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ORCIDChristopher Liaw
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2020 – today
- 2025
[c22]Mohammad Afzali, Hassan Ashtiani, Christopher Liaw:
Agnostic Private Density Estimation for GMMs via List Global Stability. ALT 2025: 41-66- [i24]Kshipra Bhawalkar, Jeff Dean, Christopher Liaw, Aranyak Mehta, Neel Patel:
Equilibria and Learning in Modular Marketplaces. CoRR abs/2502.20346 (2025) - 2024
- [j6]Nicholas J. A. Harvey, Christopher Liaw, Victor S. Portella:
Continuous Prediction with Experts' Advice. J. Mach. Learn. Res. 25: 228:1-228:32 (2024) - [j5]Nicholas J. A. Harvey, Chris Liaw, Sikander Randhawa:
Tight analyses for subgradient descent I: Lower bounds. Open J. Math. Optim. 5: 1-17 (2024) - [j4]Gagan Aggarwal, Ashwinkumar Badanidiyuru, Santiago R. Balseiro, Kshipra Bhawalkar, Yuan Deng, Zhe Feng, Gagan Goel, Christopher Liaw, Haihao Lu, Mohammad Mahdian, Jieming Mao, Aranyak Mehta, Vahab Mirrokni, Renato Paes Leme, Andrés Perlroth, Georgios Piliouras, Jon Schneider, Ariel Schvartzman, Balasubramanian Sivan, Kelly Spendlove, Yifeng Teng, Di Wang, Hanrui Zhang, Mingfei Zhao, Wennan Zhu, Song Zuo:
Auto-Bidding and Auctions in Online Advertising: A Survey. SIGecom Exch. 22(1): 159-183 (2024) - [c21]Mohammad Afzali, Hassan Ashtiani, Christopher Liaw:
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples. ALT 2024: 47-73 - [c20]Yang Cai
, Christopher Liaw, Aranyak Mehta, Mingfei Zhao:
The Power of Two-Sided Recruitment in Two-Sided Markets. STOC 2024: 201-212 - [c19]Yang Cai, Zhe Feng, Christopher Liaw, Aranyak Mehta, Grigoris Velegkas:
User Response in Ad Auctions: An MDP Formulation of Long-term Revenue Optimization. WWW 2024: 111-122 - [c18]Christopher Liaw, Aranyak Mehta, Wennan Zhu:
Efficiency of Non-Truthful Auctions in Auto-bidding with Budget Constraints. WWW 2024: 223-234 - [i23]Zhe Feng, Christopher Liaw, Zixin Zhou:
Improved Online Learning Algorithms for CTR Prediction in Ad Auctions. CoRR abs/2403.00845 (2024) - [i22]Mohammad Afzali, Hassan Ashtiani, Christopher Liaw:
Agnostic Private Density Estimation via Stable List Decoding. CoRR abs/2407.04783 (2024) - [i21]Gagan Aggarwal, Ashwinkumar Badanidiyuru, Santiago R. Balseiro, Kshipra Bhawalkar, Yuan Deng, Zhe Feng, Gagan Goel, Christopher Liaw, Haihao Lu, Mohammad Mahdian, Jieming Mao, Aranyak Mehta, Vahab Mirrokni, Renato Paes Leme, Andrés Perlroth, Georgios Piliouras, Jon Schneider, Ariel Schvartzman, Balasubramanian Sivan, Kelly Spendlove, Yifeng Teng, Di Wang, Hanrui Zhang, Mingfei Zhao, Wennan Zhu, Song Zuo:
Auto-bidding and Auctions in Online Advertising: A Survey. CoRR abs/2408.07685 (2024) - 2023
- [c17]Jamil Arbas, Hassan Ashtiani, Christopher Liaw:
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models. ICML 2023: 1018-1040 - [c16]Zhe Feng, Christopher Liaw, Zixin Zhou:
Improved Online Learning Algorithms for CTR Prediction in Ad Auctions. ICML 2023: 9921-9937 - [c15]Christopher Liaw, Aranyak Mehta, Andrés Perlroth:
Efficiency of Non-Truthful Auctions in Auto-bidding: The Power of Randomization. WWW 2023: 3561-3571 - [i20]Yang Cai, Zhe Feng, Christopher Liaw, Aranyak Mehta:
User Response in Ad Auctions: An MDP Formulation of Long-Term Revenue Optimization. CoRR abs/2302.08108 (2023) - [i19]Jamil Arbas, Hassan Ashtiani, Christopher Liaw:
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models. CoRR abs/2303.04288 (2023) - [i18]Yang Cai, Christopher Liaw, Aranyak Mehta, Mingfei Zhao:
The Power of Two-sided Recruitment in Two-sided Markets. CoRR abs/2307.03844 (2023) - [i17]Mohammad Afzali, Hassan Ashtiani, Christopher Liaw:
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples. CoRR abs/2309.03847 (2023) - [i16]Christopher Liaw, Aranyak Mehta, Wennan Zhu:
Efficiency of Non-Truthful Auctions in Auto-bidding with Budget Constraints. CoRR abs/2310.09271 (2023) - 2022
- [c14]Hassan Ashtiani, Christopher Liaw:
Private and polynomial time algorithms for learning Gaussians and beyond. COLT 2022: 1075-1076 - [i15]Victor Sanches Portella, Christopher Liaw, Nicholas J. A. Harvey:
Continuous Prediction with Experts' Advice. CoRR abs/2206.00236 (2022) - [i14]Christopher Liaw, Aranyak Mehta, Andrés Perlroth:
Efficiency of non-truthful auctions under auto-bidding. CoRR abs/2207.03630 (2022) - 2021
- [c13]Zhe Feng, Guru Guruganesh, Christopher Liaw, Aranyak Mehta, Abhishek Sethi:
Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions. AAAI 2021: 5399-5406 - [c12]Ishaq Aden-Ali, Hassan Ashtiani, Christopher Liaw:
Privately Learning Mixtures of Axis-Aligned Gaussians. NeurIPS 2021: 3925-3938 - [i13]Ishaq Aden-Ali, Hassan Ashtiani, Christopher Liaw:
Privately Learning Mixtures of Axis-Aligned Gaussians. CoRR abs/2106.02162 (2021) - [i12]Hassan Ashtiani, Christopher Liaw:
Private and polynomial time algorithms for learning Gaussians and beyond. CoRR abs/2111.11320 (2021) - 2020
- [j3]Hassan Ashtiani, Shai Ben-David, Nicholas J. A. Harvey, Christopher Liaw, Abbas Mehrabian, Yaniv Plan:
Near-optimal Sample Complexity Bounds for Robust Learning of Gaussian Mixtures via Compression Schemes. J. ACM 67(6): 32:1-32:42 (2020) - [c11]Nicholas J. A. Harvey, Christopher Liaw, Edwin A. Perkins, Sikander Randhawa:
Optimal anytime regret for two experts. FOCS 2020: 1404-1415 - [c10]Nicholas J. A. Harvey, Christopher Liaw, Tasuku Soma:
Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds. NeurIPS 2020 - [i11]Nicholas J. A. Harvey, Christopher Liaw, Edwin A. Perkins, Sikander Randhawa:
Optimal anytime regret with two experts. CoRR abs/2002.08994 (2020) - [i10]Zhe Feng, Guru Guruganesh, Christopher Liaw, Aranyak Mehta, Abhishek Sethi:
Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions. CoRR abs/2009.06136 (2020)
2010 – 2019
- 2019
- [j2]Peter L. Bartlett, Nick Harvey, Christopher Liaw, Abbas Mehrabian:
Nearly-tight VC-dimension and Pseudodimension Bounds for Piecewise Linear Neural Networks. J. Mach. Learn. Res. 20: 63:1-63:17 (2019) - [c9]Nicholas J. A. Harvey, Christopher Liaw, Yaniv Plan, Sikander Randhawa:
Tight analyses for non-smooth stochastic gradient descent. COLT 2019: 1579-1613 - [c8]Hu Fu, Christopher Liaw, Sikander Randhawa:
The Vickrey Auction with a Single Duplicate Bidder Approximates the Optimal Revenue. EC 2019: 419-420 - [c7]Weiwei Kong, Christopher Liaw, Aranyak Mehta, D. Sivakumar:
A new dog learns old tricks: RL finds classic optimization algorithms. ICLR (Poster) 2019 - [i9]Hu Fu, Christopher Liaw, Sikander Randhawa:
The Vickrey Auction with a Single Duplicate Bidder Approximates the Optimal Revenue. CoRR abs/1905.03773 (2019) - [i8]Nicholas J. A. Harvey, Christopher Liaw, Sikander Randhawa:
Simple and optimal high-probability bounds for strongly-convex stochastic gradient descent. CoRR abs/1909.00843 (2019) - 2018
- [c6]Christopher Liaw, Paul Liu, Robert Reiss:
Approximation Schemes for Covering and Packing in the Streaming Model. CCCG 2018: 172-179 - [c5]Hassan Ashtiani, Shai Ben-David, Nicholas J. A. Harvey, Christopher Liaw, Abbas Mehrabian, Yaniv Plan:
Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes. NeurIPS 2018: 3416-3425 - [c4]Hu Fu, Christopher Liaw, Pinyan Lu, Zhihao Gavin Tang:
The Value of Information Concealment. SODA 2018: 2533-2544 - [c3]Nicholas J. A. Harvey, Christopher Liaw, Paul Liu:
Greedy and Local Ratio Algorithms in the MapReduce Model. SPAA 2018: 43-52 - [i7]Nicholas J. A. Harvey, Christopher Liaw, Paul Liu:
Greedy and Local Ratio Algorithms in the MapReduce Model. CoRR abs/1806.06421 (2018) - [i6]Nicholas J. A. Harvey, Christopher Liaw, Yaniv Plan, Sikander Randhawa:
Tight Analyses for Non-Smooth Stochastic Gradient Descent. CoRR abs/1812.05217 (2018) - 2017
- [j1]Nicholas J. A. Harvey, Christopher Liaw:
Rainbow Hamilton cycles and lopsidependency. Discret. Math. 340(6): 1261-1270 (2017) - [c2]Nick Harvey, Christopher Liaw, Abbas Mehrabian:
Nearly-tight VC-dimension bounds for piecewise linear neural networks. COLT 2017: 1064-1068 - [c1]Petra Berenbrink, Peter Kling, Christopher Liaw, Abbas Mehrabian:
Tight Load Balancing Via Randomized Local Search. IPDPS 2017: 192-201 - [i5]Nick Harvey, Christopher Liaw, Abbas Mehrabian:
Nearly-tight VC-dimension bounds for piecewise linear neural networks. CoRR abs/1703.02930 (2017) - [i4]Christopher Liaw, Paul Liu, Robert Reiss:
Approximation Schemes for Covering and Packing in the Streaming Model. CoRR abs/1706.09533 (2017) - [i3]Petra Berenbrink, Peter Kling, Christopher Liaw, Abbas Mehrabian:
Tight Load Balancing via Randomized Local Search. CoRR abs/1706.09997 (2017) - [i2]Hu Fu, Chris Liaw, Pinyan Lu, Zhihao Gavin Tang:
The Value of Information Concealment. CoRR abs/1707.05875 (2017) - 2016
- [i1]Christopher Liaw, Abbas Mehrabian, Yaniv Plan, Roman Vershynin:
A simple tool for bounding the deviation of random matrices on geometric sets. CoRR abs/1603.00897 (2016)
Coauthor Index
aka: Nick Harvey
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