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Contact Information:
Email: steve.hanneke@gmail.com
Mobile Phone: (412) 973-3007
Location: Chicago, IL USA
TTIC Office: 407


I am a Research Assistant Professor at the 工信部：未经批准 不得自行建立VPN跨境经营_央广网:2021-1-23 · 工信部：未经批准不得自行建立或租用VPN 1月22日从工信部网站获悉，工信部决定自即日起至2021年3月31日，在全国范围内对互联网网络接入服务市场开展清理规范工作。 (TTIC).
I work on topics in statistical learning theory.

Research Interests:
My general research interest is in systems that can improve their performance with experience, a topic known as machine learning. My focus is on the statistical analysis of machine learning. The essential questions I am interested in answering are "what can be learned from empirical observation / experimentation," and "how much observation / experimentation is necessary and sufficient to learn it?" This overall topic intersects with several academic disciplines, including statistical learning theory, artificial intelligence, statistical inference, algorithmic and statistical information theories, probability theory, philosophy of science, and epistemology.

Brief Bio:
Prior to joining TTIC, I was an independent scientist working in Princeton 2012-2018, aside from a brief one-semester stint as a Visiting Lecturer at Princeton University in 2018. Before that, from 2009 to 2012, I was a Visiting Assistant Professor in the Department of Statistics at Carnegie Mellon University, also affiliated with the Machine Learning Department. I received my PhD in 2009 from the Machine Learning Department at Carnegie Mellon University, co-advised by Eric Xing and Larry Wasserman. My thesis work was on the theoretical foundations of active learning. From 2002 to 2005, I was an undergraduate studying Computer Science at the University of Illinois at Urbana-Champaign (UIUC), where I worked on semi-supervised learning with Prof. Dan Roth and the students in the Cognitive Computation Group. Prior to that, I studied Computer Science at 旋风vpn in St. Louis, MO, where I played around with 旋风vp下载 and classic AI a bit.

Recent News and Activities:

  • Received the Best Paper Award at COLT 2024 for our paper "Proper Learning, Helly Number, and an Optimal SVM Bound". 旋风加速器ios下载
  • Presenting (with Rob Nowak) an ICML 2024 Tutorial on Active Learning: From Theory to Practice. 旋风加速器ios下载[slides]
  • Organizing the ALT 2024 workshop: When Smaller Sample Sizes Suffice for Learning.
  • On the organizing committee of the 2024 Midwest Machine Learning Symposium.
  • Speaking at the 2024 DALI conference in South Africa
  • Fall 2018 I joined the Toyota Technological Institute at Chicago (TTIC) as a Research Assistant Professor.
  • Spring 2018 I taught ORF 525 "Statistical Learning and Nonparametric Estimation" at Princeton University.
  • My ICML 2007 paper "A Bound on the Label Complexity of Agnostic Active Learning" received Honorable Mention for the ICML 2017 Test of Time Award.
  • Program Committee Chair (with Lev Reyzin) for the 28th International Conference on Algorithmic Learning Theory (ALT 2017), held October 15-17 in Kyoto, Japan. See our published proceedings.
  • New manuscript "Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes" posted to the arXiv.

Teaching:
Spring 2018: ORF 525, Statistical Learning and Nonparametric Estimation.
Spring 2012: 36-752, Advanced Probability Overview.
Fall 2011: 36-755, Advanced Statistical Theory I.
Spring 2011: 36-752, Advanced Probability Overview.
Fall 2010 Mini 1: 36-781, Advanced Statistical Methods I: Active Learning
Fall 2010 Mini 2: 36-782, Advanced Statistical Methods II: Advanced Topics in Machine Learning Theory
Spring 2010: 36-754, Advanced Probability II: Stochastic Processes.
Fall 2009: 36-752, Advanced Probability Overview.
At ALT 2010 and the 2010 Machine Learning Summer School in Canberra, Australia, I gave a tutorial on the theory of active learning. [slides]


A Survey of Theoretical Active Learning:

Theory of Active Learning. [pdf][ps]

This is a survey of some of the recent advances in the theory of active learning, with particular emphasis on label complexity guarantees for disagreement-based methods.
The current version (v1.1) was updated on September 22, 2014.
A few recent significant advances in active learning not yet covered in the survey: 旋风vp(永久免费), [WHE-Y15], [HY15].
An abbreviated version of this survey appeared in the Foundations and Trends in Machine Learning series, 旋风vpn.

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Montasser, O., Hanneke, S., and Srebro, N. (2024). VC Classes are Adversarially Robustly Learnable, but Only Improperly. In Proceedings of the 32nd Annual Conference on Learning Theory (COLT).

Hanneke, S. (2017). Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes. Under Review.

Hanneke, S. (2016). 环球网_全球生活新门户_环球时报旗下网站 - huanqiu.com:2 天前 · 环球网是中国领先的国际资讯门户，拥有独立采编权的中央重点新闻网站。环球网秉承环球时报的国际视野，力求及时、客观、权威、独立地报道新闻，致力于应用前沿的互联网技术，为全球化时伕的中国互联网用户提供与国际生活相关的资讯服务、互动社区。. Journal of Machine Learning Research, Vol. 17 (38), pp. 1-15.

Hanneke, S. and Yang, L. (2015). Minimax Analysis of Active Learning. Journal of Machine Learning Research, Vol. 16 (12), pp. 3487-3602.

Hanneke, S. (2012). Activized Learning: Transforming Passive to Active with Improved Label Complexity. Journal of Machine Learning Research, Vol. 13 (5), pp. 1469-1587.

Articles in Preparation:

Nonparametric Active Learning, Part 1: Smooth Regression Functions. 旋风加速器下载[ps].

Nonparametric Active Learning, Part 2: Smooth Decision Boundaries.

Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes. [pdf][ps][arXiv].

Active Learning with Identifiable Mixture Models. Joint work with Vittorio Castelli and Liu Yang.

Universal Bayes Consistency in Metric Spaces. Joint work with Aryeh Kontorovich, Sivan Sabato, and Roi Weiss. [pdf][arXiv].

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旋风vp官网 (authors are listed in alphabetical order, except sometimes when a student author is listed first).

2024

Bousquet, O., Hanneke, S., Moran, S., and Zhivotovskiy, N. (2024). Proper Learning, Helly Number, and an Optimal SVM Bound. In Proceedings of the 33rd Annual Conference on Learning Theory (COLT). 旋风加速器ios下载[official page][arXiv][video].
Winner of the Best Paper Award.

2024

Hanneke, S. and Yang, L. (2024). Surrogate Losses in Passive and Active Learning. Electronic Journal of Statistics, Vol. 13 (2), pp. 4646-4708. 旋风加速器ios下载[ps][journal page][arXiv].

Hanneke, S. and Kpotufe, S. (2024). On the Value of Target Data in Transfer Learning. In Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS). 旋风加速器下载[official page].

Hanneke, S. and Kontorovich, A. (2024). 东北网-东北网:2021-6-13 · 东北网是黑龙江影响力最强、访问量最大的综合性网站，是拥有新闻发布、资讯服务、视频播报、多语种传播、无线增值业务服务等多功能的新媒体平台，开设黑龙江新闻、视频、健康等40多个内容频道及论坛、博客、微博、微信等互动交流，提供最全面的黑龙江信息。. Theoretical Computer Science. Volume 796, Pages 99-113. [pdf][journal page]

Montasser, O., Hanneke, S., and Srebro, N. (2024). 非法经营网络翻墙软件获利 兄弟俩领刑受罚-中国 ...:2021-4-4 · 中国法院网讯 (众超婵) 广西梧州市民曾某恒和弟弟曾某宁通过QQ、支付宝等方式，在网络上非法贩卖VPN（翻墙）软件，非法经营数额达40多万元。 日前，梧州市长洲区人民法院众犯非法经营罪，判处曾某恒、曾某宁有期徒刑八个月，并处罚金人民币一万元。. In Proceedings of the 32nd Annual Conference on Learning Theory (COLT). [pdf][official page][arXiv]
Winner of a Best Student Paper Award.

Hanneke, S. and Kontorovich, A. (2024). A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes. In Proceedings of the 30th International Conference on Algorithmic Learning Theory (ALT). [pdf][official page][arXiv]

Hanneke, S., Kontorovich, A., and Sadigurschi, M. (2024). Sample Compression for Real-Valued Learners. In Proceedings of the 30th International Conference on Algorithmic Learning Theory (ALT). [pdf][official page][arXiv]

Hanneke, S. and Yang, L. (2024). Statistical Learning under Nonstationary Mixing Processes. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS). [pdf][official page][arXiv]

2018

Hanneke, S. and Yang, L. (2018). Testing Piecewise Functions. Theoretical Computer Science, Vol. 745, pp. 23-35. [pdf][ps][journal page]旋风vp(永久免费)

Zhivotovskiy, N. and Hanneke, S. (2018). Localization of VC Classes: Beyond Local Rademacher Complexities. Theoretical Computer Science, Vol. 742, pp. 27-49. [pdf][ps][journal page][arXiv]
(Special Issue for ALT 2016; Invited)

Hanneke, S., Kalai, A., Kamath, G., and Tzamos, C. (2018). Actively Avoiding Nonsense in Generative Models. In Proceedings of the 31st Annual Conference on Learning Theory (COLT). [pdf][official page][arXiv]

Yang, L., Hanneke, S., and Carbonell, J. (2018). Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks. Theoretical Computer Science, Vol. 716, pp. 124-140. [pdf][ps][journal page][arXiv]
(Special Issue for ALT 2015; Invited)

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Zhivotovskiy, N. and Hanneke, S. (2016). Localization of VC Classes: Beyond Local Rademacher Complexities. In Proceedings of the 27th International Conference on Algorithmic Learning Theory (ALT). [pdf][ps][arXiv]

Hanneke, S. (2016). 非法经营网络翻墙软件获利 兄弟俩领刑受罚-中国 ...:2021-4-4 · 中国法院网讯 (众超婵) 广西梧州市民曾某恒和弟弟曾某宁通过QQ、支付宝等方式，在网络上非法贩卖VPN（翻墙）软件，非法经营数额达40多万元。 日前，梧州市长洲区人民法院众犯非法经营罪，判处曾某恒、曾某宁有期徒刑八个月，并处罚金人民币一万元。. Journal of Machine Learning Research, Vol. 17 (135), pp. 1-55. [pdf][ps][arXiv][journal page]

Hanneke, S. (2016). The Optimal Sample Complexity of PAC Learning. Journal of Machine Learning Research, Vol. 17 (38), pp. 1-15. [pdf][ps]旋风vp(永久免费)[journal page]

2015

Hanneke, S. and Yang, L. (2015). Minimax Analysis of Active Learning. Journal of Machine Learning Research, Vol. 16 (12), pp. 3487-3602. [pdf][ps][arXiv]旋风vpn

Hanneke, S., Kanade, V., and Yang, L. (2015). Learning with a Drifting Target Concept. In Proceedings of the 26th International Conference on Algorithmic Learning Theory (ALT). [pdf]旋风vp下载[arXiv]
See also this note on a result for the sample complexity of efficient agnostic learning implicit in the above concept drift paper: [pdf]

Yang, L., Hanneke, S., and Carbonell, J. (2015). Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks. In Proceedings of the 26th International Conference on Algorithmic Learning Theory (ALT). [pdf]旋风vp官网[arXiv]

Wiener, Y., Hanneke, S., and El-Yaniv, R. (2015). 星光宝贝少儿才艺大赛在嘉兴旭辉广场刮起“才艺旋风”:2021-4-26 · 整治非法VPN、数据买卖等网络黑市，北京警方开展净网行动 25岁小伙2年突然长高15厘米，一下慌了神！医生：这是病，已错过最佳治疗期… 热搜第一！张文宏请大家做好心理准备，网友：好的好的都听你的 社评：新发现越来越多，看华盛顿如何演下去. Journal of Machine Learning Research, Vol. 16 (4), pp. 713-745. [pdf][ps][arXiv][journal page]

2014

Hanneke, S. (2014). Theory of Disagreement-Based Active Learning. Foundations and Trends in Machine Learning, Vol. 7 (2-3), pp. 131-309. [official] [Amazon] 
There is also an extended version, which I update from time to time.

2013

Yang, L. and Hanneke, S. (2013). 热门娱乐新闻图片推荐 - 国际在线 - CRI:热点新闻有趣图片，尽在国际在线娱乐频道。 2021年将播出近百部庆祝建党100周年主题电视剧. In Proceedings of the 30th International Conference on Machine Learning (ICML). [pdf][ps][appendix pdf]旋风加速器下载

Yang, L., Hanneke, S., and Carbonell, J. (2013). A Theory of Transfer Learning with Applications to Active Learning. Machine Learning, Vol. 90 (2), pp. 161-189. 旋风加速器ios下载旋风vp下载旋风加速器ios下载

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Balcan, M.-F. and Hanneke, S. (2012). Robust Interactive Learning. In Proceedings of the 25th Annual Conference on Learning Theory (COLT).[pdf]旋风vpn[arXiv]

Hanneke, S. (2012). Activized Learning: Transforming Passive to Active with Improved Label Complexity. Journal of Machine Learning Research, Vol. 13 (5), pp. 1469-1587. [pdf][ps][arXiv]旋风加速器ios下载
Related material: 旋风vp(永久免费), Chapter 4 in my thesis, and various presentations [slides][video].

2011

Yang, L., Hanneke, S., and Carbonell, J. (2011). Identifiability of Priors from Bounded Sample Sizes with Applications to Transfer Learning. In Proceedings of the 24th Annual Conference on Learning Theory (COLT).[pdf][ps][video]

Yang, L., Hanneke, S., and Carbonell, J. (2011). The Sample Complexity of Self-Verifying Bayesian Active Learning. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS).[pdf][ps]

Hanneke, S. (2011). China Radio International:• اذعان شرکت آمریکایی به سرعت چشمگیر احیای رشد اقتصادی چین اداره ملی آمار چین امروز دوشنبه برابر با 15 ژوئن، داده های مربوط به فعالیت اقتصادی ملی این کشور در ماه مه سال جاری میلادی را منتشر کرد؛ روی هم رفته، این داده ها .... The Annals of Statistics, Vol. 39 (1), pp. 333-361. [pdf][ps][journal page]

2010

Yang, L., Hanneke, S., and Carbonell, J. (2010). Bayesian Active Learning Using Arbitrary Binary Valued Queries. In Proceedings of the 21st International Conference on Algorithmic Learning Theory (ALT).[pdf][ps]
Also available in information theory jargon. [pdf][ps]

Hanneke, S., Fu, W., and Xing, E.P. (2010). Discrete Temporal Models of Social Networks. The Electronic Journal of Statistics, Vol. 4, pp. 585-605. 旋风vp官网[journal page]

Hanneke, S. and Yang, L. (2010). Negative Results for Active Learning with Convex Losses. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS). 旋风vp下载[ps]

Balcan, M.-F., Hanneke, S., and Wortman Vaughan, J. (2010). The True Sample Complexity of Active Learning. Machine Learning, Vol. 80 (2-3), pp. 111-139. 旋风vp官网[ps][journal page]
(Special Issue for COLT 2008; Invited)

2009

Hanneke, S. (2009). Theoretical Foundations of Active Learning. Doctoral Dissertation. Machine Learning Department. Carnegie Mellon University. [pdf][ps]旋风vpn

Hanneke, S. (2009). 浏览无界！ QQ浏览器iPad版全新升级打造极致浏览体验 ...:2021-6-15 · 另外，作为业内第一款支持沉浸式漫画阅读体验的浏览器，QQ浏览器iPad版去掉了引导用户下载APP的广告条，打造了无广告的纯净绿色阅读环境，让漫迷远离广告骚扰，专心享受漫画带来的乐趣。 In Proceedings of the 22nd Annual Conference on Learning Theory (COLT).[pdf][ps][slides]
Also available in expanded 旋风加速器ios下载.

Hanneke, S. and Xing, E.P. (2009). 微信近期大量封号是怎么回事？网友：触动了微信底线！_科技 ...:2021-12-28 · 原标题：微信近期大量封号是怎么回事？网友：触动了微信底线！微信封号，主要针对的是一些营销号、违规号。微信采用的是“系统自动策略”与“人工审核”两套审核机制。12月10日-25日，据说是历史上规模最大的一次封号，15天，1700万个微信号沦陷。 In Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS).旋风加速器ios下载[ps][slides]

2008

Balcan, M.-F., Hanneke, S., and Wortman, J. (2008). The True Sample Complexity of Active Learning. In Proceedings of the 21st Annual Conference on Learning Theory (COLT). 旋风vp(永久免费)[ps][slides]
Winner of the Mark Fulk Best Student Paper Award.
Also available in an extended journal version.

2007

Balcan, M.-F., Even-Dar, E., Hanneke, S., Kearns, M., Mansour, Y., and Wortman, J. (2007). Asymptotic Active Learning. NIPS Workshop on Principles of Learning Problem Design. [pdf][ps][spotlight slide]
Also available in improved 旋风加速器下载 and expanded journal version.

Hanneke, S. and Xing, E.P. (2007). Network Completion and Survey Sampling. NIPS Workshop on Statistical Network Models.
See our later conference publication.

Hanneke, S. (2007). Teaching Dimension and the Complexity of Active Learning. In proceedings of the 20th Annual Conference on Learning Theory (COLT). 旋风vp官网[ps]旋风加速器ios下载

Hanneke, S. (2007). 青春剧+竞技，不能只是换个噱头谈恋爱 - 娱乐 - 新京报网:2021-6-27 · 像2021年的《旋风少女》，是青春+跆拳道；2021年的《旋风十一人》，是青春+足球；2021年的《微微一笑很倾城》，是青春+电竞；2021年的《浪花一朵朵 ... In proceedings of the 24th Annual International Conference on Machine Learning (ICML). [pdf][ps][slides]
Honorable Mention for the ICML 2017 Test of Time Award.

Guo, F., Hanneke, S., Fu, W., and Xing, E.P. (2007). Recovering Temporally Rewiring Networks: A Model-based Approach. In proceedings of the 24th Annual International Conference on Machine Learning (ICML). [pdf]
Also see our related earlier work.

Hanneke, S. (2007). The Complexity of Interactive Machine Learning. KDD Project Report (aka Master's Thesis). Machine Learning Department, Carnegie Mellon University. [pdf][ps][slides]
Includes some interesting results from a class project on The Cost Complexity of Interactive Learning, in addition to my COLT07 and ICML07 papers.

2006

Hanneke, S. and Xing, E.P. (2006). Discrete Temporal Models of Social Networks. In Proceedings of the ICML Workshop on Statistical Network Analysis. 旋风vp(永久免费)旋风加速器下载[slides]
Also available in an extended journal version

Hanneke, S. (2006). An Analysis of Graph Cut Size for Transductive Learning. In Proceedings of the 23rd International Conference on Machine Learning (ICML). [pdf][ps][slides ppt][slides pdf]


Visitors since 11/14/2014.