gong (Tencent PCG)
2019	Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches Maurizio Ferrari Dacrema (Politecnico di Milano); Paolo Cremonesi (Politecnico di Milano); Dietmar Jannach (University of Klagenfurt)
2018	Causal Embeddings for Recommendation Stephen Bonner (Criteo AI Labs); Flavian Vasile (Criteo AI Labs)
2017	Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and Recommendations Xiaoying Zhang (The Chinese University of Hong Kong); Junzhou Zhao (The Chinese University of Hong Kong); John C.S. Lui (The Chinese University of Hong Kong)
2016	Local Item-Item Models for Top-N Recommendation Evangelia Christakopoulou (University of Minnesota); George Karypis (University of Minnesota)
2015	Context-Aware Event Recommendation in Event-based Social Networks Augusto Q. Macedo (Federal University of Campina Grande); Leandro B. Marinho (Federal University of Campina Grande); Rodrygo L.T. Santos (Federal University of Campina Grande)
2014	Beyond Clicks: Dwell Time for Personalization Xing Yi (Yahoo Labs); Liangjie Hong (Yahoo Labs); Erheng Zhong (Yahoo Labs); Nathan Nan Liu (Yahoo Labs); Suju Rajan (Yahoo Labs)
2013	A Fast Parallel SGD for Matrix Factorization in Shared Memory Systems Yong Zhuang (National Taiwan University); Wei-Sheng Chin (National Taiwan University); Yu-Chih Juan (National Taiwan University); Chih-Jen Lin (National Taiwan University)

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CVPR

The full list of CVPR best papers is presented on this website.

Year	Paper
2021	GIRAFFE: Representing Scenes As Compositional Generative Neural Feature Fields Michael Niemeyer (Max Planck Institute for Intelligent Systems); Andreas Geiger (University of Tubingen)
2020	Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild Shangzhe Wu (University of Oxford); Christian Rupprecht (University of Oxford); Andrea Vedaldi (Oxford University)
2019	A theory of Fermat Paths for Non-Line-of-Sight Shape Reconstruction Shumian Xin (Carnegie Mellon University); Sotiris Nousias (University of Toronto); Kiriakos N. Kutulakos (University of Toronto); Aswin C. Sankaranarayanan (Carnegie Mellon University); Srinivasa G. Narasimhan (Carnegie Mellon University); Ioannis Gkioulekas (Carnegie Mellon University)
2018	Taskonomy: Disentangling Task Transfer Learning Amir R. Zamir (Stanford University); Alexander Sax (Stanford University); William Shen (Stanford University); Leonidas Guibas (Stanford University); Jitendra Malik (University of California Berkeley); Silvio Savarese (Stanford University)
2017	1. Densely Connected Convolutional Networks Zhuang Liu (Tsinghua University); Gao Huang (Cornell University); Laurens van der Maaten (Facebook AI Research); Kilian Q. Weinberger (Cornell University) 2. Learning from Simulated and Unsupervised Images through Adversarial Training Ashish Shrivastava (Apple Inc.); Tomas Pfister (Apple Inc.); Oncel Tuzel (Apple Inc.); Josh Susskind (Apple Inc.); Wenda Wang (Apple Inc.); Russ Webb (Apple Inc.)
2016	Deep Residual Learning for Image Recognition Kaiming He (Microsoft Research); Xiangyu Zhang (Microsoft Research); Shaoqing Ren (Microsoft Research); Jian Sun (Microsoft Research)
2015	DynamicFusion: Reconstruction and Tracking of Non-rigid Scenes in Real-Time Richard A. Newcombe (University of Washington); Dieter Fox (University of Washington); Steven M. Seitz (University of Washington)
2014	What Camera Motion Reveals About Shape with Unknown BRDF Manmohan Chandraker (NEC Labs America)
2013	Fast, Accurate Detection of 100,000 Object Classes on a Single Machine Thomas Dean (Google); Mark A. Ruzon (Google); Mark Segal (Google); Jonathon Shlens (Google); Sudheendra Vijayanarasimhan (Google); Jay Yagnik (Google)

ICCV

The ICCV Best Paper Award is also called the Marr Prize, named after British neuroscientist David Marr. The full list of ICCV best papers is presented on this website.

Year	Paper
2019	SinGAN: Learning a Generative Model from a Single Natural Image Tamar Rott Shaham (Israel Institute of Technology); Tomer Michaeli (Israel Institute of Technology); Tali Dekel (Google Research)
2017	Mask R-CNN Kaiming He (Facebook AI Research); Georgia Gkioxari (Facebook AI Research); Piotr Dollar (Facebook AI Research); Ross Girshick (Facebook AI Research)
2015	Deep Neural Decision Forests Peter Kontschieder (Microsoft Research); Madalina Fiterau (Carnegie Mellon University); Antonio Criminisi (Microsoft Research); Samuel Rota Bulò (Microsoft Research)
2013	From Large Scale Image Categorization to Entry-Level Categories Vicente Ordonez (University of North Carolina at Chapel Hill); Jia Deng (Stanford University); Yejin Choi (Stony Brook University); Alexander Berg (University of North Carolina at Chapel Hill); Tamara Berg (University of North Carolina at Chapel Hill)

ECCV

The full list of ECCV best papers is presented on this website.

Year	Paper
2020	RAFT: Recurrent All-Pairs Field Transforms for Optical Flow Zachary Teed (Princeton University); Jia Deng (Princeton University)
2018	Augmented Autoencoders: Implicit 3D Orientation Learning for 6D Object Detection from RGB Images Martin Sundermeyer (German Aerospace Center); Zoltan-Csaba Marton (German Aerospace Center); Maximilian Durner (German Aerospace Center); Rudolph Triebel (German Aerospace Center)
2016	Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera Hanme Kim (Imperial College London); Stefan Leutenegger (Imperial College London); Andrew J. Davison (Imperial College London)
2014	1. Large-Scale Object Classification using Label Relation Graphs Jia Deng (University of Michigan); Nan Ding (Google); Yangqing Jia (Google); Andrea Frome (Google); Kevin Murphy (Google); Samy Bengio (Google); Yuan Li (Google); Hartmut Neven (Google); Hartwig Adam (Google) 2. Scene Chronology Kevin Matzen (Cornell University); Noah Snavely (Cornell University)

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ACL

The full list of ACL best papers is presented on this website.

Year	Paper
2020	Beyond Accuracy: Behavioral Testing of NLP Models with CheckList Marco Tulio Ribeiro (Microsoft Research), Tongshuang Wu (University of Washington), Carlos Guestrin (University of Washington), Sameer Singh (University of Washington)
2019	Bridging the Gap between Training and Inference for Neural Machine Translation Wen Zhang (University of Chinese Academy of Sciences); Yang Feng (University of Chinese Academy of Sciences); Fandong Meng (WeChat AI); Di You (Worcester Polytechnic Institute); Qun Liu (Huawei Noah’s Ark Lab)
2018	Finding syntax in human encephalography with beam search John Hale (Cornell University); Chris Dyer (DeepMind); Adhiguna Kuncoro (University of Oxford); Jonathan R. Brennan (University of Michigan)
2017	Probabilistic Typology: Deep Generative Models of Vowel Inventories Ryan Cotterell (Johns Hopkins University); Jason Eisner (Johns Hopkins University)
2016	Finding Non-Arbitrary Form-Meaning Systematicity Using String-Metric Learning for Kernel Regression E. Darío Gutiérrez (University of California Berkeley); Roger Levy (Massachusetts Institute of Technology); Benjamin K. Bergen (University of California San Diego)
2015	1. Improving Evaluation of Machine Translation Quality Estimation Yvette Graham (Trinity College Dublin) 2. Learning Dynamic Feature Selection for Fast Sequential Prediction Emma Strubell (University of Massachusetts Amherst); Luke Vilnis (University of Massachusetts Amherst); Kate Silverstein (University of Massachusetts Amherst); Andrew McCallum (University of Massachusetts Amherst)
2014	Fast and Robust Neural Network Joint Models for Statistical Machine Translation Jacob Devlin (Raytheon BBN Technologies); Rabih Zbib (Raytheon BBN Technologies); Zhongqiang Huang (Raytheon BBN Technologies); Thomas Lamar (Raytheon BBN Technologies); Richard Schwartz (Raytheon BBN Technologies); John Makhoul (Raytheon BBN Technologies)
2013	Grounded Language Learning from Video Described with Sentences Haonan Yu (Purdue University); Jeffrey Mark Siskind (Purdue University)

EMNLP

The full list of EMNLP best papers is presented on this website.

Year	Paper
2020	Digital voicing of Silent Speech David Gaddy (University of California, Berkeley); Dan Klein (University of California, Berkeley)
2019	Specializing Word Embeddings (for Parsing) by Information Bottleneck Xiang Lisa Li (Johns Hopkins University); Jason Eisner (Johns Hopkins University)
2018	Linguistically-Informed Self-Attention for Semantic Role Labeling Emma Strubell (University of Massachusetts Amherst); Patrick Verga (University of Massachusetts Amherst); Daniel Andor (Google AI Language); David Weiss (Google AI Language); Andrew McCallum (University of Massachusetts Amherst)
2017	1. Depression and Self-Harm Risk Assessment in Online Forums Andrew Yates (Max Planck Institute for Informatics); Arman Cohan (Georgetown University); Nazli Goharian (Georgetown University) 2. Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints Jieyu Zhao (University of Virginia); Tianlu Wang (University of Virginia); Mark Yatskar (University of Washington); Vicente Ordonez (University of Virginia); Kai-Wei Chang (University of Virginia)
2016	1. Global Neural CCG Parsing with Optimality Guarantees Kenton Lee (University of Washington); Mike Lewis (University of Washington); Luke Zettlemoyer (University of Washington) 2. Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning Karthik Narasimhan (Massachusetts Institute of Technology); Adam Yala (Massachusetts Institute of Technology); Regina Barzilay (Massachusetts Institute of Technology)
2015	1. Broad-coverage CCG Semantic Parsing with AMR Yoav Artzi (Cornell University); Kenton Lee (University of Washington); Luke Zettlemoyer (University of Washington) 2. Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems Tsung-Hsien Wen (Cambridge University); Milica Gasic (Cambridge University); Nikola Mrkši´c (Cambridge University); Pei-Hao Su (Cambridge University); David Vandyke (Cambridge University); Steve Young (Cambridge University)
2014	Modeling Biological Processes for Reading Comprehension Vivek Srikumar (Stanford University); Pei-Chun Chen (Stanford University); Abby Vander Linden (Stanford University); Brittany Harding (Stanford University); Brad Huang (Stanford University); Peter Clark (Stanford University); Christopher D. Manning (Stanford University)
2013	Breaking Out of Local Optima with Count Transforms and Model Recombination: A Study in Grammar Induction Valentin Spitkovsky (Stanford University); Hiyan Alshawi (Stanford University); Daniel Jurafsky (Stanford University)

NAACL

The full list of NAACL best papers is presented on this website.

Year	Paper
2019	BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Jacob Devlin (Google AI Language); Ming-Wei Chang (Google AI Language); Kenton Lee (Google AI Language); Kristina Toutanova (Google AI Language)
2018	Deep contextualized word representations Matthew E. Peters (Allen Institute for Artificial Intelligence); Mark Neumann (Allen Institute for Artificial Intelligence); Mohit Iyyer (Allen Institute for Artificial Intelligence); Matt Gardner (Allen Institute for Artificial Intelligence); Christopher Clark (University of Washington); Kenton Lee (University of Washington); Luke Zettlemoyer (Allen Institute for Artificial Intelligence)
2016	1. Feuding Families and Former Friends; Unsupervised Learning for Dynamic Fictional Relationships Mohit Iyyer (University of Maryland); Anupam Guha (University of Maryland); Snigdha Chaturvedi (University of Maryland); Jordan Boyd-Graber (University of Colorado); Hal Daumé III (University of Maryland) 2. Learning to Compose Neural Networks for Question Answering Jacob Andreas (University of California, Berkeley); Marcus Rohrbach (University of California, Berkeley); Trevor Darrell (University of California, Berkeley); Dan Klein (University of California, Berkeley)
2015	Unsupervised Morphology Induction Using Word Embeddings Radu Soricut (Google); Franz Och (Human Longevity)

EACL

Year	Paper
2021	1. Error Analysis and the Role of Morphology Marcel Bollmann (University of Copenhagen); Anders Søgaard (University of Copenhagen) 2. Is Supervised Syntactic Parsing Beneficial for Language Understanding Tasks? An Empirical Investigation Goran Glavaš (University of Mannheim); Ivan Vulić (University of Cambridge)