The paper tackles contextual bandits with continuous actions using smoothing and zooming techniques.
problem Learning with continuous action spaces in the context of contextual bandits.
method The approach involves smoothing and zooming techniques to handle the continuous action space and unknown smoothness parameters.
result Improved regret bounds and adaptive algorithms for contextual bandits with continuous actions.
A multi-stage reinforcement learning method for object detection.
problem Efficiently detecting objects within images with high accuracy.
method Hierarchical tree-like region candidates, zoom and refinement stages, aspect ratio modification, multiple reward metrics.
result The multi-stage approach leads to more correct detections compared to single-stage methods.
We explore two techniques which use color to make sense of statistical text models. One method uses in-text annotations to illustrate a model's view of particular tokens in particular documents. Another uses a high-level, "words-as-pixels" graphic to display an entire corpus. Together, these methods offer both zoomed-i…
Study shows reinforcement learning algorithm's performance depends on metric space's size.
problem Continuous state and action spaces with metrics.
method Refined analysis of Sinclair et al. (2019) algorithm.
result Regret scales with the zooming dimension, a new parameter capturing near optimal actions.
The paper tackles multi-player information asymmetry bandits in metric spaces.
problem Information asymmetry in rewards, actions, or both in multiplayer bandit problems.
method Adopted CAB and zooming algorithms for information asymmetry in rewards and actions.
result Regret bounds of the same order in all 3 problem settings.
Improved adaptive rates for Lipschitz bandit problem.
problem Sequentially maximize an unknown Lipschitz function with noisy evaluations.
method Characterizes regret through integrals of suboptimality gaps over level sets, adapting to local growth.
result Improved adaptive rates strictly improving over classical zooming bounds.
New extensions for homogeneous distributions on deformations to the normal cone.
problem Extending homogeneous distributions on a specific geometric structure.
method Using the zoom action and Meyer's results on weakly homogeneous distributions.
result All homogeneous extensions of distributions on the DNC are described.
In this paper we propose the multi-objective contextual bandit problem with similarity information. This problem extends the classical contextual bandit problem with similarity information by introducing multiple and possibly conflicting objectives. Since the best arm in each objective can be different given the contex…
New algorithm improves efficiency and cost of noisy optimization.
problem Noisy optimization problems in expensive function evaluations.
method Tree-based zoom strategy for parallel surrogate optimization.
result ProSRS achieves faster convergence and lower cost compared to state-of-the-art algorithms.
Enhances Infomap algorithm to prioritize metadata in community detection.
problem Balancing metadata importance in community detection.
method Introduces a tuning parameter to the Infomap algorithm.
result Improves mutual information with metadata at the cost of structural detectability.
New algorithm tackles delayed feedback in Lipschitz bandits with sublinear regret.
problem Delayed feedback in Lipschitz bandits.
method Design of algorithms for bounded and unbounded stochastic delays.
result Sublinear regret guarantees for both bounded and unbounded delays.
The paper tackles high-dimensional Bayesian optimization using tree-structured additive models.
problem Scaling Bayesian Optimization to high-dimensional problems.
method Tree-structured additive models with hybrid graph learning and zooming-based algorithms.
result Demonstrates faster model learning and reduced model complexity in high-dimensional settings.
ZoomRL learns efficient strategies for large state-action spaces using a metric.
problem Handling large state-action spaces in reinforcement learning.
method ZoomRL leverages continuous bandits to adaptively discretize the joint space.
result Achieves worst-case regret of $ ilde{O}(H^{ rac{5}{2}} K^{ rac{d+1}{d+2}})$.
New method controls generative models with continuous factors.
problem Lack of control over generative models and poor understanding of latent space.
method Introduces a method to find meaningful directions in latent space for precise control of generated images.
result Demonstrates effectiveness of method for GANs and variational auto-encoders.
Automated video conferencing system improves user experience with ASD and VC.
problem Improve remote video conferencing experience through automated speaker detection and virtual cinematography.
method Uses 4K wide-FOV camera, depth camera, and microphone array to extract features and train machine learning models for ASD and VC.
result System performs within 0.3 MOS of an expert cinematographer, as rated by users.
Algorithm tackles adaptive discretization in adversarial Lipschitz bandits for dynamic pricing and auctions.
problem Adaptive discretization in adversarial Lipschitz bandits.
method Adversarial Zooming algorithm for adaptive discretization.
result First algorithm for adversarial Lipschitz bandits with instance-dependent regret bounds.
Cohomology fractals are visual representations of cohomology classes on hyperbolic 3-manifolds.
problem Visualizing cohomology classes on hyperbolic 3-manifolds.
method Cohomology fractals are images associated to cohomology classes. They are related to limit sets of Kleinian groups but differ in key aspects. An implementation using ideal triangulations and ray-casting is presented.
result Cohomology fractals allow for real-time zooming in any direction at arbitrary depth.
We describe a novel algorithm for noisy global optimisation and continuum-armed bandits, with good convergence properties over any continuous reward function having finitely many polynomial maxima. Over such functions, our algorithm achieves square-root regret in bandits, and inverse-square-root error in optimisation, …
In this paper we present a review of the existing typologies of Internet service users. We zoom in on social networking services including blogs and crowdsourcing websites. Based on the results of the analysis of the considered typologies obtained by means of FCA we developed a new user typology of a certain class of I…
Study shows how steepest descent algorithms' geometric margin increases during training.
problem Understanding implicit bias in steepest descent algorithms for neural networks.
method Analysis of steepest descent algorithms with infinitesimal learning rates in homogeneous neural networks.
result Limit points of training trajectories correspond to KKT points of margin-maximization problems.
New model allows sparse graphs with many triangles to be represented.
problem Sparse graphs with many triangles cannot be accurately represented in finite dimensions.
method Infinite-dimensional inner product model with manifold representations.
result Local neighborhoods can be represented in lower dimensions.
Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art results in many signal and image processing tasks. It is now well understood that the choice of the …
Network Lens identifies node behaviors in heterogeneous networks with high accuracy.
problem Identifying different behaviors in various parts of large heterogeneous networks.
method Zoom into network using different-sized lenses to capture local structure, weight signatures to predict node labels.
result Achieved a peak accuracy of ~42% on two networks with ~100,000 and ~1,000,000 nodes, significantly better than random.
We consider rough stochastic volatility models where the driving noise of volatility has fractional scaling, in the "rough" regime of Hurst parameter H<1/2. This regime recently attracted a lot of attention both from the statistical and option pricing point of view. With focus on the latter, we sharpen the large de…
Study shows how 'crowding' in equity trading affects performance and costs.
problem Deterioration of strategy performance, increased trading costs, and systemic risk due to equity factor crowding.
method Direct metrics of crowding based on imbalances of trades executed on the market, analyzing U.S. equity market data.
result Significant signs of crowding in well-known equity signals, especially Momentum, affecting order flow and portfolio rebalancing.
Probabilistic STNs improve image classification and robustness.
problem Training and robustness issues in STNs.
method Probabilistic extension of STNs that estimates stochastic transformations.
result Improved classification performance, robustness, and model calibration.
Recently, impressive denoising results have been achieved by Bayesian approaches which assume Gaussian models for the image patches. This improvement in performance can be attributed to the use of per-patch models. Unfortunately such an approach is particularly unstable for most inverse problems beyond denoising. In th…
Anti-correlated noise improves machine learning model generalization.
problem Improving machine learning model generalization.
method Injecting anticorrelated noise into gradient descent steps.
result Anti-correlated noise leads to better model generalization than uncorrelated noise.
We analyze the daily stock data of the Nasdaq Composite index in the 22-year period 1992-2013 and identify market states as clusters of correlation matrices with similar correlation structures. We investigate the stability of the correlation structure of each state by estimating the statistical fluctuations of correlat…
Requirements elicitation can be very challenging in projects that require deep domain knowledge about the system at hand. As analysts have the full control over the elicitation process, their lack of knowledge about the system under study inhibits them from asking related questions and reduces the accuracy of requireme…
ECG improves graph clustering and resolves resolution limit issues.
problem Graph clustering resolution limit issue.
method ECG uses consensus clustering to improve graph clustering.
result ECG alleviates the resolution limit issue and improves partition stability.
Study lenient regret and good-action identification in Gaussian process bandits.
problem Optimizing function values above a certain threshold in Gaussian process bandits.
method Study lenient regret notions and introduce algorithms for finding good actions.
result Upper and lower bounds on lenient regret for GP-UCB and elimination algorithms.
Simplifies large action space bandits by selecting representative actions.
problem Efficiently managing large action spaces with correlated outcomes.
method Random sampling and solving of bandit instances to identify representative actions.
result The algorithm selects a smaller set of representative actions that perform nearly as well as the full action space.
Unsupervised method discovers interpretable directions in GAN latent space.
problem Discovering interpretable directions in GAN latent space without supervision.
method Model-agnostic procedure to identify directions corresponding to semantic manipulations.
result Findings include directions for background removal and competitive saliency detection performance.
IDM learns clustered representations for complex FELs.
problem Learning meaningful representations for complex free energy landscapes.
method Information Distilling of Metastability (IDM) method.
result IDM achieves physically meaningful representations of FELs.
A new method corrects for bias in selecting the best candidate.
problem Bias in selecting the best candidate leads to invalid conclusions.
method Zoom correction, flexible for parametric and nonparametric settings.
result Valid inference on the winner is possible, even with selection bias.
RandQL is a new model-free algorithm for MDPs with a novel learning rate randomization approach.
problem Minimizing regret in episodic MDPs with model-free methods.
method Randomized Q-learning with learning rate randomization.
result RandQL achieves optimal regret bounds in both tabular and metric state-action spaces.
Lead/lag relationships are an important stylized fact at high frequency. Some assets follow the path of others with a small time lag. We provide indicators to measure this phenomenon using tick-by-tick data. Strongly asymmetric cross-correlation functions are empirically observed, especially in the future/stock case. W…
Algorithm learns diffusion processes with high-dimensional state spaces.
problem Stochastic control of unbounded diffusion processes with high-dimensional state spaces.
method Adaptive partitioning and learning algorithm that refines discretization based on estimation bias and statistical confidence.
result Established regret bounds that depend on problem parameters, extending to unbounded diffusion processes.
Analysis of histopathology slides is a critical step for many diagnoses, and in particular in oncology where it defines the gold standard. In the case of digital histopathological analysis, highly trained pathologists must review vast whole-slide-images of extreme digital resolution (100,0002 pixels) across multiple…
New normalizing flows for sphere distributions improve complexity and scale handling.
problem No straightforward normalizing flows for Fisher-Bingham distributions in higher dimensions.
method Zoom-linear-project (ZLP)-Fisher flows that gradually add complexity and handle varying scales.
result Generalizes Fisher-Bingham distributions to normalizing flows in any dimension.
IDF++ improves integer discrete flows for lossless compression.
problem Theoretical limitations of integer discrete flows for lossless compression.
method Investigated and improved integer discrete flows, addressing gradient bias and architecture modifications.
result Different architecture modifications improve integer discrete flows for lossless compression.
A new method trains deep networks by separating weight locations from values.
problem Training deep networks efficiently and effectively.
method Lookahead Permutation (LaPerm) to train DNNs by reconnecting weights.
result LaPerm can train DNNs with random and dense, sparse, or single-valued initial weights.
StyleNeRF generates high-resolution images with 3D consistency and style control.
problem Generating high-resolution images with fine details and 3D consistency.
method Integrates NeRF into a style-based generator for efficient high-resolution image synthesis.
result Synthesizes high-resolution images at interactive rates with high 3D consistency and style control.
We propose a novel methodology, forest floor, to visualize and interpret random forest (RF) models. RF is a popular and useful tool for non-linear multi-variate classification and regression, which yields a good trade-off between robustness (low variance) and adaptiveness (low bias). Direct interpretation of a RF model…
Paper argues context equals environment, improving AI generalization.
problem AI models struggle to generalize in new environments.
method In-Context Risk Minimization (ICRM) algorithm.
result ICRM leads to significant out-of-distribution performance improvements.
Machine learning separates Gaia DR2 stars into accreted and in-situ categories.
problem Identifying accreted stars from Gaia DR2 data using limited 5D kinematics.
method Transfer learning on mock Gaia catalogs, then cross-matched with real data.
result Identifies 767,000 accreted stars within Gaia DR2.
Unified analysis of tree-based methods for online reinforcement learning.
problem Designing efficient algorithms for online reinforcement learning with low sample complexity, storage, and computational burden.
method Unified theoretical analysis of tree-based hierarchical partitioning methods for online reinforcement learning.
result Our algorithms provide guarantees that scale with respect to the 'zooming dimension', improving upon ambient dimension scaling.