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Christopher Liaw
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2020 – today
- 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 - [i21]Zhe Feng, Christopher Liaw, Zixin Zhou:
Improved Online Learning Algorithms for CTR Prediction in Ad Auctions. CoRR abs/2403.00845 (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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