no code implementations • 15 Apr 2024 • Dylan J. Foster, Yanjun Han, Jian Qian, Alexander Rakhlin
Our main results settle the statistical and computational complexity of online estimation in this framework.
no code implementations • 12 Feb 2024 • Yuxiao Wen, Yanjun Han, Zhengyuan Zhou
Interestingly, $\beta_M(G)$ interpolates between $\alpha(G)$ (the independence number of the graph) and $\mathsf{m}(G)$ (the maximum acyclic subgraph (MAS) number of the graph) as the number of contexts $M$ varies.
no code implementations • 22 Nov 2023 • Yanjun Han, Philippe Rigollet, George Stepaniants
Feature alignment methods are used in many scientific disciplines for data pooling, annotation, and comparison.
no code implementations • NeurIPS 2023 • Simina Brânzei, Mahsa Derakhshan, Negin Golrezaei, Yanjun Han
We analyze the properties of this auction in both the offline and online settings.
no code implementations • 12 Feb 2023 • Nived Rajaraman, Yanjun Han, Jiantao Jiao, Kannan Ramchandran
We consider the sequential decision-making problem where the mean outcome is a non-linear function of the chosen action.
no code implementations • 19 Jan 2023 • Dylan J. Foster, Noah Golowich, Yanjun Han
Recently, Foster et al. (2021) introduced the Decision-Estimation Coefficient (DEC), a measure of statistical complexity which leads to upper and lower bounds on the optimal sample complexity for a general class of problems encompassing bandits and reinforcement learning with function approximation.
no code implementations • 5 Nov 2022 • Wei zhang, Yanjun Han, Zhengyuan Zhou, Aaron Flores, Tsachy Weissman
In the past four years, a particularly important development in the digital advertising industry is the shift from second-price auctions to first-price auctions for online display ads.
no code implementations • 1 Nov 2022 • Yifei Wang, Tavor Baharav, Yanjun Han, Jiantao Jiao, David Tse
In the infinite-armed bandit problem, each arm's average reward is sampled from an unknown distribution, and each arm can be sampled further to obtain noisy estimates of the average reward of that arm.
no code implementations • 17 Feb 2022 • Nika Haghtalab, Yanjun Han, Abhishek Shetty, Kunhe Yang
For the smoothed analysis setting, our results give the first oracle-efficient algorithm for online learning with smoothed adversaries [HRS22].
no code implementations • 12 Jan 2022 • Brian Axelrod, Shivam Garg, Yanjun Han, Vatsal Sharan, Gregory Valiant
In this work, we place the sample amplification problem on a firm statistical foundation by deriving generally applicable amplification procedures, lower bound techniques and connections to existing statistical notions.
no code implementations • NeurIPS 2021 • Nived Rajaraman, Yanjun Han, Lin Yang, Jingbo Liu, Jiantao Jiao, Kannan Ramchandran
In contrast, when the MDP transition structure is known to the learner such as in the case of simulators, we demonstrate fundamental differences compared to the tabular setting in terms of the performance of an optimal algorithm, Mimic-MD (Rajaraman et al. (2020)) when extended to the function approximation setting.
no code implementations • 25 Feb 2021 • Nived Rajaraman, Yanjun Han, Lin F. Yang, Kannan Ramchandran, Jiantao Jiao
We establish an upper bound $O(|\mathcal{S}|H^{3/2}/N)$ for the suboptimality using the Mimic-MD algorithm in Rajaraman et al (2020) which we prove to be computationally efficient.
no code implementations • 5 Jan 2021 • Xi Chen, Yanjun Han, Yining Wang
{The adversarial combinatorial bandit with general non-linear reward is an important open problem in bandit literature, and it is still unclear whether there is a significant gap from the case of linear reward, stochastic bandit, or semi-bandit feedback.}
no code implementations • 30 Oct 2020 • Yanzhen Zheng, Changzheng Sun, Bing Xiong, Lai Wang, Zhibiao Hao, Jian Wang, Yanjun Han, Hongtao Li, Jiadong Yu, Yi Luo
Thanks to its high nonlinearity and high refractive index contrast, GaN-on-insulator (GaNOI) is also a promising platform for nonlinear optical applications.
Optics Applied Physics
no code implementations • 9 Jul 2020 • Yanjun Han, Zhengyuan Zhou, Aaron Flores, Erik Ordentlich, Tsachy Weissman
In this paper, we take an online learning angle and address the fundamental problem of learning to bid in repeated first-price auctions, where both the bidder's private valuations and other bidders' bids can be arbitrary.
no code implementations • 14 Apr 2020 • Yanjun Han, Zhengqing Zhou, Zhengyuan Zhou, Jose Blanchet, Peter W. Glynn, Yinyu Ye
We study the sequential batch learning problem in linear contextual bandits with finite action sets, where the decision maker is constrained to split incoming individuals into (at most) a fixed number of batches and can only observe outcomes for the individuals within a batch at the batch's end.
no code implementations • 22 Mar 2020 • Yanjun Han, Zhengyuan Zhou, Tsachy Weissman
In this paper, we develop the first learning algorithm that achieves a near-optimal $\widetilde{O}(\sqrt{T})$ regret bound, by exploiting two structural properties of first-price auctions, i. e. the specific feedback structure and payoff function.
no code implementations • 20 Jul 2019 • Jayadev Acharya, Clément L. Canonne, Yanjun Han, Ziteng Sun, Himanshu Tyagi
We study goodness-of-fit of discrete distributions in the distributed setting, where samples are divided between multiple users who can only release a limited amount of information about their samples due to various information constraints.
1 code implementation • NeurIPS 2019 • Zijun Gao, Yanjun Han, Zhimei Ren, Zhengqing Zhou
While the minimax regret for the two-armed stochastic bandits has been completely characterized in \cite{perchet2016batched}, the effect of the number of arms on the regret for the multi-armed case is still open.
no code implementations • 7 Feb 2019 • Leighton Pate Barnes, Yanjun Han, Ayfer Ozgur
We consider the problem of learning high-dimensional, nonparametric and structured (e. g. Gaussian) distributions in distributed networks, where each node in the network observes an independent sample from the underlying distribution and can use $k$ bits to communicate its sample to a central processor.
no code implementations • 23 Feb 2018 • Yanjun Han, Jiantao Jiao, Tsachy Weissman
We present \emph{Local Moment Matching (LMM)}, a unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance.
no code implementations • NeurIPS 2018 • Yanjun Han, Jiantao Jiao, Chuan-Zheng Lee, Tsachy Weissman, Yihong Wu, Tiancheng Yu
For estimating the Shannon entropy of a distribution on $S$ elements with independent samples, [Paninski2004] showed that the sample complexity is sublinear in $S$, and [Valiant--Valiant2011] showed that consistent estimation of Shannon entropy is possible if and only if the sample size $n$ far exceeds $\frac{S}{\log S}$.
no code implementations • NeurIPS 2018 • Jiantao Jiao, Weihao Gao, Yanjun Han
We analyze the Kozachenko--Leonenko (KL) nearest neighbor estimator for the differential entropy.
no code implementations • 11 Oct 2017 • Yanjun Han, Jiantao Jiao, Rajarshi Mukherjee
We provide a complete picture of asymptotically minimax estimation of $L_r$-norms (for any $r\ge 1$) of the mean in Gaussian white noise model over Nikolskii-Besov spaces.
no code implementations • 18 Sep 2017 • Jiantao Jiao, Yanjun Han
We analyze bias correction methods using jackknife, bootstrap, and Taylor series.
no code implementations • 5 Jul 2017 • Jiantao Jiao, Yanjun Han, Irena Fischer-Hwang, Tsachy Weissman
We show through case studies that it is easier to estimate the fundamental limits of data processing than to construct explicit algorithms to achieve those limits.
no code implementations • 3 Nov 2016 • Sihan Li, Jiantao Jiao, Yanjun Han, Tsachy Weissman
We show that with or without nonlinearities, by adding shortcuts that have depth two, the condition number of the Hessian of the loss function at the zero initial point is depth-invariant, which makes training very deep models no more difficult than shallow ones.
no code implementations • 26 Sep 2014 • Jiantao Jiao, Kartik Venkat, Yanjun Han, Tsachy Weissman
In a nutshell, a message of this recent work is that, for a wide class of functionals, the performance of these essentially optimal estimators with $n$ samples is comparable to that of the MLE with $n \ln n$ samples.
no code implementations • NeurIPS 2010 • Yanjun Han, Qing Tao, Jue Wang
In multi-instance learning, there are two kinds of prediction failure, i. e., false negative and false positive.