Paper presents efficient algorithms for constructing confidence intervals in algorithmic leveraging.
problem Efficiently constructing confidence intervals for algorithmic leveraging regression coefficients.
method Developed efficient algorithms for finite sample confidence intervals.
result Confidence intervals have the desired coverage probabilities, outperforming bootstrap methods.
One popular method for dealing with large-scale data sets is sampling. For example, by using the empirical statistical leverage scores as an importance sampling distribution, the method of algorithmic leveraging samples and rescales rows/columns of data matrices to reduce the data size before performing computations on…
New algorithms estimate matrix leverage scores using rank revealing and randomization.
problem Estimating leverage scores for matrices of arbitrary rank.
method Combining rank revealing methods with randomized dimensionality reduction.
result Effective estimators for leverage scores, even in rank deficient cases.
Paper develops efficient methods for leverage score sampling and kernel ridge regression.
problem Efficiently sampling leverage scores for large matrices.
method Novel algorithm for leverage score sampling and kernel ridge regression solver.
result Proposed algorithms are the most efficient and accurate for leverage score sampling and kernel ridge regression.
Deterministic column sampling using ridge leverage scores provides accurate matrix sketches for ridge regression.
problem Regularizing ill-posed linear least-squares problems with small but non-zero coefficients.
method Deterministic column sampling using ridge leverage scores.
result Deterministic algorithm provides (1 + ε) error column subset selection and projection-cost preservation.
We model leverage as stochastic but independent of return shocks and of volatility and perform likelihood-based inference via the recently developed iterated filtering algorithm using S&P500 data, contributing new evidence to the still slim empirical support for random leverage variation.
SALSA efficiently approximates leverage scores for big data, improving ARMA model fitting.
problem Efficiently approximating leverage scores for large matrices.
method Sequential approximate leverage-score algorithm (SALSA) using randomized numerical linear algebra.
result SALSA approximates leverage scores within (1+O(ε)) with high probability. New Bitcoin coin selection method improves cost savings.
problem Improving cost savings in Bitcoin coin selection.
method Coin selection with leverage, allowing user-tunable parameters.
result Natural replacement for standard knapsack method.
Sketched SVD improves SVD runtime for large datasets.
problem Efficiently applying SVD to large datasets.
method Randomized sketching to approximate SVD.
result Sketched SVD provides accurate leverage score ordering.
A new robust PCA method uses Innovation Search and Leverage Scores.
problem Outlier detection and robust PCA in data clustering.
method Innovation Search and Leverage Scores.
result The method provides theoretical guarantees and outperforms existing algorithms.
LSAR efficiently estimates AR models for big time series data.
problem Efficiently analyzing large-scale time series data with high accuracy.
method Developed a fast algorithm to estimate leverage scores and an efficient LSAR algorithm for fitting AR models.
result LSAR algorithm finds maximum likelihood estimates with high probability and improved worst-case running time.
A new algorithm for efficient kernel Nyström approximation.
problem Efficiently approximating large kernel matrices for machine learning.
method Recursive sampling of landmark points using ridge leverage scores.
result Scalable and accurate kernel approximation with linear runtime.
New framework uses user feedback in CB problems for better decision-making.
problem Improving decision-making in contextual bandit problems with user-triggered feedback.
method Developed a new framework to leverage user-triggered feedback in CB problems, robust to feedback bias.
result Improved regret guarantees for CB algorithms using user feedback.
New algorithms improve Bayesian inference for SV models with leverage.
problem Efficient Bayesian estimation of SV models with leverage.
method Derive novel algorithms for centered and non-centered parameterizations, combine samplers using ASIS.
result Stable sampling efficiency irrespective of parameterization.
New algorithm samples matrix rows proportional to their ℓ_p norm in a turnstile data stream.
problem Sampling rows of a dynamic matrix efficiently in a turnstile data stream.
method Develops a novel algorithm for sampling rows proportional to their ℓ_p norm in a turnstile data stream, returning sampled row indexes and approximated sampling probabilities.
result Achieves (1+ε) approximation for logistic regression in a turnstile data stream with polynomial sketch size. A new algorithm selects data subsets avoiding outliers and high leverage points.
problem Outliers and high leverage points skew model estimates in subsamples.
method Unsupervised and supervised exchange procedures to select nearly D-optimal subsets.
result The new methods improve model accuracy by avoiding influential points.
Paper introduces max-plus statistical leverage scores for faster approximation of conventional scores.
problem Approximating statistical leverage scores of complex matrices efficiently.
method Max-plus algebraic analogue for statistical leverage scores.
result Max-plus statistical leverage scores can approximate conventional scores quickly and accurately.
In this paper, we consider the problem of column subset selection. We present a novel analysis of the spectral norm reconstruction for a simple randomized algorithm and establish a new bound that depends explicitly on the sampling probabilities. The sampling dependent error bound (i) allows us to better understand the …
Active learning aims to obtain a classifier of high accuracy by using fewer label requests in comparison to passive learning by selecting effective queries. Many active learning methods have been developed in the past two decades, which sample queries based on informativeness or representativeness of unlabeled data poi…
Algorithm leverages low-rank relations between surrogate tasks for structured prediction.
problem Structured prediction with large or infinite-dimensional surrogate spaces.
method Trace norm regularization to leverage relationships between surrogate outputs without explicit coding/decoding functions.
result Our algorithm can improve generalization performance over previous methods.
This paper improves random feature sampling using empirical leverage scores.
problem Optimizing the number of features for kernel approximation and supervised learning.
method Uses empirical leverage scores to optimize feature sampling.
result Empirical sampling of random features using leverage scores outperforms vanilla Monte Carlo sampling.
Efficiently approximates statistical leverage scores for faster KRR.
problem Accurately estimating statistical leverage scores for fast KRR.
method Analytic formula for statistical leverage scores, leveraging kernel spectral density.
result Linear time approximation with theoretical guarantees, significantly faster than existing methods.
GLCB uses Gated Linear Networks for online contextual bandits.
problem Online learning in contextual bandits with uncertainty estimation.
method Gated Linear Networks (GLNs) for prediction and uncertainty estimation.
result GLCB outperforms state-of-the-art methods in online contextual bandits.
New algorithm improves group fairness in social classification problems by exploiting performativity.
problem Inequities in social classification problems due to performativity.
method Develops algorithmic fairness practices that leverage performativity to achieve stronger group fairness guarantees.
result Achieves stronger group fairness guarantees compared to non-performative settings.
Paper introduces a fast, robust, scalable method for detecting changes in data streams.
problem Detecting changes in data streams efficiently and reliably.
method Bayesian online changepoint detection with provable robustness and scalability.
result The proposed method is more than 10 times faster than previous approaches and provides provable robustness.
Training examples are not all equally informative. Active learning strategies leverage this observation in order to massively reduce the number of examples that need to be labeled. We leverage the same observation to build a generic strategy for parallelizing learning algorithms. This strategy is effective because the …
Binary testing for softmax models requires many samples, similar to leverage score models.
problem Binary hypothesis testing for softmax models and leverage score models.
method Analyzing sample complexity and drawing analogies between models.
result Sample complexity is asymptotically \(O(ε^{-2})\), where \(ε\) is the distance between model parameters.
Framework uses human judgment to distinguish algorithmically indistinguishable cases.
problem Clarifying human-AI collaboration in prediction and decision tasks.
method Integrates human judgment to distinguish algorithmically indistinguishable cases.
result Improves performance of any feasible algorithmic predictor.
New algorithm learns tasks from video demonstrations using proprioceptive information.
problem Lack of idealized conditions in imitation learning from video demonstrations.
method Proposes an algorithm that leverages proprioceptive state representations for policy learning.
result Outperforms other IfO algorithms by a large margin in MuJoCo domains.
This paper proposes a hybrid CPU-GPU framework for faster graphlet computation.
problem Efficiently computing k-vertex induced subgraph statistics in large networks.
method Hybrid multi-core CPU-GPU framework, single GPU methods, and multi-GPU methods.
result 300 times faster than state-of-the-art methods.
EMDQN uses episodic memory to improve RL efficiency.
problem Sample inefficiency of deep RL algorithms.
method Leverages episodic memory to supervise training.
result Significantly reduces interaction rounds for state-of-the-art performance.
Differentially private policy evaluation improves reinforcement learning efficiency.
problem Sample inefficiency in reinforcement learning.
method Differentially private actor-critic model initialization.
result Improves sample efficiency in control problems.
Framework uses supervised knowledge to improve unsupervised learning.
problem Improving unsupervised learning performance.
method Leveraging supervised datasets to reduce unsupervised learning to supervised learning.
result Framework helps choose number of clusters, remove outliers, and circumvent Kleinberg's impossibility result.
This paper improves parallel belief propagation for scalable machine learning.
problem Efficient parallelization of belief propagation for large-scale machine learning tasks.
method Use of scalable relaxed schedulers to parallelize belief propagation.
result Our approach outperforms previous methods in scalability and convergence time.
Kernel-based algorithm optimizes cellular network configuration through multi-task learning.
problem Optimizing network configuration based on field experience and minimizing exploration cost.
method Kernel-based multi-BS contextual bandit algorithm leveraging conditional kernel embedding for multi-task learning.
result The proposed algorithm reduces exploration cost and improves network performance.
This work tackles community detection in networks with node attributes, achieving exact recovery.
problem Community detection in networks with correlated node attributes.
method Information-theoretic criterion and iterative clustering algorithm maximizing joint likelihood.
result Exact recovery of community labels under a general model for network and node attributes.
New algorithm reduces online learning regret by exploiting historical invariances.
problem Stochastic non-stationary linear bandits with changing reward models.
method ISD-linUCB algorithm that learns invariances in reward model.
result Significant regret improvements in fast-changing environments with historical data.
Adverts optimize organic traffic by strategically bidding in e-commerce feeds.
problem Maximizing organic traffic through strategic advertising in e-commerce feeds.
method Proposes a novel Leverage optimization problem and a Hybrid Training Leverage Bidding (HTLB) algorithm to optimize traffic.
result Demonstrates superior performance of the HTLB algorithm in optimizing organic traffic.
Efficiently estimates private least squares with linear error growth.
problem Private estimation of ordinary least squares with bounded residuals and leverage.
method Scaled noise added to a stable nonprivate estimator of the regression vector.
result Near-optimal accuracy guarantee with linear error growth in dimension.
A new iterative algorithm improves RFDA for high-dimensional data.
problem High-dimensional data challenges conventional FDA and RFDA.
method Iterative sketching-based algorithm with accuracy guarantees.
result Accurate approximations can be achieved with smaller sample sizes.
Faster solution for regression and ERM problems using leverage score sampling.
problem Efficiently solving regression and ERM problems with large datasets.
method Combination of leverage score sampling, proximal point methods, and accelerated coordinate descent.
result Improved running time for solving regression and ERM problems.
Generalist neural learner can execute multiple algorithms.
problem Building models that can execute multiple algorithms.
method Single graph neural network processor, incorporating knowledge from specialist models.
result Generalist learner can execute multiple algorithms with improved performance.
New methods discover causal relationships from multiple related data views.
problem Causal discovery from non-Gaussian data.
method Multi-view linear Structural Equation Model (SEM) with weak assumptions.
result Identifiability of acyclic SEMs and successful causal graph estimation.
Efficiently learns private models using public data.
problem Improving private learning performance with public data.
method Proves computationally efficient algorithms for private learning with public data.
result First computationally efficient algorithms for private learning with public data.
Quantum algorithm for pricing European call options.
problem Accurate valuation of financial derivatives, especially for complex models and options.
method Transforms classical FFT into quantum QFT for pricing European call options.
result Quantum algorithm outperforms classical Monte Carlo simulation in NISQ era.
KL regularization helps RL algorithms by implicitly averaging q-values.
problem Understanding why KL regularization improves RL performance.
method An approximate value iteration scheme, studying KL and entropy regularization.
result Strong performance bound combining linear horizon dependency and averaging effect of estimation errors.
Designs algorithms to assist humans without affecting their decisions.
problem Algorithms often fail to improve human decisions.
method Formalizes algorithm design using potential outcomes framework and monotonicity assumption.
result Derives minimax optimal recommendation algorithms for limited data.
Develops a flexible batched experimentation framework for limited adaptivity.
problem Challenges of continual reallocation in bandit algorithms with delayed feedback.
method Computational framework leveraging Gaussian sequential experiment and dynamic programming.
result Improves statistical power over standard methods, even compared to Bayesian bandit algorithms.