Search Results for author:
Found 34 papers, 10 papers with code
Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits
Assuming access to the distribution of the covariates, we propose a novel low-rank matrix estimation method called LowPopArt and provide its recovery guarantee that depends on a novel quantity denoted by B(Q) that characterizes the hardness of the problem, where Q is the covariance matrix of the measurement distribution.
Agnostic Interactive Imitation Learning: New Theory and Practical Algorithms
We study interactive imitation learning, where a learner interactively queries a demonstrating expert for action annotations, aiming to learn a policy that has performance competitive with the expert, using as few annotations as possible.
Efficient Active Learning Halfspaces with Tsybakov Noise: A Non-convex Optimization Approach
We study the problem of computationally and label efficient PAC active learning $d$-dimensional halfspaces with Tsybakov Noise~\citep{tsybakov2004optimal} under structured unlabeled data distributions.
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards
Maillard sampling \cite{maillard13apprentissage}, an attractive alternative to Thompson sampling, has recently been shown to achieve competitive regret guarantees in the sub-Gaussian reward setting \cite{bian2022maillard} while maintaining closed-form action probabilities, which is useful for offline policy evaluation.
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits
In this paper, we propose a simple and computationally efficient sparse linear estimation method called PopArt that enjoys a tighter $\ell_1$ recovery guarantee compared to Lasso (Tibshirani, 1996) in many problems.
On Efficient Online Imitation Learning via Classification
We make the following contributions: (1) we show that in the $\textbf{COIL}$ problem, any proper online learning algorithm cannot guarantee a sublinear regret in general; (2) we propose $\textbf{Logger}$, an improper online learning algorithmic framework, that reduces $\textbf{COIL}$ to online linear optimization, by utilizing a new definition of mixed policy class; (3) we design two oracle-efficient algorithms within the $\textbf{Logger}$ framework that enjoy different sample and interaction round complexity tradeoffs, and conduct finite-sample analyses to show their improvements over naive behavior cloning; (4) we show that under the standard complexity-theoretic assumptions, efficient dynamic regret minimization is infeasible in the $\textbf{Logger}$ framework.
Thompson Sampling for Robust Transfer in Multi-Task Bandits
We study the problem of online multi-task learning where the tasks are performed within similar but not necessarily identical multi-armed bandit environments.
Active Fairness Auditing
The fast spreading adoption of machine learning (ML) by companies across industries poses significant regulatory challenges.
Margin-distancing for safe model explanation
The growing use of machine learning models in consequential settings has highlighted an important and seemingly irreconcilable tension between transparency and vulnerability to gaming.
SIM-ECG: A Signal Importance Mask-driven ECGClassification System
Heart disease is the number one killer, and ECGs can assist in the early diagnosis and prevention of deadly outcomes.
Improving the trustworthiness of image classification models by utilizing bounding-box annotations
We study utilizing auxiliary information in training data to improve the trustworthiness of machine learning models.
Provably Efficient Multi-Task Reinforcement Learning with Model Transfer
We study multi-task reinforcement learning (RL) in tabular episodic Markov decision processes (MDPs).
Improved Algorithms for Efficient Active Learning Halfspaces with Massart and Tsybakov noise
We give a computationally-efficient PAC active learning algorithm for $d$-dimensional homogeneous halfspaces that can tolerate Massart noise (Massart and N\'ed\'elec, 2006) and Tsybakov noise (Tsybakov, 2004).
Multitask Bandit Learning Through Heterogeneous Feedback Aggregation
In many real-world applications, multiple agents seek to learn how to perform highly related yet slightly different tasks in an online bandit learning protocol.
Active Online Learning with Hidden Shifting Domains
We propose a surprisingly simple algorithm that adaptively balances its regret and its number of label queries in settings where the data streams are from a mixture of hidden domains.
Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality
In this paper, we focus on the finite hypothesis case and ask if one can achieve the asymptotic optimality while enjoying bounded regret whenever possible.
Active Online Domain Adaptation
We propose a surprisingly simple algorithm that adaptively balances its regret and its number of label queries in settings where the data streams are from a mixture of hidden domains.
Efficient Contextual Bandits with Continuous Actions
We create a computationally tractable algorithm for contextual bandits with continuous actions having unknown structure.
Attribute-Efficient Learning of Halfspaces with Malicious Noise: Near-Optimal Label Complexity and Noise Tolerance
We answer this question in the affirmative by designing a computationally efficient active learning algorithm with near-optimal label complexity of $\tilde{O}\big({s \log^4 \frac d \epsilon} \big)$ and noise tolerance $\eta = \Omega(\epsilon)$, where $\epsilon \in (0, 1)$ is the target error rate, under the assumption that the distribution over (uncorrupted) unlabeled examples is isotropic log-concave.
Efficient active learning of sparse halfspaces with arbitrary bounded noise
Even in the presence of mild label noise, i. e. $\eta$ is a small constant, this is a challenging problem and only recently have label complexity bounds of the form $\tilde{O}\big(s \cdot \mathrm{polylog}(d, \frac{1}{\epsilon})\big)$ been established in [Zhang, 2018] for computationally efficient algorithms.
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
We design a new algorithm for batch active learning with deep neural network models.
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case
Under the more challenging weak linear separability condition, we design an efficient algorithm with a mistake bound of $\min (2^{\widetilde{O}(K \log^2 (1/\gamma))}, 2^{\widetilde{O}(\sqrt{1/\gamma} \log K)})$.
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
We study contextual bandit learning with an abstract policy class and continuous action space.
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
We investigate the feasibility of learning from a mix of both fully-labeled supervised data and contextual bandit data.
Efficient active learning of sparse halfspaces
We study the problem of efficient PAC active learning of homogeneous linear classifiers (halfspaces) in $\mathbb{R}^d$, where the goal is to learn a halfspace with low error using as few label queries as possible.
Spectral Learning of Binomial HMMs for DNA Methylation Data
We consider learning parameters of Binomial Hidden Markov Models, which may be used to model DNA methylation data.
Efficient Online Bandit Multiclass Learning with O(sqrt{T}) Regret
An efficient bandit algorithm for $\sqrt{T}$-regret in online multiclass prediction?
Efficient Online Bandit Multiclass Learning with $\tilde{O}(\sqrt{T})$ Regret
The regret bound holds simultaneously with respect to a family of loss functions parameterized by $\eta$, for a range of $\eta$ restricted by the norm of the competitor.
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces
It has been a long-standing problem to efficiently learn a halfspace using as few labels as possible in the presence of noise.
The Extended Littlestone's Dimension for Learning with Mistakes and Abstentions
In this paper, we address both challenges.
Search Improves Label for Active Learning
We investigate active learning with access to two distinct oracles: Label (which is standard) and Search (which is not).
Active Learning from Weak and Strong Labelers
This work addresses active learning with labels obtained from strong and weak labelers, where in addition to the standard active learning setting, we have an extra weak labeler which may occasionally provide incorrect labels.
Spectral Learning of Large Structured HMMs for Comparative Epigenomics
We develop a latent variable model and an efficient spectral algorithm motivated by the recent emergence of very large data sets of chromatin marks from multiple human cell types.
Beyond Disagreement-based Agnostic Active Learning
We study agnostic active learning, where the goal is to learn a classifier in a pre-specified hypothesis class interactively with as few label queries as possible, while making no assumptions on the true function generating the labels.
