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ORCIDDavid A. McAllester
Person information
- affiliation: Toyota Technological Institute at Chicago, USA
- affiliation: AT&T Labs Research, Florham Park, USA
- affiliation: Massachusetts Institute of Technology (MIT), Artificial Intelligence Lab
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2010 – 2019
- 2019
[c96]Hai Wang, Dian Yu, Kai Sun, Jianshu Chen, Dong Yu, David A. McAllester, Dan Roth:
Evidence Sentence Extraction for Machine Reading Comprehension. CoNLL 2019: 696-707- [i14]Hai Wang, Dian Yu, Kai Sun, Jianshu Chen, Dong Yu, Dan Roth, David A. McAllester:
Evidence Sentence Extraction for Machine Reading Comprehension. CoRR abs/1902.08852 (2019) - 2017
- [c95]Zewei Chu, Hai Wang, Kevin Gimpel, David A. McAllester:
Broad Context Language Modeling as Reading Comprehension. EACL (2) 2017: 52-57 - [c94]Hai Wang, Takeshi Onishi, Kevin Gimpel, David A. McAllester:
Emergent Predication Structure in Hidden State Vectors of Neural Readers. Rep4NLP@ACL 2017: 26-36 - 2016
- [c93]Takeshi Onishi, Hai Wang, Mohit Bansal, Kevin Gimpel, David A. McAllester:
Who did What: A Large-Scale Person-Centered Cloze Dataset. EMNLP 2016: 2230-2235 - [i13]Takeshi Onishi, Hai Wang, Mohit Bansal, Kevin Gimpel, David A. McAllester:
Who did What: A Large-Scale Person-Centered Cloze Dataset. CoRR abs/1608.05457 (2016) - [i12]Zewei Chu, Hai Wang, Kevin Gimpel, David A. McAllester:
Broad Context Language Modeling as Reading Comprehension. CoRR abs/1610.08431 (2016) - [i11]Hai Wang, Takeshi Onishi, Kevin Gimpel, David A. McAllester:
Emergent Logical Structure in Vector Representations of Neural Readers. CoRR abs/1611.07954 (2016) - 2015
- [c92]Hai Wang, Mohit Bansal, Kevin Gimpel, David A. McAllester:
Machine Comprehension with Syntax, Frames, and Semantics. ACL (2) 2015: 700-706 - 2014
- [c91]Koichiro Yamaguchi, David A. McAllester, Raquel Urtasun:
Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV (5) 2014: 756-771 - [c90]Shubhendu Trivedi, David A. McAllester, Greg Shakhnarovich:
Discriminative Metric Learning by Neighborhood Gerrymandering. NIPS 2014: 3392-3400 - [i10]David A. McAllester:
Implementation and Abstraction in Mathematics. CoRR abs/1407.7274 (2014) - 2013
- [j28]Pedro F. Felzenszwalb, Ross B. Girshick, David A. McAllester, Deva Ramanan:
Visual object detection with deformable part models. Commun. ACM 56(9): 97-105 (2013) - [c89]Koichiro Yamaguchi, David A. McAllester, Raquel Urtasun:
Robust Monocular Epipolar Flow Estimation. CVPR 2013: 1862-1869 - [i9]David A. McAllester, Satinder Singh:
Approximate Planning for Factored POMDPs using Belief State Simplification. CoRR abs/1301.6719 (2013) - [i8]David A. McAllester:
A PAC-Bayesian Tutorial with A Dropout Bound. CoRR abs/1307.2118 (2013) - 2012
- [j27]Hossein Tehrani Niknejad, Akihiro Takeuchi, Seiichi Mita, David A. McAllester:
On-Road Multivehicle Tracking Using Deformable Object Model and Particle Filter With Improved Likelihood Estimation. IEEE Trans. Intell. Transp. Syst. 13(2): 748-758 (2012) - [j26]Chunzhao Guo, Seiichi Mita, David A. McAllester:
Robust Road Detection and Tracking in Challenging Scenarios Based on Markov Random Fields With Unsupervised Learning. IEEE Trans. Intell. Transp. Syst. 13(3): 1338-1354 (2012) - [c88]Koichiro Yamaguchi, Tamir Hazan, David A. McAllester, Raquel Urtasun:
Continuous Markov Random Fields for Robust Stereo Estimation. ECCV (5) 2012: 45-58 - [i7]Koichiro Yamaguchi, Tamir Hazan, David A. McAllester, Raquel Urtasun:
Continuous Markov Random Fields for Robust Stereo Estimation. CoRR abs/1204.1393 (2012) - [i6]David A. McAllester, Michael Collins, Fernando Pereira:
Case-Factor Diagrams for Structured Probabilistic Modeling. CoRR abs/1207.4135 (2012) - [i5]David A. McAllester, Petri Myllymäki:
Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (2008). CoRR abs/1208.5154 (2012) - 2011
- [c87]Joseph Keshet
, David A. McAllester, Tamir Hazan:
PAC-Bayesian approach for minimization of phoneme error rate. ICASSP 2011: 2224-2227 - [c86]Jian Peng, Tamir Hazan, David A. McAllester, Raquel Urtasun:
Convex Max-Product over Compact Sets for Protein Folding. ICML 2011: 729-736 - [c85]Chunzhao Guo, Seiichi Mita, David A. McAllester:
Adaptive non-planar road detection and tracking in challenging environments using segmentation-based Markov Random Field. ICRA 2011: 1172-1179 - [c84]Joseph Keshet, Chih-Chieh Cheng, Mark Stoehr, David A. McAllester:
Direct Error Rate Minimization of Hidden Markov Models. INTERSPEECH 2011: 449-452 - [c83]Hossein Tehrani Niknejad, Koji Takahashi, Seiichi Mita, David A. McAllester:
Vehicle detection and tracking at nighttime for urban autonomous driving. IROS 2011: 4442-4447 - [c82]Hossein Tehrani Niknejad, Seiichi Mita, David A. McAllester, Takashi Naito:
Vision-based vehicle detection for nighttime with discriminately trained mixture of weighted deformable part models. ITSC 2011: 1560-1565 - [c81]Chunzhao Guo, Seiichi Mita, David A. McAllester:
Hierarchical road understanding for intelligent vehicles based on sensor fusion. ITSC 2011: 1672-1679 - [c80]Chunzhao Guo, Wataru Sato, Long Han, Seiichi Mita, David A. McAllester:
Graph-based 2D road representation of 3D point clouds for intelligent vehicles. Intelligent Vehicles Symposium 2011: 715-721 - [c79]Hossein Tehrani Niknejad, Koji Takahashi, Seiichi Mita, David A. McAllester:
Embedded multi-sensors objects detection and tracking for urban autonomous driving. Intelligent Vehicles Symposium 2011: 1128-1135 - [c78]David A. McAllester:
Generalization bounds and consistency for latent-structural probit and ramp loss. MLSLP 2011 - [c77]Ross B. Girshick, Pedro F. Felzenszwalb, David A. McAllester:
Object Detection with Grammar Models. NIPS 2011: 442-450 - [c76]David A. McAllester, Joseph Keshet:
Generalization Bounds and Consistency for Latent Structural Probit and Ramp Loss. NIPS 2011: 2205-2212 - [i4]János A. Csirik, Michael L. Littman, David A. McAllester, Robert E. Schapire, Peter Stone:
Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions. CoRR abs/1106.5270 (2011) - [i3]Pedro F. Felzenszwalb, David A. McAllester:
The Generalized A* Architecture. CoRR abs/1110.2216 (2011) - 2010
- [j25]Pedro F. Felzenszwalb, Ross B. Girshick, David A. McAllester, Deva Ramanan:
Object Detection with Discriminatively Trained Part-Based Models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9): 1627-1645 (2010) - [j24]Prahladh Harsha, Rahul Jain, David A. McAllester, Jaikumar Radhakrishnan:
The communication complexity of correlation. IEEE Trans. Inf. Theory 56(1): 438-449 (2010) - [c75]Hoang Trinh, David A. McAllester:
Structure and motion from road-driving stereo sequences. CVPR Workshops 2010: 9-16 - [c74]Pedro F. Felzenszwalb, Ross B. Girshick, David A. McAllester:
Cascade object detection with deformable part models. CVPR 2010: 2241-2248 - [c73]Pedro F. Felzenszwalb, Ross B. Girshick, David A. McAllester, Deva Ramanan:
Discriminative Latent Variable Models for Object Detection. ICML 2010: 11-12 - [c72]Chunzhao Guo, Seiichi Mita, David A. McAllester:
Lane detection and tracking in challenging environments based on a weighted graph and integrated cues. IROS 2010: 5543-5550 - [c71]Chunzhao Guo, Seiichi Mita, David A. McAllester:
A vision system for autonomous vehicle navigation in challenging traffic scenes using integrated cues. ITSC 2010: 1697-1704 - [c70]Chunzhao Guo, Seiichi Mita, David A. McAllester:
MRF-based road detection with unsupervised learning for autonomous driving in changing environments. Intelligent Vehicles Symposium 2010: 361-368 - [c69]Akihiro Takeuchi, Seiichi Mita, David A. McAllester:
On-road vehicle tracking using deformable object model and particle filter with integrated likelihoods. Intelligent Vehicles Symposium 2010: 1014-1021 - [c68]David A. McAllester, Tamir Hazan, Joseph Keshet:
Direct Loss Minimization for Structured Prediction. NIPS 2010: 1594-1602
2000 – 2009
- 2009
- [c67]Hoang Trinh, David A. McAllester:
Unsupervised Learning of Stereo Vision with Monocular Depth Cues. BMVC 2009: 1-11 - [c66]Chunzhao Guo, Seiichi Mita, David A. McAllester:
Stereovision-based road boundary detection for intelligent vehicles in challenging scenarios. IROS 2009: 1723-1728 - [c65]Alexander Ihler, David A. McAllester:
Particle Belief Propagation. AISTATS 2009: 256-263 - 2008
- [j23]David A. McAllester, Michael Collins, Fernando Pereira:
Case-factor diagrams for structured probabilistic modeling. J. Comput. Syst. Sci. 74(1): 84-96 (2008) - [c64]Pedro F. Felzenszwalb, David A. McAllester, Deva Ramanan:
A discriminatively trained, multiscale, deformable part model. CVPR 2008 - [c63]Hoang Trinh, David A. McAllester:
Particle-Based Belief Propagation for Structure from Motion and Dense Stereo Vision with Unknown Camera Constraints. RobVis 2008: 16-28 - [e3]David A. McAllester, Petri Myllymäki:
UAI 2008, Proceedings of the 24th Conference in Uncertainty in Artificial Intelligence, Helsinki, Finland, July 9-12, 2008. AUAI Press 2008, ISBN 0-9749039-4-9 [contents] - 2007
- [j22]Pedro F. Felzenszwalb, David A. McAllester:
The Generalized A* Architecture. J. Artif. Intell. Res. 29: 153-190 (2007) - [c62]Prahladh Harsha, Rahul Jain, David A. McAllester, Jaikumar Radhakrishnan:
The Communication Complexity of Correlation. CCC 2007: 10-23 - 2006
- [j21]Matthias Blume, David A. McAllester:
Sound and complete models of contracts. J. Funct. Program. 16(4-5): 375-414 (2006) - [c61]Pedro F. Felzenszwalb, David A. McAllester:
A Min-Cover Approach for Finding Salient Curves. CVPR Workshops 2006: 185 - [i2]Prahladh Harsha, Rahul Jain, David A. McAllester, Jaikumar Radhakrishnan:
The communication complexity of correlation. Electron. Colloquium Comput. Complex. TR06 (2006) - 2005
- [c60]Scott Sanner, David A. McAllester:
Affine Algebraic Decision Diagrams (AADDs) and their Application to Structured Probabilistic Inference. IJCAI 2005: 1384-1390 - [c59]Yasemin Altun, David A. McAllester, Mikhail Belkin:
Margin Semi-Supervised Learning for Structured Variables. NIPS 2005: 33-40 - 2004
- [j20]John Langford, David A. McAllester:
Computable Shell Decomposition Bounds. J. Mach. Learn. Res. 5: 529-547 (2004) - [c58]Matthias Blume, David A. McAllester:
A sound (and complete) model of contracts. ICFP 2004: 189-200 - [c57]Peter L. Bartlett, Michael Collins, Benjamin Taskar, David A. McAllester:
Exponentiated Gradient Algorithms for Large-margin Structured Classification. NIPS 2004: 113-120 - [c56]David A. McAllester, Michael Collins, Fernando Pereira:
Case-Factor Diagrams for Structured Probabilistic Modeling. UAI 2004: 382-391 - 2003
- [j19]Peter Stone, Robert E. Schapire, Michael L. Littman, János A. Csirik, David A. McAllester:
Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions. J. Artif. Intell. Res. 19: 209-242 (2003) - [j18]David A. McAllester, Luis E. Ortiz:
Concentration Inequalities for the Missing Mass and for Histogram Rule Error. J. Mach. Learn. Res. 4: 895-911 (2003) - [j17]David A. McAllester:
PAC-Bayesian Stochastic Model Selection. Mach. Learn. 51(1): 5-21 (2003) - [c55]David A. McAllester:
Simplified PAC-Bayesian Margin Bounds. COLT 2003: 203-215 - [c54]David A. McAllester:
Joint RTA-TLCA Invited Talk: A Logical Algorithm for ML Type Inference. RTA 2003: 436-451 - 2002
- [j16]Robert Givan, David A. McAllester, Carl Witty, Dexter Kozen:
Tarskian Set Constraints. Inf. Comput. 174(2): 105-131 (2002) - [j15]David A. McAllester, Deniz Yuret:
Alpha-Beta-Conspiracy Search. J. Int. Comput. Games Assoc. 25(1): 16-35 (2002) - [j14]David A. McAllester:
On the complexity analysis of static analyses. J. ACM 49(4): 512-537 (2002) - [j13]Yishay Mansour, David A. McAllester:
Boosting Using Branching Programs. J. Comput. Syst. Sci. 64(1): 103-112 (2002) - [j12]Robert Givan, David A. McAllester:
Polynomial-time computation via local inference relations. ACM Trans. Comput. Log. 3(4): 521-541 (2002) - [c53]Peter Stone, Robert E. Schapire, János A. Csirik, Michael L. Littman, David A. McAllester:
ATTac-2001: A Learning, Autonomous Bidding Agent. AMEC 2002: 143-160 - [c52]Harald Ganzinger, David A. McAllester:
Logical Algorithms. ICLP 2002: 209-223 - [c51]Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, János A. Csirik:
Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. ICML 2002: 546-553 - [c50]David A. McAllester, Luis E. Ortiz:
Concentration Inequalities for the Missing Mass and for Histogram Rule Error. NIPS 2002: 351-358 - 2001
- [j11]Andrew W. Appel, David A. McAllester:
An indexed model of recursive types for foundational proof-carrying code. ACM Trans. Program. Lang. Syst. 23(5): 657-683 (2001) - [c49]Peter Stone, David A. McAllester:
An architecture for action selection in robotic soccer. Agents 2001: 316-323 - [c48]Harald Ganzinger, David A. McAllester:
A New Meta-complexity Theorem for Bottom-Up Logic Programs. IJCAR 2001: 514-528 - [c47]Sanjoy Dasgupta, Michael L. Littman, David A. McAllester:
PAC Generalization Bounds for Co-training. NIPS 2001: 375-382 - 2000
- [c46]David A. McAllester, Robert E. Schapire:
On the Convergence Rate of Good-Turing Estimators. COLT 2000: 1-6 - [c45]John Langford, David A. McAllester:
Computable Shell Decomposition Bounds. COLT 2000: 25-34 - [c44]Yishay Mansour, David A. McAllester:
Generalization Bounds for Decision Trees. COLT 2000: 69-74 - [c43]Yishay Mansour, David A. McAllester:
Boosting Using Branching Programs. COLT 2000: 220-224 - [c42]David A. McAllester:
Meta-complexity Theorems: Talk Abstract. CP 2000: 13-17 - [c41]William W. Cohen, Henry A. Kautz, David A. McAllester:
Hardening soft information sources. KDD 2000: 255-259 - [c40]David A. McAllester, Peter Stone:
Keeping the Ball from CMUnited-99. RoboCup 2000: 333-338 - [c39]Patrick Riley, Peter Stone, David A. McAllester, Manuela M. Veloso:
ATT-CMUnited-2000: Third Place Finisher in the RoboCup-2000 Simulator League. RoboCup 2000: 489-492 - [e2]David A. McAllester:
Automated Deduction - CADE-17, 17th International Conference on Automated Deduction, Pittsburgh, PA, USA, June 17-20, 2000, Proceedings. Lecture Notes in Computer Science 1831, Springer 2000, ISBN 3-540-67664-3 [contents] - [i1]Robert Givan, David A. McAllester:
Polynomial-time Computation via Local Inference Relations. CoRR cs.LO/0007020 (2000)
1990 – 1999
- 1999
- [j10]David A. McAllester:
Some PAC-Bayesian Theorems. Mach. Learn. 37(3): 355-363 (1999) - [c38]Steven P. Abney, David A. McAllester, Fernando Pereira:
Relating Probabilistic Grammars and Automata. ACL 1999: 542-549 - [c37]David A. McAllester:
PAC-Bayesian Model Averaging. COLT 1999: 164-170 - [c36]David A. McAllester:
World-Modeling vs. World-Axiomatizing. LPNMR 1999: 375-388 - [c35]Yishay Mansour, David A. McAllester:
Boosting with Multi-Way Branching in Decision Trees. NIPS 1999: 300-306 - [c34]Richard S. Sutton, David A. McAllester, Satinder Singh, Yishay Mansour:
Policy Gradient Methods for Reinforcement Learning with Function Approximation. NIPS 1999: 1057-1063 - [c33]David A. McAllester:
On the Complexity Analysis of Static Analyses. SAS 1999: 312-329 - [c32]David A. McAllester, Satinder Singh:
Approximate Planning for Factored POMDPs using Belief State Simplification. UAI 1999: 409-416 - [e1]Harald Ganzinger, David A. McAllester, Andrei Voronkov:
Logic Programming and Automated Reasoning, 6th International Conference, LPAR'99, Tbilisi, Georgia, September 6-10, 1999, Proceedings. Lecture Notes in Computer Science 1705, Springer 1999, ISBN 3-540-66492-0 [contents] - 1998
- [c31]David A. McAllester:
Some PAC-Bayesian Theorems. COLT 1998: 230-234 - [c30]Witold Charatonik, David A. McAllester, Damian Niwinski, Andreas Podelski, Igor Walukiewicz:
The Horn Mu-calculus. LICS 1998: 58-69 - 1997
- [c29]David A. McAllester, Bart Selman, Henry A. Kautz:
Evidence for Invariants in Local Search. AAAI/IAAI 1997: 321-326 - [c28]Daphne Koller, David A. McAllester, Avi Pfeffer:
Effective Bayesian Inference for Stochastic Programs. AAAI/IAAI 1997: 740-747 - [c27]Nevin Heintze, David A. McAllester:
On the Complexity of Set-Based Analysis. ICFP 1997: 150-163 - [c26]Bart Selman, Henry A. Kautz, David A. McAllester:
Ten Challenges in Propositional Reasoning and Search. IJCAI (1) 1997: 50-54 - [c25]Nevin Heintze, David A. McAllester:
On the Cubic Bottleneck in Subtyping and Flow Analysis. LICS 1997: 342-351 - [c24]Nevin Heintze, David A. McAllester:
Linear-time Subtransitive Control Flow Analysis. PLDI 1997: 261-272 - 1996
- [j9]David A. McAllester:
The Rise of Nonlinear Mathematical Programming. ACM Comput. Surv. 28(4es): 68 (1996) - [c23]David A. McAllester, Kostas Arkoudas:
Walther Recursion. CADE 1996: 643-657 - [c22]Henry A. Kautz, David A. McAllester, Bart Selman:
Encoding Plans in Propositional Logic. KR 1996: 374-384 - [c21]David A. McAllester, Robert Givan, Carl Witty, Dexter Kozen:
Tarskian Set Constraints. LICS 1996: 138-147 - 1995
- [j8]David A. McAllester, J. Kucan, D. F. Otth:
A Proof of Strong Normalization of F_2, F_omega and Beyond. Inf. Comput. 121(2): 193-200 (1995) - 1994
- [c20]Matthew L. Ginsberg, David A. McAllester:
GSAT and Dynamic Backtracking. KR 1994: 226-237 - [c19]Matthew L. Ginsberg, David A. McAllester:
GSAT and Dynamic Backtracking. PPCP 1994: 243-265 - [c18]Frédéric Benhamou, David A. McAllester, Pascal Van Hentenryck:
CLP(Intervals) Revisited. ILPS 1994: 124-138 - 1993
- [j7]David A. McAllester, Robert Givan:
Taxonomic Syntax for First Order Inference. J. ACM 40(2): 246-283 (1993) - [j6]David A. McAllester:
Automatic Recognition of Tractability in Inference Relations. J. ACM 40(2): 284-303 (1993) - [j5]David A. McAllester, Prakash Panangaden, Vasant Shanbhogue:
Nonexpressibility of Fairness and Signaling. J. Comput. Syst. Sci. 47(2): 287-321 (1993) - [j4]Hai-Ping Ko, David A. McAllester, Mark E. Nadel:
Lower Bounds for the Lengths of Refutations. J. Log. Program. 17(1): 31-58 (1993) - [c17]Jeffrey Mark Siskind, David A. McAllester:
Nondeterministic Lisp as a Substrate for Constraint Logic Programming. AAAI 1993: 133-138 - [c16]David A. McAllester:
Bottom Up Logic Programming as an Inference Tool. ICTAI 1993: 8 - 1992
- [j3]David A. McAllester, Robert Givan:
Natural Language Syntax and First-Order Inference. Artif. Intell. 56(1): 1-20 (1992) - [c15]David A. McAllester:
Grammar Rewriting. CADE 1992: 124-138 - [c14]Robert Givan, David A. McAllester:
New Results on Local Inference Relations. KR 1992: 403-412 - 1991
- [j2]David A. McAllester:
Socratic Sequent Systems. SIGART Bull. 2(3): 98-100 (1991) - [c13]David A. McAllester, David Rosenblitt:
Systematic Nonlinear Planning. AAAI 1991: 634-639 - [c12]David A. McAllester:
Observations on Cognitive Judgments. AAAI 1991: 910-914 - [c11]Robert Givan, David A. McAllester, Sameer Shalaby:
Natural Language Based Inference Procedures Applied to Schubert's Steamroller. AAAI 1991: 915-920 - 1990
- [c10]David A. McAllester:
Truth Maintenance. AAAI 1990: 1109-1116
1980 – 1989
- 1989
- [c9]David A. McAllester, Robert Givan, Tanveer Fatima:
Taxonomic Syntax for First Order Inference. KR 1989: 289-300 - 1988
- [j1]David A. McAllester:
Conspiracy Numbers for Min-Max Search. Artif. Intell. 35(3): 287-310 (1988) - [c8]Ramin Zabih, David A. McAllester:
A Rearrangement Search Strategy for Determining Propositional Satisfiability. AAAI 1988: 155-160 - [c7]David A. McAllester:
Ontic: A Knowledge Representation System for Mathematics. CADE 1988: 742-743 - [c6]David A. McAllester, Prakash Panangaden, Vasant Shanbhogue:
Nonexpressibility of Fairness and Signaling. FOCS 1988: 377-386 - [c5]Charles Elkan, David A. McAllester:
Automated Inductive Reasoning about Logic Programs. ICLP/SLP 1988: 876-892 - 1987
- [c4]Ramin Zabih, David A. McAllester, David Chapman:
Non-Deterministic Lisp with Dependency-directed Backtracking. AAAI 1987: 59-65 - 1986
- [c3]David A. McAllester, Ramin Zabih:
Boolean Classes. OOPSLA 1986: 417-423 - 1984
- [c2]Joseph Y. Halpern, David A. McAllester:
Likelihood, Probability, and Knowledge. AAAI 1984: 137-141 - 1981
- [c1]David A. McAllester:
Algebraic Approximations. IJCAI 1981: 1024-1026
Coauthor Index
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