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ORCIDAdam Tauman Kalai
Adam Kalai
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- affiliation: University of Chicago, USA
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2020 – today
- 2024
[j21]Adam Tauman Kalai
, Ehud Kalai:
Beyond dominance and Nash: Ranking equilibria by critical mass. Games Econ. Behav. 144: 378-394 (2024)- [c89]Ayush Agrawal, Mirac Suzgun, Lester Mackey, Adam Kalai:
Do Language Models Know When They're Hallucinating References? EACL (Findings) 2024: 912-928 - [c88]Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Adam Tauman Kalai, Preetum Nakkiran:
Loss Minimization Yields Multicalibration for Large Neural Networks. ITCS 2024: 17:1-17:21 - [c87]Adam Tauman Kalai, Santosh S. Vempala:
Calibrated Language Models Must Hallucinate. STOC 2024: 160-171 - [i47]Mirac Suzgun, Adam Tauman Kalai:
Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding. CoRR abs/2401.12954 (2024) - 2023
- [j20]Myra Cheng, Maria De-Arteaga, Lester Mackey, Adam Tauman Kalai:
Social norm bias: residual harms of fairness-aware algorithms. Data Min. Knowl. Discov. 37(5): 1858-1884 (2023) - [c86]Ethan Prihar, Morgan P. Lee, Mia Hopman, Adam Tauman Kalai, Sofia Vempala, Allison Wang, Gabriel Wickline, Aly Murray, Neil T. Heffernan:
Comparing Different Approaches to Generating Mathematics Explanations Using Large Language Models. AIED (Posters/Late Breaking Results/...) 2023: 290-295 - [c85]Patrick Haluptzok, Matthew Bowers, Adam Tauman Kalai:
Language Models Can Teach Themselves to Program Better. ICLR 2023 - [c84]Gati V. Aher, Rosa I. Arriaga, Adam Tauman Kalai:
Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies. ICML 2023: 337-371 - [c83]Shafi Goldwasser, David F. Gruber, Adam Tauman Kalai, Orr Paradise:
A Theory of Unsupervised Translation Motivated by Understanding Animal Communication. NeurIPS 2023 - [c82]Elad Hazan, Adam Tauman Kalai, Varun Kanade, Clara Mohri, Y. Jennifer Sun:
Partial Matrix Completion. NeurIPS 2023 - [i46]Jaroslaw Blasiok, Parikshit Gopalan, Lunjia Hu, Adam Tauman Kalai, Preetum Nakkiran:
Loss minimization yields multicalibration for large neural networks. CoRR abs/2304.09424 (2023) - [i45]Ayush Agrawal, Lester Mackey, Adam Tauman Kalai:
Do Language Models Know When They're Hallucinating References? CoRR abs/2305.18248 (2023) - [i44]Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Harkirat Singh Behl, Xin Wang, Sébastien Bubeck, Ronen Eldan, Adam Tauman Kalai, Yin Tat Lee, Yuanzhi Li:
Textbooks Are All You Need. CoRR abs/2306.11644 (2023) - [i43]Eric Zelikman, Eliana Lorch, Lester Mackey, Adam Tauman Kalai:
Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation. CoRR abs/2310.02304 (2023) - [i42]Silen Naihin, David Atkinson, Marc Green, Merwane Hamadi, Craig Swift, Douglas Schonholtz, Adam Tauman Kalai, David Bau:
Testing Language Model Agents Safely in the Wild. CoRR abs/2311.10538 (2023) - [i41]Adam Tauman Kalai, Santosh S. Vempala:
Calibrated Language Models Must Hallucinate. CoRR abs/2311.14648 (2023) - 2022
- [c81]Parikshit Gopalan, Adam Tauman Kalai, Omer Reingold, Vatsal Sharan, Udi Wieder:
Omnipredictors. ITCS 2022: 79:1-79:21 - [c80]David Alvarez-Melis, Vikas Garg, Adam Kalai:
Are GANs overkill for NLP? NeurIPS 2022 - [c79]Surbhi Goel, Sham M. Kakade, Adam Kalai, Cyril Zhang:
Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms. NeurIPS 2022 - [i40]David Alvarez-Melis, Vikas Garg, Adam Tauman Kalai:
Why GANs are overkill for NLP. CoRR abs/2205.09838 (2022) - [i39]Patrick Haluptzok, Matthew Bowers, Adam Tauman Kalai:
Language Models Can Teach Themselves to Program Better. CoRR abs/2207.14502 (2022) - [i38]Gati Aher, Rosa I. Arriaga, Adam Tauman Kalai:
Using Large Language Models to Simulate Multiple Humans. CoRR abs/2208.10264 (2022) - [i37]Varun Kanade, Elad Hazan, Adam Tauman Kalai:
Partial Matrix Completion. CoRR abs/2208.12063 (2022) - [i36]Surbhi Goel, Sham M. Kakade, Adam Tauman Kalai, Cyril Zhang:
Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms. CoRR abs/2209.00735 (2022) - [i35]Shafi Goldwasser, David F. Gruber, Adam Tauman Kalai, Orr Paradise:
A Theory of Unsupervised Translation Motivated by Understanding Animal Communication. CoRR abs/2211.11081 (2022) - 2021
- [c78]Vikas K. Garg, Adam Tauman Kalai, Katrina Ligett, Zhiwei Steven Wu:
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization. AISTATS 2021: 3574-3582 - [c77]Adam Tauman Kalai, Varun Kanade:
Efficient Learning with Arbitrary Covariate Shift. ALT 2021: 850-864 - [c76]Adam Kalai, Varun Kanade:
Towards optimally abstaining from prediction with OOD test examples. NeurIPS 2021: 12774-12785 - [c75]Tal Schuster, Ashwin Kalyan, Alex Polozov, Adam Kalai:
Programming Puzzles. NeurIPS Datasets and Benchmarks 2021 - [i34]Adam Kalai, Varun Kanade:
Efficient Learning with Arbitrary Covariate Shift. CoRR abs/2102.07802 (2021) - [i33]Adam Tauman Kalai, Varun Kanade:
Towards optimally abstaining from prediction. CoRR abs/2105.14119 (2021) - [i32]Tal Schuster, Ashwin Kalyan, Oleksandr Polozov, Adam Tauman Kalai:
Programming Puzzles. CoRR abs/2106.05784 (2021) - [i31]Myra Cheng, Maria De-Arteaga, Lester Mackey, Adam Tauman Kalai:
Social Norm Bias: Residual Harms of Fairness-Aware Algorithms. CoRR abs/2108.11056 (2021) - [i30]Parikshit Gopalan, Adam Tauman Kalai, Omer Reingold, Vatsal Sharan, Udi Wieder:
Omnipredictors. CoRR abs/2109.05389 (2021) - 2020
- [c74]Lydia T. Liu, Ashia Wilson, Nika Haghtalab, Adam Tauman Kalai, Christian Borgs, Jennifer T. Chayes:
The disparate equilibria of algorithmic decision making when individuals invest rationally. FAT* 2020: 381-391 - [c73]Shafi Goldwasser, Adam Tauman Kalai, Yael Tauman Kalai, Omar Montasser:
Identifying unpredictable test examples with worst-case guarantees. ITA 2020: 1-14 - [c72]Werner Geyer, Lydia B. Chilton, Ranjitha Kumar, Adam Tauman Kalai:
HAI-GEN 2020: Workshop on Human-AI Co-Creation with Generative Models. IUI Companion 2020: 13-14 - [c71]Werner Geyer, Lydia B. Chilton, Ranjitha Kumar, Adam Tauman Kalai:
HAI-GEN 2020 : Workshop on Human-AI Co-Creation with Generative Models. HAI-GEN+user2agent@IUI 2020 - [c70]Shafi Goldwasser, Adam Tauman Kalai, Yael Kalai, Omar Montasser:
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples. NeurIPS 2020 - [i29]Vikas K. Garg, Adam Kalai, Katrina Ligett, Zhiwei Steven Wu:
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization. CoRR abs/2002.05660 (2020) - [i28]Shafi Goldwasser, Adam Tauman Kalai, Yael Tauman Kalai, Omar Montasser:
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples. CoRR abs/2007.05145 (2020)
2010 – 2019
- 2019
- [c69]Christian Borgs, Jennifer T. Chayes, Nika Haghtalab, Adam Tauman Kalai, Ellen Vitercik:
Algorithmic Greenlining: An Approach to Increase Diversity. AIES 2019: 69-76 - [c68]Nathaniel Swinger, Maria De-Arteaga, Neil Thomas Heffernan IV, Mark D. M. Leiserson, Adam Tauman Kalai:
What are the Biases in My Word Embedding? AIES 2019: 305-311 - [c67]Brian Bullins, Elad Hazan, Adam Kalai, Roi Livni:
Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning. ALT 2019: 235-246 - [c66]Daniel Alabi, Adam Tauman Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, Ellen Vitercik:
Learning to Prune: Speeding up Repeated Computations. COLT 2019: 30-33 - [c65]Maria De-Arteaga, Alexey Romanov, Hanna M. Wallach, Jennifer T. Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Cem Geyik, Krishnaram Kenthapadi, Adam Tauman Kalai:
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting. FAT 2019: 120-128 - [c64]Limor Gultchin, Genevieve Patterson, Nancy Baym, Nathaniel Swinger, Adam Kalai:
Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops. ICML 2019: 2474-2483 - [c63]Alexey Romanov, Maria De-Arteaga, Hanna M. Wallach, Jennifer T. Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Cem Geyik, Krishnaram Kenthapadi, Anna Rumshisky, Adam Kalai:
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes. NAACL-HLT (1) 2019: 4187-4195 - [i27]Maria De-Arteaga, Alexey Romanov, Hanna M. Wallach, Jennifer T. Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Cem Geyik, Krishnaram Kenthapadi, Adam Tauman Kalai:
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting. CoRR abs/1901.09451 (2019) - [i26]Limor Gultchin, Genevieve Patterson, Nancy Baym, Nathaniel Swinger, Adam Tauman Kalai:
Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops. CoRR abs/1902.02783 (2019) - [i25]Alexey Romanov, Maria De-Arteaga, Hanna M. Wallach, Jennifer T. Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Cem Geyik, Krishnaram Kenthapadi, Anna Rumshisky, Adam Tauman Kalai:
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes. CoRR abs/1904.05233 (2019) - [i24]Daniel Alabi, Adam Tauman Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, Ellen Vitercik:
Learning to Prune: Speeding up Repeated Computations. CoRR abs/1904.11875 (2019) - [i23]Lydia T. Liu, Ashia Wilson, Nika Haghtalab, Adam Tauman Kalai, Christian Borgs, Jennifer T. Chayes:
The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationally. CoRR abs/1910.04123 (2019) - 2018
- [c62]Konstantina Christakopoulou, Adam Tauman Kalai:
Glass-Box Program Synthesis: A Machine Learning Approach. AAAI 2018: 646-653 - [c61]Steve Hanneke, Adam Tauman Kalai, Gautam Kamath, Christos Tzamos:
Actively Avoiding Nonsense in Generative Models. COLT 2018: 209-227 - [c60]Daniel Alabi, Nicole Immorlica, Adam Kalai:
Unleashing Linear Optimizers for Group-Fair Learning and Optimization. COLT 2018: 2043-2066 - [c59]Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, Mark D. M. Leiserson:
Decoupled Classifiers for Group-Fair and Efficient Machine Learning. FAT 2018: 119-133 - [c58]Samira Samadi, Santosh S. Vempala, Adam Tauman Kalai:
Usability of Humanly Computable Passwords. HCOMP 2018: 174-183 - [i22]Steve Hanneke, Adam Kalai, Gautam Kamath, Christos Tzamos:
Actively Avoiding Nonsense in Generative Models. CoRR abs/1802.07229 (2018) - [i21]Daniel Alabi, Nicole Immorlica, Adam Tauman Kalai:
When optimizing nonlinear objectives is no harder than linear objectives. CoRR abs/1804.04503 (2018) - [i20]Nathaniel Swinger, Maria De-Arteaga, Neil Thomas Heffernan IV, Mark D. M. Leiserson, Adam Tauman Kalai:
What are the biases in my word embedding? CoRR abs/1812.08769 (2018) - 2017
- [c57]Kenneth Charles Arnold, Kai-Wei Chang, Adam Tauman Kalai:
Learning to Suggest Phrases. AAAI Workshops 2017 - [c56]Kenneth C. Arnold, Kai-Wei Chang, Adam Kalai:
Counterfactual Language Model Adaptation for Suggesting Phrases. IJCNLP(2) 2017: 49-54 - [c55]Shyam Upadhyay, Kai-Wei Chang, Matt Taddy, Adam Kalai, James Y. Zou:
Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context. Rep4NLP@ACL 2017: 101-110 - [c54]Danielle Bragg, Shiri Azenkot, Kevin Larson, Ann Bessemans, Adam Tauman Kalai:
Designing and Evaluating Livefonts. UIST 2017: 481-492 - [e2]Steven Dow, Adam Tauman Kalai:
Proceedings of the Fifth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2017, 23-26 October 2017, Québec City, Québec, Canada. AAAI Press 2017 [contents] - [i19]Shyam Upadhyay, Kai-Wei Chang, Matt Taddy, Adam Tauman Kalai, James Y. Zou:
Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context. CoRR abs/1706.08160 (2017) - [i18]Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, Max D. M. Leiserson:
Decoupled classifiers for fair and efficient machine learning. CoRR abs/1707.06613 (2017) - [i17]Vikas K. Garg, Adam Kalai:
Supervising Unsupervised Learning. CoRR abs/1709.05262 (2017) - [i16]Konstantina Christakopoulou, Adam Tauman Kalai:
Glass-Box Program Synthesis: A Machine Learning Approach. CoRR abs/1709.08669 (2017) - [i15]Kenneth C. Arnold, Kai-Wei Chang, Adam Tauman Kalai:
Counterfactual Language Model Adaptation for Suggesting Phrases. CoRR abs/1710.01799 (2017) - [i14]Samira Samadi, Santosh S. Vempala, Adam Tauman Kalai:
Usability of Humanly Computable Passwords. CoRR abs/1712.03650 (2017) - 2016
- [c53]Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, Adam Tauman Kalai:
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. NIPS 2016: 4349-4357 - [c52]Danielle Bragg, Shiri Azenkot, Adam Tauman Kalai:
Reading and Learning Smartfonts. UIST 2016: 391-402 - [c51]Kenneth C. Arnold, Krzysztof Z. Gajos, Adam Tauman Kalai:
On Suggesting Phrases vs. Predicting Words for Mobile Text Composition. UIST 2016: 603-608 - [i13]Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, Adam Tauman Kalai:
Quantifying and Reducing Stereotypes in Word Embeddings. CoRR abs/1606.06121 (2016) - [i12]Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, Adam Kalai:
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. CoRR abs/1607.06520 (2016) - [i11]Vikas K. Garg, Adam Tauman Kalai:
Meta-Unsupervised-Learning: A supervised approach to unsupervised learning. CoRR abs/1612.09030 (2016) - 2015
- [c50]James Y. Zou, Kamalika Chaudhuri, Adam Tauman Kalai:
Crowdsourcing Feature Discovery via Adaptively Chosen Comparisons. HCOMP 2015: 198-205 - [c49]Miaomiao Wen, Nancy Baym, Omer Tamuz, Jaime Teevan, Susan T. Dumais, Adam Kalai:
OMG UR Funny! Computer-Aided Humor with an Application to Chat. ICCC 2015: 86-93 - [c48]Peter Organisciak, Jaime Teevan, Susan T. Dumais, Robert C. Miller, Adam Tauman Kalai:
Matching and Grokking: Approaches to Personalized Crowdsourcing. IJCAI 2015: 4296-4302 - [i10]James Y. Zou, Kamalika Chaudhuri, Adam Tauman Kalai:
Crowdsourcing Feature Discovery via Adaptively Chosen Comparisons. CoRR abs/1504.00064 (2015) - 2014
- [c47]Peter Organisciak, Jaime Teevan, Susan T. Dumais, Robert C. Miller, Adam Tauman Kalai:
A Crowd of Your Own: Crowdsourcing for On-Demand Personalization. HCOMP 2014: 192-200 - 2013
- [c46]Peter Organisciak, Jaime Teevan, Susan T. Dumais, Robert C. Miller, Adam Tauman Kalai:
Personalized Human Computation. HCOMP (Works in Progress / Demos) 2013 - [c45]Aditya Krishna Menon, Omer Tamuz, Sumit Gulwani, Butler W. Lampson, Adam Kalai:
A Machine Learning Framework for Programming by Example. ICML (1) 2013: 187-195 - [c44]Sivan Sabato, Adam Kalai:
Feature Multi-Selection among Subjective Features. ICML (3) 2013: 810-818 - [c43]Kuat Yessenov, Shubham Tulsiani, Aditya Krishna Menon, Robert C. Miller, Sumit Gulwani, Butler W. Lampson, Adam Kalai:
A colorful approach to text processing by example. UIST 2013: 495-504 - [i9]Sivan Sabato, Adam Kalai:
Feature Multi-Selection among Subjective Features. CoRR abs/1302.4297 (2013) - 2012
- [j19]Adam Tauman Kalai, Ankur Moitra, Gregory Valiant:
Disentangling Gaussians. Commun. ACM 55(2): 113-120 (2012) - [j18]Adam Tauman Kalai, Varun Kanade, Yishay Mansour:
Reliable agnostic learning. J. Comput. Syst. Sci. 78(5): 1481-1495 (2012) - [i8]Aditya Krishna Menon, Omer Tamuz, Sumit Gulwani, Butler W. Lampson, Adam Tauman Kalai:
Textual Features for Programming by Example. CoRR abs/1209.3811 (2012) - 2011
- [j17]Adam Kalai, Ehud Kalai:
Cooperation in two person games, revisited. SIGecom Exch. 10(1): 13-16 (2011) - [c42]Omer Tamuz, Ce Liu, Serge J. Belongie, Ohad Shamir, Adam Kalai:
Adaptively Learning the Crowd Kernel. ICML 2011: 673-680 - [c41]Brendan Juba, Adam Tauman Kalai, Sanjeev Khanna, Madhu Sudan:
Compression without a common prior: an information-theoretic justification for ambiguity in language. ICS 2011: 79-86 - [c40]Sham M. Kakade, Adam Kalai, Varun Kanade, Ohad Shamir:
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression. NIPS 2011: 927-935 - [c39]Nicole Immorlica, Adam Tauman Kalai, Brendan Lucier, Ankur Moitra, Andrew Postlewaite, Moshe Tennenholtz:
Dueling algorithms. STOC 2011: 215-224 - [i7]Nicole Immorlica, Adam Tauman Kalai, Brendan Lucier, Ankur Moitra, Andrew Postlewaite, Moshe Tennenholtz:
Dueling Algorithms. CoRR abs/1101.2883 (2011) - [i6]Sham M. Kakade, Adam Tauman Kalai, Varun Kanade, Ohad Shamir:
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression. CoRR abs/1104.2018 (2011) - [i5]Omer Tamuz, Ce Liu, Serge J. Belongie, Ohad Shamir, Adam Tauman Kalai:
Adaptively Learning the Crowd Kernel. CoRR abs/1105.1033 (2011) - 2010
- [j16]Adam Tauman Kalai, Ehud Kalai, Ehud Lehrer, Dov Samet:
A commitment folk theorem. Games Econ. Behav. 69(1): 127-137 (2010) - [j15]Christian Borgs, Jennifer T. Chayes, Nicole Immorlica, Adam Tauman Kalai, Vahab S. Mirrokni, Christos H. Papadimitriou:
The myth of the Folk Theorem. Games Econ. Behav. 70(1): 34-43 (2010) - [c38]Michal Feldman, Adam Kalai, Moshe Tennenholtz:
Playing Games without Observing Payoffs. ICS 2010: 106-110 - [c37]Adam Kalai, Michael Mitzenmacher, Madhu Sudan:
Tight asymptotic bounds for the deletion channel with small deletion probabilities. ISIT 2010: 997-1001 - [c36]Adam Tauman Kalai, Ehud Kalai:
Cooperation and competition in strategic games with private information. EC 2010: 345-346 - [c35]Aaron Roth, Maria-Florina Balcan, Adam Kalai, Yishay Mansour:
On the Equilibria of Alternating Move Games. SODA 2010: 805-816 - [c34]Adam Tauman Kalai, Ankur Moitra, Gregory Valiant:
Efficiently learning mixtures of two Gaussians. STOC 2010: 553-562 - [c33]Christian Borgs, Jennifer T. Chayes, Adam Tauman Kalai, Azarakhsh Malekian, Moshe Tennenholtz:
A Novel Approach to Propagating Distrust. WINE 2010: 87-105 - [e1]Adam Tauman Kalai, Mehryar Mohri:
COLT 2010 - The 23rd Conference on Learning Theory, Haifa, Israel, June 27-29, 2010. Omnipress 2010, ISBN 978-0-9822529-2-5 [contents]
2000 – 2009
- 2009
- [j14]Sanjoy Dasgupta, Adam Tauman Kalai, Claire Monteleoni:
Analysis of Perceptron-Based Active Learning. J. Mach. Learn. Res. 10: 281-299 (2009) - [j13]Sham M. Kakade, Adam Tauman Kalai, Katrina Ligett:
Playing Games with Approximation Algorithms. SIAM J. Comput. 39(3): 1088-1106 (2009) - [c32]Adam Tauman Kalai, Varun Kanade, Yishay Mansour:
Reliable Agnostic Learning. COLT 2009 - [c31]Adam Tauman Kalai, Ravi Sastry:
The Isotron Algorithm: High-Dimensional Isotonic Regression. COLT 2009 - [c30]Adam Tauman Kalai, Alex Samorodnitsky, Shang-Hua Teng:
Learning and Smoothed Analysis. FOCS 2009: 395-404 - [c29]Adam Kalai, Varun Kanade:
Potential-Based Agnostic Boosting. NIPS 2009: 880-888 - 2008
- [j12]Adam Tauman Kalai, Adam R. Klivans, Yishay Mansour, Rocco A. Servedio:
Agnostically Learning Halfspaces. SIAM J. Comput. 37(6): 1777-1805 (2008) - [c28]Parikshit Gopalan, Adam Kalai, Adam R. Klivans:
A Query Algorithm for Agnostically Learning DNF?. COLT 2008: 515-516 - [c27]Christian Borgs, Jennifer T. Chayes, Nicole Immorlica, Adam Tauman Kalai, Vahab S. Mirrokni, Christos H. Papadimitriou:
The myth of the folk theorem. STOC 2008: 365-372 - [c26]Parikshit Gopalan, Adam Tauman Kalai, Adam R. Klivans:
Agnostically learning decision trees. STOC 2008: 527-536 - [c25]Adam Tauman Kalai, Yishay Mansour, Elad Verbin:
On agnostic boosting and parity learning. STOC 2008: 629-638 - [c24]Reid Andersen, Christian Borgs, Jennifer T. Chayes, Uriel Feige, Abraham D. Flaxman, Adam Kalai, Vahab S. Mirrokni, Moshe Tennenholtz:
Trust-based recommendation systems: an axiomatic approach. WWW 2008: 199-208 - [i4]Adam Tauman Kalai, Shang-Hua Teng:
Decision trees are PAC-learnable from most product distributions: a smoothed analysis. CoRR abs/0812.0933 (2008) - 2007
- [c23]Adam Tauman Kalai:
Learning Nested Halfspaces and Uphill Decision Trees. COLT 2007: 378-392 - [c22]Sham M. Kakade, Adam Tauman Kalai, Katrina Ligett:
Playing games with approximation algorithms. STOC 2007: 546-555 - [i3]Christian Borgs, Jennifer T. Chayes, Nicole Immorlica, Adam Kalai, Vahab S. Mirrokni, Christos H. Papadimitriou:
The Myth of the Folk Theorem. Electron. Colloquium Comput. Complex. TR07 (2007) - 2006
- [j11]Adam Tauman Kalai, Santosh S. Vempala:
Simulated Annealing for Convex Optimization. Math. Oper. Res. 31(2): 253-266 (2006) - [c21]Elad Hazan, Adam Kalai, Satyen Kale, Amit Agarwal:
Logarithmic Regret Algorithms for Online Convex Optimization. COLT 2006: 499-513 - [c20]Ivona Bezáková, Adam Kalai, Rahul Santhanam:
Graph model selection using maximum likelihood. ICML 2006: 105-112 - [c19]Eli Ben-Sasson, Adam Tauman Kalai, Ehud Kalai:
An Approach to Bounded Rationality. NIPS 2006: 145-152 - 2005
- [j10]Adam Tauman Kalai, Rocco A. Servedio:
Boosting in the presence of noise. J. Comput. Syst. Sci. 71(3): 266-290 (2005) - [j9]Adam Tauman Kalai, Santosh S. Vempala:
Efficient algorithms for online decision problems. J. Comput. Syst. Sci. 71(3): 291-307 (2005) - [c18]Sanjoy Dasgupta, Adam Tauman Kalai, Claire Monteleoni:
Analysis of Perceptron-Based Active Learning. COLT 2005: 249-263 - [c17]Adam Tauman Kalai, Adam R. Klivans, Yishay Mansour, Rocco A. Servedio:
Agnostically Learning Halfspaces. FOCS 2005: 11-20 - [c16]Sham M. Kakade, Adam Kalai:
From Batch to Transductive Online Learning. NIPS 2005: 611-618 - [c15]Abraham Flaxman, Adam Tauman Kalai, H. Brendan McMahan:
Online convex optimization in the bandit setting: gradient descent without a gradient. SODA 2005: 385-394 - 2004
- [c14]Adam Kalai:
Learning Monotonic Linear Functions. COLT 2004: 487-501 - [i2]Abraham Flaxman, Adam Tauman Kalai, H. Brendan McMahan:
Online convex optimization in the bandit setting: gradient descent without a gradient. CoRR cs.LG/0408007 (2004) - 2003
- [j8]Avrim Blum, Shuchi Chawla, Adam Kalai:
Static Optimality and Dynamic Search-Optimality in Lists and Trees. Algorithmica 36(3): 249-260 (2003) - [j7]Avrim Blum, Adam Tauman Kalai, Jon M. Kleinberg:
Admission Control to Minimize Rejections. Internet Math. 1(2): 165-176 (2003) - [j6]Avrim Blum, Adam Kalai, Hal Wasserman:
Noise-tolerant learning, the parity problem, and the statistical query model. J. ACM 50(4): 506-519 (2003) - [j5]Adam Kalai:
Generating Random Factored Numbers, Easily. J. Cryptol. 16(4): 287-289 (2003) - [c13]Adam Kalai, Santosh S. Vempala:
Efficient Algorithms for Online Decision Problems. COLT 2003: 26-40 - [c12]Adam Kalai, Rocco A. Servedio:
Boosting in the presence of noise. STOC 2003: 195-205 - 2002
- [j4]Steven M. Seitz, Adam Kalai, Heung-Yeung Shum:
Omnivergent Stereo. Int. J. Comput. Vis. 48(3): 159-172 (2002) - [j3]Adam Kalai, Santosh S. Vempala:
Efficient Algorithms for Universal Portfolios. J. Mach. Learn. Res. 3: 423-440 (2002) - [c11]Avrim Blum, Shuchi Chawla, Adam Kalai:
Static optimality and dynamic search-optimality in lists and trees. SODA 2002: 1-8 - [c10]Adam Kalai:
Generating random factored numbers, easily. SODA 2002: 412-412 - [c9]Adam Kalai:
Efficient pattern-matching with don't cares. SODA 2002: 655-656 - 2001
- [c8]Avrim Blum, Adam Kalai, Jon M. Kleinberg:
Admission Control to Minimize Rejections. WADS 2001: 155-164 - 2000
- [c7]Adam Kalai, Santosh S. Vempala:
Efficient Algorithms for Universal Portfolios. FOCS 2000: 486-491 - [c6]Avrim Blum, Adam Kalai, Hal Wasserman:
Noise-tolerant learning, the parity problem, and the statistical query model. STOC 2000: 435-440 - [i1]Avrim Blum, Adam Kalai, Hal Wasserman:
Noise-Tolerant Learning, the Parity Problem, and the Statistical Query Model. CoRR cs.LG/0010022 (2000)
1990 – 1999
- 1999
- [j2]Avrim Blum, Adam Kalai:
Universal Portfolios With and Without Transaction Costs. Mach. Learn. 35(3): 193-205 (1999) - [c5]Avrim Blum, Adam Kalai, John Langford:
Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation. COLT 1999: 203-208 - [c4]Avrim Blum, Carl Burch, Adam Kalai:
Finely-Competitive Paging. FOCS 1999: 450-458 - [c3]Adam Kalai, Stanley F. Chen, Avrim Blum, Ronald Rosenfeld:
On-line algorithms for combining language models. ICASSP 1999: 745-748 - [c2]Heung-Yeung Shum, Adam Kalai, Steven M. Seitz:
Omnivergent Stereo. ICCV 1999: 22-29 - 1998
- [j1]Avrim Blum, Adam Kalai:
A Note on Learning from Multiple-Instance Examples. Mach. Learn. 30(1): 23-29 (1998) - 1997
- [c1]Avrim Blum, Adam Kalai:
Universal Portfolios With and Without Transaction Costs. COLT 1997: 309-313
Coauthor Index
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