arXiv research

New measure SEV shows non-sparse models can still have low decision sparsity.

This paper improves prediction rule ensembles using model-based data generation.

CONFETTI improves interpretability of deep learning models for MTS by providing counterfactual explanations.

"Sparse" neural networks, in which relatively few neurons or connections are active, are common in both machine learning and neuroscience. Whereas in machine learning, "sparsity" is related to a penalty term that leads to some connecting weights becoming small or zero, in biological brains, sparsity is often created wh…

MOSS optimizes decision rules for accuracy and stability.

Decision trees are consistent for regression and classification tasks even with many predictors.

The paper tackles reward-relevance in offline RL with sparse decision dynamics.

Optimizes predictions for specific tasks using parametrized decision analysis.

Within machine learning, the supervised learning field aims at modeling the input-output relationship of a system, from past observations of its behavior. Decision trees characterize the input-output relationship through a series of nested $if-then-else$ questions, the testing nodes, leading to a set of predictions, th…

We demonstrate that, in the classical non-stochastic regret minimization problem with $d$ decisions, gains and losses to be respectively maximized or minimized are fundamentally different. Indeed, by considering the additional sparsity assumption (at each stage, at most $s$ decisions incur a nonzero outcome), we derive…

This work extends neural networks to automatically select features by stochastically penalizing feature involvement.

Novel algorithm reduces feature inclusion in online decision-making.

A new algorithm reduces communication costs for collaborative decision-making across clients.

Enhances counterfactual explanations with more valid and informative saliency maps.

A new framework for interpretable models using sparse linear layers.

Understanding large-scale patterns in student course enrollment is a problem of great interest to university administrators and educational researchers. Yet important decisions are often made without a good quantitative framework of the process underlying student choices. We propose a probabilistic approach to modellin…

Nonparametric methods are widely applicable to statistical inference problems, since they rely on a few modeling assumptions. In this context, the fresh look advocated here permeates benefits from variable selection and compressive sampling, to robustify nonparametric regression against outliers - that is, data markedl…

We present DCSVM, an efficient algorithm for multi-class classification using Support Vector Machines. DCSVM is a divide and conquer algorithm which relies on data sparsity in high dimensional space and performs a smart partitioning of the whole training data set into disjoint subsets that are easily separable. A singl…

New adaptive learning rate improves FTRL's adaptivity to sparsity, game-dependency, and best-of-both-worlds.

New insights into optimal portfolios and ecological equilibria reveal surprising complexity.

New algorithm minimizes regret in sparse reinforcement learning.

Paper proposes a new dynamic pricing method with always-valid online statistical learning.

New algorithm reduces dimensionality in stochastic optimization.

We propose a minimax concave penalized multi-armed bandit algorithm under generalized linear model (G-MCP-Bandit) for a decision-maker facing high-dimensional data in an online learning and decision-making process. We demonstrate that the G-MCP-Bandit algorithm asymptotically achieves the optimal cumulative regret in t…

ARFs generate plausible counterfactuals for models, improving model understanding.

Bayesian approach for constructing and rebalancing sparse index-tracking portfolios.

Many sequential decision-making tasks require choosing at each decision step the right action out of the vast set of possibilities by extracting actionable intelligence from high-dimensional data streams. Most of the times, the high-dimensionality of actions and data makes learning of the optimal actions by traditional…

Meta-KeL learns kernels from offline data to improve sequential decision-making.

We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. Our models are decision lists, which consist of a series of if...then... statements (e.g., if high blood pressure, then stroke) that discretize a high-dimensional, multivariate feature space into a series of sim…

PRESTO improves rare event prediction by shrinking towards proportional odds model.

A new method reduces bias in adaptive Lasso estimates.

A new algorithm reduces regret in high-dimensional online learning problems.

LSDAT reduces query efficiency for decision-based adversarial attacks.

Proposes SHORE model for efficient MOR with sparsity and scalability.

New insights explain speedup saturation in distributed learning with large batches and delays.

The study provides theoretical guarantees for the statistical performance of optimal decision trees.

New layer sparsity concept improves neural networks.

Paper introduces MTGP for engineering tasks with sparse data.

The lack of interpretability remains a key barrier to the adoption of deep models in many applications. In this work, we explicitly regularize deep models so human users might step through the process behind their predictions in little time. Specifically, we train deep time-series models so their class-probability pred…

Our work is focused on the joint sparsity recovery problem where the common sparsity pattern is corrupted by Poisson noise. We formulate the confidence-constrained optimization problem in both least squares (LS) and maximum likelihood (ML) frameworks and study the conditions for perfect reconstruction of the original r…

New algorithms for efficient learning with long-term rewards in contextual bandits.

This paper optimizes high-dimensional oblique splits for decision trees, enhancing performance and computational efficiency.

New theorem for generalized group sparsity improves consistency and convergence rates.

This paper studies activation sparsity in large language models, finding key trends and implications.

New method targets sparsity to prevent overfitting in deep nets.

Embeds sparsity in deep neural networks, allowing exact zero parameters.

Survey on principles and challenges of interpretable machine learning.

Sparsity in Deep Neural Networks (DNNs) is studied extensively with the focus of maximizing prediction accuracy given an overall parameter budget. Existing methods rely on uniform or heuristic non-uniform sparsity budgets which have sub-optimal layer-wise parameter allocation resulting in a) lower prediction accuracy o…