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Collect awesome best papers from top AI conferences.

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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 |


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

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

When multiple best papers are available:

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


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

  1. AI Conference Deadlines

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


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 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) |


| 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 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 ( | | 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 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)|


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) |


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) |


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) |


| 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) |


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) |


| 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) |


| 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) |


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) |


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) |


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) |


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) |


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) |


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) |


| 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) |

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