New algorithms reduce overfitting in multiclass classification.
problem Excessive reuse of test datasets in machine learning leads to overfitting, especially in multiclass classification.
method Developed computationally efficient algorithms to reduce overfitting bias in multi-class classification.
result Achieved overfitting bias of Θ(√(k/(mn)), k/n), matching known upper bounds.
Memory-constrained algorithms need superlinear memory for efficient convex optimization.
problem Efficiently minimizing convex functions with limited memory.
method Analyzing first-order algorithms with superlinear memory constraints.
result Superlinear memory is necessary for optimal performance in convex optimization.
Improved private learning of halfspaces with reduced sample complexity.
problem Private learning of halfspaces with reduced sample complexity.
method Iterative algorithm for solving linear feasibility problem, improving state-of-the-art results.
result Sample complexity reduced to d 2.5 ⋅ 2 log ∗ ∣ G ∣ d^{2.5} \cdot 2^{\log^*|G|} d 2.5 ⋅ 2 l o g ∗ ∣ G ∣ , improving d 2 d^2 d 2 factor. CoLT assesses neural posterior estimates by detecting discrepancies across conditioning inputs.
problem Validating neural posterior estimates from limited data.
method Conditional Localization Test (CoLT) learns a localization function to detect strong deviations.
result CoLT provides rigorous guarantees and practical scalability for comparing true and neural posterior distributions.
Quadratic memory is essential for optimal convex optimization queries.
problem Optimal query complexity for convex optimization and feasibility problems.
method Lower bounds on query complexity for convex optimization and feasibility problems.
result Center-of-mass algorithms are Pareto-optimal for both convex optimization and feasibility problems.
Paper settles sample complexity for learning from multiple distributions.
problem Learning from multiple data distributions with a hypothesis class of bounded VC dimension.
method Introduced an algorithm with sample complexity of O((d+k)ε^-2)·(k/ε)^o(1).
result Algorithm matches lower bound up to sub-polynomial factor.
This work accelerates gradient descent with anytime convergence guarantees.
problem Improving the convergence rate of gradient descent methods.
method Proposes a stepsize schedule for gradient descent that achieves anytime convergence rates.
result Gradient descent can achieve convergence rates of O ( T − 1.119 ) O(T^{-1.119}) O ( T − 1.119 ) for any stopping time T T T . A simple linear algebraic explanation of the algorithm in "A Spectral Algorithm for Learning Hidden Markov Models" (COLT 2009). Most of the content is in Figure 2; the text just makes everything precise in four nearly-trivial claims.
The paper solves open questions in computable PAC learning, providing a complete landscape.
problem Understanding the boundaries and capabilities of computable PAC learning.
method Analyzing and constructing decidable hypothesis classes with different sample complexities and Littlestone dimensions.
result A complete understanding of CPAC learnability, answering open questions and confirming conjectures.
Study on list learning with noisy data, showing limits and some learnable cases.
problem Learning from noisy data in a list learning context.
method Inspired by coding theory, extends list learning model to study sparse conjunctions and parities/majors.
result Sparse conjunctions can be efficiently list learned under certain conditions, but parities and majors cannot be efficiently learned.
Replication confirms CGD's effectiveness in competitive games.
problem Reproducibility of a novel Nash equilibrium algorithm.
method Replicated experiments and provided Python implementation.
result CGD avoids oscillatory and divergent behaviours.
Novel approach to universal online learning for bounded losses, closing open problems.
problem Characterizing processes for universal online learning under non-i.i.d. conditions.
method Characterization of processes admitting strong and weak universal learning, introduction of optimistically universal learning rule.
result Introduction of a novel 1NN algorithm that is optimistically universal for bounded losses.
Abstracts index for ML4H workshop at NeurIPS 2019.
problem No specific problem stated; index of accepted abstracts.
method Not specified; index of accepted abstracts.
result No specific result stated.
VoxCeleb 2019 challenge assesses speaker recognition in uncontrolled settings.
problem Evaluate speaker recognition technology in unconstrained data.
method Public dataset, challenge, and workshop at Interspeech 2019.
result Baseline results and discussions provided.
Data set tracks real-time election results for 4 hours post-October 2019 Portuguese elections.
problem Real-time tracking of election results for predictive modeling.
method Real-time data collection and interval-based analysis.
result Data set supports various predictive modeling tasks including numerical forecasting.
This work proves lower bounds on a greedy teaching set construction algorithm.
problem Characterize the best-case teaching dimension of a concept class.
method A greedy algorithm that iteratively adds points to the teaching set to restrict the concept class the most.
result Lower bounds on the performance of the greedy approach for small k, extending up to k ≤ c*d for small constant c.
Learning the natural parameters z ∈ R n z \in \mathbb{R}^n z ∈ R n of discrete distributions μ z μ_z μ z from independent samples constrained to a subset S ⊆ { 0 , 1 } n S \subseteq \{0,1\}^n S ⊆ { 0 , 1 } n is a foundational challenge in high-dimensional statistics. Existing methods for efficiently estimating truncated Boolean product distributions, notably the work …
Abstract geometric structures flow harmonically.
problem Geometric structures on Riemannian manifolds.
method Twistorial interpretation and abstract harmonicity condition.
result Established analytic properties of geometric gradient flow.
New findings show transfer learning is possible even when density ratios are unbounded.
problem Transfer learning under unbounded density ratios.
method Low-degree polynomial estimators, general transfer inequality over R n \mathbb{R}^n R n . result Non-trivial transfer learning possible under mild assumptions, including log-concave measures.
New sampling and identity-testing methods for mixtures of distributions that don't satisfy approximate tensorization of entropy.
problem Sampling and identity-testing for mixtures of distributions that don't satisfy approximate tensorization of entropy.
method Fast mixing of Glauber dynamics and efficient identity-testers in the coordinate-conditional sampling access model.
result Efficient identity-testers for mixtures of ATE distributions in the coordinate-conditional sampling access model.
This paper describes our system submitted to SemEval 2019 Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours, Subtask A (Gorrell et al., 2019). The challenge focused on classifying whether posts from Twitter and Reddit support, deny, query, or comment a hidden rumour, truthfulness of which is …
A fast spectral algorithm estimates mean of heavy-tailed vectors efficiently.
problem Estimating the mean of heavy-tailed random vectors with optimal error bound.
method Spectral algorithm using eigenvector computations and novel hyperplane connection.
result Achieves optimal sub-gaussian error bound with improved runtime.
Learning linear predictors with the logistic loss---both in stochastic and online settings---is a fundamental task in machine learning and statistics, with direct connections to classification and boosting. Existing "fast rates" for this setting exhibit exponential dependence on the predictor norm, and Hazan et al. (20…
Open problem: fixed-budget best arm identification complexity.
problem Understanding the complexity of identifying the best arm in a fixed budget setting.
method Analyzing existing results and conjectures in the fixed-confidence setting.
result Open questions remain about the fixed-budget setting.
Analyzed US firm data 1970-2019, identifying scale effects and distributional forms.
problem Understanding differences between small and large firms over time.
method Examined all public US firms, used stylized facts and DLN distribution analysis.
result Small firms are systematically different from large firms, with scale-dependent heteroskedasticity.
A new bandit problem where experiments can be interrupted if results are not promising.
problem Interruptible multi-armed bandit problem with a threshold for cumulative reward.
method Formalized survival regret, identified key components (regret and probability of ruin), derived lower bounds and optimal policies.
result No policy can achieve sublinear survival regret, but optimal policies minimize survival regret in a Pareto sense.
We present an efficient second-order algorithm with O ~ ( 1 η T ) \tilde{O}(\frac{1}η\sqrt{T}) O ~ ( η 1 T ) regret for the bandit online multiclass problem. The regret bound holds simultaneously with respect to a family of loss functions parameterized by η η η , for a range of η η η restricted by the norm of the competitor. The family of loss funct…
Two methods for quantile regression are compared and found to produce tighter intervals.
problem Comparing methods for producing prediction intervals in quantile regression.
method Two recently proposed methods combining conformal inference and quantile regression.
result Romano et al.'s method typically yields tighter prediction intervals in finite samples.
We propose a hypergraph-based active learning scheme which we term H S 2 HS^2 H S 2 , H S 2 HS^2 H S 2 generalizes the previously reported algorithm S 2 S^2 S 2 originally proposed for graph-based active learning with pointwise queries [Dasarathy et al., COLT 2015]. Our H S 2 HS^2 H S 2 method can accommodate hypergraph structures and allows one to ask bo…
Investigates the relationship between US money supply and asset indices over 2001-2019.
problem Determining the relationship between US money supply and asset indices growth.
method Information entropy methodology applied to US asset indices (Property, Russell 2000, S&P 500, NASDAQ) over 2001-2019.
result Growth in US broad money supply is the main determinant of US asset indices growth, especially the NASDAQ and Russell 2000.
Improved bounds on combining hypothesis classes for binary functions.
problem Understanding how to combine hypothesis classes for binary functions.
method Established upper bounds on Littlestone and threshold dimensions for combined classes.
result Upper bounds are nearly tight and give exponential improvements.
Study confirms improved performance of Self-Critique and Adapt method.
problem Improving performance of MAML++ method.
method Self-Critique and Adapt (SCA) method.
result SCA method improves performance of MAML++.
Abstract notes on robust statistical learning theory.
problem Developing robust estimators for statistical learning.
method Stressing principles of robust estimators construction and analysis.
result Emphasizes main principles of robust estimators construction and analysis.
Lectures on symplectic aspects of surface degenerations at KIAS.
problem Exploring symplectic structures in surface degenerations.
method Expository account of symplectic aspects of cyclic quotient surface singularities.
result Discussion of symplectic structures in surface degenerations.
Revisits causal inference identifiability with positivity assumption.
problem General identifiability in causal inference without positivity assumption.
method Introduces new algorithm sound and complete under positivity assumption.
result New algorithm connects general identifiability to classical identifiability.
Task focuses on fact checking in Q&A forums, improving over baseline systems.
problem Fact checking in community Q&A forums to distinguish factual from opinion.
method Two subtasks: distinguishing factual vs. opinion/advice/socializing, predicting answer truthfulness.
result Improved over baseline systems for both subtasks, but not for Subtask B.
The study of projective varieties with nef anticanonical divisors and log terminal singularities.
problem Understanding the structure and properties of projective varieties with specific divisor conditions.
method Analyzing the Albanese map and MRC fibration for klt projective varieties, showing locally constant fibrations and product decompositions.
result Generalization of results for smooth projective varieties to the klt case, including decomposition into rationally connected and projective varieties with trivial canonical divisor.
Reply to Ogburn et al. on their critique of Wang and Blei's work.
problem Critique of Wang and Blei's work on the blessings of multiple causes.
method Discussion and clarification of Wang and Blei's claims and findings.
result Wang and Blei's premise is correct and there are no foundational errors.
Study the Mexican stock market's interdependency structure from 2000-2019.
problem Characterize the interdependency structure of the Mexican Stock Exchange.
method Estimate correlation/concentration matrices from different models and compute network theory metrics.
result Visualizations provide a comprehensive overview of the stock market's interdependency structure.
New method calibrates eSSVI volatility surfaces without arbitrage.
problem Sequential calibration of eSSVI surfaces lacks global view and guarantees no arbitrage.
method Global and arbitrage-free parametrization of eSSVI surfaces.
result Faster calibration always guarantees an arbitrage-free fit.
Improved disentanglement in VAEs using aggregated feature maps.
problem Improving disentanglement in Variational Autoencoders (VAEs).
method Regionally aggregated feature maps extracted from pre-trained CNNs on ImageNet.
result 2nd place in NeurIPS 2019 disentanglement challenge.
Study optimizes trading strategies in markets with transaction costs and uncertain models.
problem Optimizing trading strategies in markets with transaction costs and model uncertainty.
method Maximizing worst-case expected utility over a class of models on a filtered probability space.
result Existence of optimal trading strategies for general càdlàg price processes and incomplete filtrations.
Local regularization fails in transductive learning for some multiclass problems.
problem Whether local regularization can learn all transductive multiclass problems.
method Provided a negative answer by exhibiting a specific multiclass problem.
result Local regularization cannot learn all transductive multiclass problems.
Detects out-of-distribution inputs in deep generative models.
problem Mismatch between model's typical set and high probability density areas.
method Statistically principled test using likelihood distribution.
result Successfully detects out-of-distribution sets in challenging cases.
Improved sampling from non-log-concave distributions with polynomial query complexity.
problem Sampling from distributions with non-log-concave densities efficiently.
method Combining Ornstein-Uhlenbeck process assumptions and polynomial moment conditions.
result Polynomial query complexity improvement over previous methods.
New proof shows faster convergence rate for robust estimation with Lasso in adversarially contaminated outputs.
problem Robust estimation of parameters in the presence of adversarial output contamination.
method Extended Lasso with Huber loss function and L 1 L_1 L 1 penalty, focusing on specific properties of the Huber function. result Same convergence rate as Dalalyan and Thompson (2019), but with a different proof.
Study improves CNNs for audio scene classification by restricting receptive fields and adding frequency awareness.
problem Improving CNNs for robust acoustic scene classification.
method Investigated different receptive field configurations for various CNN architectures and introduced Frequency Aware CNNs.
result Several well-performing submissions to DCASE 2019 Challenge were achieved.
AVEC 2019 challenges AI in detecting depression and cross-cultural emotions.
problem Detecting depression and cross-cultural emotions from audiovisual data.
method Comparison of machine learning methods under standardized conditions.
result Baseline system performance on state-of-mind, depression, and cross-cultural tasks.