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Found 34 papers, 5 papers with code
Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning
We present a novel multimodal preference dataset for creative tasks, consisting of over 250 million human ratings on more than 2. 2 million captions, collected through crowdsourcing rating data for The New Yorker's weekly cartoon caption contest over the past eight years.
Adaptive Experimentation When You Can't Experiment
Our methodology provides online services with an adaptive experimental design approach for learning the best-performing treatment for such \textit{encouragement designs}.
Off-Policy Evaluation from Logged Human Feedback
Learning from human feedback has been central to recent advances in artificial intelligence and machine learning.
Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice
Adaptive experimental design (AED) methods are increasingly being used in industry as a tool to boost testing throughput or reduce experimentation cost relative to traditional A/B/N testing methods.
DIRECT: Deep Active Learning under Imbalance and Label Noise
Our results demonstrate that DIRECT can save more than 60% of the annotation budget compared to state-of-art active learning algorithms and more than 80% of annotation budget compared to random sampling.
Fair Active Learning in Low-Data Regimes
In this work, we address the challenges of reducing bias and improving accuracy in data-scarce environments, where the cost of collecting labeled data prohibits the use of large, labeled datasets.
Pessimistic Off-Policy Multi-Objective Optimization
The pessimistic estimator can be optimized by policy gradients and performs well in all of our experiments.
Minimax Optimal Submodular Optimization with Bandit Feedback
The objective is to minimize the learner's regret over $T$ times with respect to ($1-e^{-1}$)-approximation of maximum $f(S_*)$ with $|S_*| = k$, obtained through greedy maximization of $f$.
Optimal Exploration is no harder than Thompson Sampling
In this work, we pose a natural question: is there an algorithm that can explore optimally and only needs the same computational primitives as Thompson Sampling?
A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity
For robust identification, it is well-known that if arms are chosen randomly and non-adaptively from a G-optimal design over $\mathcal{X}$ at each time then the error probability decreases as $\exp(-T\Delta^2_{(1)}/d)$, where $\Delta_{(1)} = \min_{x \neq x^*} (x^* - x)^\top \frac{1}{T}\sum_{t=1}^T \theta_t$.
Adaptive Experimental Design and Counterfactual Inference
Adaptive experimental design methods are increasingly being used in industry as a tool to boost testing throughput or reduce experimentation cost relative to traditional A/B/N testing methods.
Instance-optimal PAC Algorithms for Contextual Bandits
In the stochastic contextual bandit setting, regret-minimizing algorithms have been extensively researched, but their instance-minimizing best-arm identification counterparts remain seldom studied.
Active Learning with Safety Constraints
To our knowledge, our results are the first on best-arm identification in linear bandits with safety constraints.
An Experimental Design Approach for Regret Minimization in Logistic Bandits
Finally, we discuss the impact of the bias of the MLE on the logistic bandit problem, providing an example where $d^2$ lower order regret (cf., it is $d$ for linear bandits) may not be improved as long as the MLE is used and how bias-corrected estimators may be used to make it closer to $d$.
Nearly Optimal Algorithms for Level Set Estimation
The threshold value $\alpha$ can either be \emph{explicit} and provided a priori, or \emph{implicit} and defined relative to the optimal function value, i. e. $\alpha = (1-\epsilon)f(x_\ast)$ for a given $\epsilon > 0$ where $f(x_\ast)$ is the maximal function value and is unknown.
Selective Sampling for Online Best-arm Identification
The main results of this work precisely characterize this trade-off between labeled samples and stopping time and provide an algorithm that nearly-optimally achieves the minimal label complexity given a desired stopping time.
Improved Algorithms for Agnostic Pool-based Active Classification
We consider active learning for binary classification in the agnostic pool-based setting.
Finding All $\epsilon$-Good Arms in Stochastic Bandits
The pure-exploration problem in stochastic multi-armed bandits aims to find one or more arms with the largest (or near largest) means.
Improved Confidence Bounds for the Linear Logistic Model and Applications to Linear Bandits
Specifically, our confidence bound avoids a direct dependence on $1/\kappa$, where $\kappa$ is the minimal variance over all arms' reward distributions.
Learning to Actively Learn: A Robust Approach
Unlike the design of traditional adaptive algorithms that rely on concentration of measure and careful analysis to justify the correctness and sample complexity of the procedure, our adaptive algorithm is learned via adversarial training over equivalence classes of problems derived from information theoretic lower bounds.
A New Perspective on Pool-Based Active Classification and False-Discovery Control
In many scientific settings there is a need for adaptive experimental design to guide the process of identifying regions of the search space that contain as many true positives as possible subject to a low rate of false discoveries (i. e. false alarms).
Spectral Methods for Ranking with Scarce Data
In this paper we modify a popular and well studied method, RankCentrality for rank aggregation to account for few comparisons and that incorporates additional feature information.
An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits
This paper proposes near-optimal algorithms for the pure-exploration linear bandit problem in the fixed confidence and fixed budget settings.
Finding All ε-Good Arms in Stochastic Bandits
Mathematically, the all-{\epsilon}-good arm identification problem presents significant new challenges and surprises that do not arise in the pure-exploration objectives studied in the past.
Sequential Experimental Design for Transductive Linear Bandits
Such a transductive setting naturally arises when the set of measurement vectors is limited due to factors such as availability or cost.
Convergence rates for ordinal embedding
We prove optimal bounds for the convergence rate of ordinal embedding (also known as non-metric multidimensional scaling) in the 1-dimensional case.
A Bandit Approach to Sequential Experimental Design with False Discovery Control
We propose a new adaptive sampling approach to multiple testing which aims to maximize statistical power while ensuring anytime false discovery control.
A Bandit Approach to Multiple Testing with False Discovery Control
We propose an adaptive sampling approach for multiple testing which aims to maximize statistical power while ensuring anytime false discovery control.
Firing Bandits: Optimizing Crowdfunding
In this paper, we model the problem of optimizing crowdfunding platforms, such as the non-profit Kiva or for-profit KickStarter, as a variant of the multi-armed bandit problem.
Adaptive Sampling for Coarse Ranking
We consider the problem of active coarse ranking, where the goal is to sort items according to their means into clusters of pre-specified sizes, by adaptively sampling from their reward distributions.
If it ain't broke, don't fix it: Sparse metric repair
The distances between the data points are far from satisfying a metric.
Learning Low-Dimensional Metrics
This paper investigates the theoretical foundations of metric learning, focused on three key questions that are not fully addressed in prior work: 1) we consider learning general low-dimensional (low-rank) metrics as well as sparse metrics; 2) we develop upper and lower (minimax)bounds on the generalization error; 3) we quantify the sample complexity of metric learning in terms of the dimension of the feature space and the dimension/rank of the underlying metric;4) we also bound the accuracy of the learned metric relative to the underlying true generative metric.
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding
First, we derive prediction error bounds for ordinal embedding with noise by exploiting the fact that the rank of a distance matrix of points in $\mathbb{R}^d$ is at most $d+2$.
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning
Active learning methods automatically adapt data collection by selecting the most informative samples in order to accelerate machine learning.
