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Found 29 papers, 1 papers with code
Online Estimation via Offline Estimation: An Information-Theoretic Framework
Our main results settle the statistical and computational complexity of online estimation in this framework.
Stochastic contextual bandits with graph feedback: from independence number to MAS number
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.
Covariance alignment: from maximum likelihood estimation to Gromov-Wasserstein
Feature alignment methods are used in many scientific disciplines for data pooling, annotation, and comparison.
Learning and Collusion in Multi-unit Auctions
We analyze the properties of this auction in both the offline and online settings.
Statistical Complexity and Optimal Algorithms for Non-linear Ridge Bandits
We consider the sequential decision-making problem where the mean outcome is a non-linear function of the chosen action.
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient
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.
Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions
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.
Beyond the Best: Estimating Distribution Functionals in Infinite-Armed Bandits
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.
Oracle-Efficient Online Learning for Beyond Worst-Case Adversaries
For the smoothed analysis setting, our results give the first oracle-efficient algorithm for online learning with smoothed adversaries [HRS22].
On the Statistical Complexity of Sample Amplification
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.
On the Value of Interaction and Function Approximation in Imitation Learning
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.
Provably Breaking the Quadratic Error Compounding Barrier in Imitation Learning, Optimally
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.
Adversarial Combinatorial Bandits with General Non-linear Reward Functions
{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.}
Integrated Gallium Nitride Nonlinear Photonics
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
Learning to Bid Optimally and Efficiently in Adversarial First-price Auctions
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.
Sequential Batch Learning in Finite-Action Linear Contextual Bandits
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.
Optimal No-regret Learning in Repeated First-price Auctions
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.
Domain Compression and its Application to Randomness-Optimal Distributed Goodness-of-Fit
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.
Batched Multi-armed Bandits Problem
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.
Lower Bounds for Learning Distributions under Communication Constraints via Fisher Information
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.
Local moment matching: A unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance
We present \emph{Local Moment Matching (LMM)}, a unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance.
Entropy Rate Estimation for Markov Chains with Large State Space
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 $\frac{S}{\log S}$.
The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal
We analyze the Kozachenko--Leonenko (KL) nearest neighbor estimator for the differential entropy.
On Estimation of $L_{r}$-Norms in Gaussian White Noise Models
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.
Bias Correction with Jackknife, Bootstrap, and Taylor Series
We analyze bias correction methods using jackknife, bootstrap, and Taylor series.
Estimating the Fundamental Limits is Easier than Achieving the Fundamental Limits
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.
Demystifying ResNet
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.
Beyond Maximum Likelihood: from Theory to Practice
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.
Avoiding False Positive in Multi-Instance Learning
In multi-instance learning, there are two kinds of prediction failure, i. e., false negative and false positive.
