LADaR framework calibrates machine learning models for instance-wise predictions.
problem Challenges in assessing and calibrating predictive distributions for complex inputs.
method Local Amortized Diagnostics and Reshaping of Conditional Densities (LADaR) framework and e x t t t C a l − P I T exttt{Cal-PIT} e x ttt C a l − P I T algorithm. result Achieves better instance-wise calibration than existing methods in galaxy distance estimation.
LIMIS improves locally interpretable models by selecting and distilling key instances.
problem Low fidelity of locally interpretable models.
method LIMIS uses instance-wise subsampling guided by policy gradient and reward to improve fidelity.
result LIMIS near-matches black-box model accuracy while significantly improving fidelity.
In the Best- k k k -Arm problem, we are given n n n stochastic bandit arms, each associated with an unknown reward distribution. We are required to identify the k k k arms with the largest means by taking as few samples as possible. In this paper, we make progress towards a complete characterization of the instance-wise sample…
The paper formalizes feature attribution to address inconsistent definitions and evaluate methods.
problem Inconsistent definitions of feature relevance in feature attribution.
method Formalization based on relaxed functional dependence, extended to instance-wise setting.
result State-of-the-art methods often fail to verify necessary properties for candidate selection.
nFBST tests neural networks using Bayesian methods.
problem Traditional significance testing struggles with complex nonlinear relationships.
method nFBST uses Bayesian neural networks to test neural networks.
result nFBST can test global, local, and instance-wise significance.
GL-LowPopArt improves minimax-optimal estimation for trace regression.
problem Minimizing estimation error in generalized low-rank trace regression.
method Two-stage approach: nuclear norm regularization followed by matrix Catoni estimation.
result Achieves instance-wise optimal error bounds up to condition number.
SoftCLT improves time series representation learning by soft contrastive loss.
problem Ignoring inherent correlations in time series leads to poor representation quality.
method SoftCLT introduces instance-wise and temporal contrastive loss with soft assignments.
result SoftCLT consistently improves various downstream tasks in time series learning.
ISAHP discovers instance-level causal structures in event sequences.
problem Discovering fine-grained causal relationships in asynchronous, interdependent event sequences.
method ISAHP, a novel deep learning framework using self-attention mechanism.
result ISAHP meets Granger causality requirements and discovers complex causal structures.
New method calibrates neural network predictions for better reliability.
problem Improper probability estimates from deep networks leading to unreliable predictions.
method Proposes a constrained optimization approach for a monotonic calibration map.
result Achieves state-of-the-art performance across various datasets and models.
We consider the thresholding bandit problem, whose goal is to find arms of mean rewards above a given threshold θ θ θ , with a fixed budget of T T T trials. We introduce LSA, a new, simple and anytime algorithm that aims to minimize the aggregate regret (or the expected number of mis-classified arms). We prove that our algo…
Paper tackles hypothesis transfer learning for black-box models.
problem Difficult to build universal machine learning models across different institutions.
method Dynamic Knowledge Distillation (dkdHTL) with instance-wise weighting.
result Empirical results show the effectiveness of dkdHTL.
Graph neural networks (GNNs) have become increasingly popular for classification tasks on graph-structured data. Yet, the interplay between graph topology and feature evolution in GNNs is not well understood. In this paper, we focus on node-wise classification, illustrated with community detection on stochastic block m…
Proposes MEED framework for model interpretation.
problem Improving model interpretability and avoiding undesired characteristics.
method Adversarial Infidelity Learning (AIL) for effective feature selection.
result AIL mechanism helps learn desired conditional distribution.
New method selects features for sequential decision making.
problem Dynamic feature selection for instance-wise decisions.
method Latent variable model trained in a supervised manner; reasoning across stochastic latent space.
result Outperforms existing methods on various datasets.
New algorithm reduces reinforcement learning regret by adapting to interaction variability.
problem Existing reinforcement learning methods lack adaptability to interaction variability.
method Developed a variance-adaptive optimal algorithm for MNL function approximation.
result Achieved instance-wise optimal regret bounds, validating efficiency in practice.
Many machine learning tasks require sampling a subset of items from a collection based on a parameterized distribution. The Gumbel-softmax trick can be used to sample a single item, and allows for low-variance reparameterized gradients with respect to the parameters of the underlying distribution. However, stochastic o…
FIT evaluates time series model feature importance quantifying distributional shift.
problem Lack of explanations for time series models in high-stakes applications.
method FIT framework quantifies feature importance based on distributional shift using KL-divergence.
result FIT identifies important time points and observations superiorly compared to baselines.
Local explanation frameworks aim to rationalize particular decisions made by a black-box prediction model. Existing techniques are often restricted to a specific type of predictor or based on input saliency, which may be undesirably sensitive to factors unrelated to the model's decision making process. We instead propo…
PiNets provide faithful explanations for neural networks.
problem Lack of true explanations for neural network predictions.
method Pointwise-interpretable Networks (PiNets) that form linear models instance-wise.
result PiNets offer explanations that are meaningful, aligned, robust, and sufficient.
SuNCEt accelerates contrastive learning with minimal labeled data.
problem Efficiently learning visual representations with limited labeled data.
method Noise-contrastive estimation and neighbourhood component analysis-based semi-supervised loss.
result SuNCEt achieves semi-supervised learning accuracy with less than half the labeled data.
Paper develops efficient mechanisms for estimating variance and covariance under differential privacy in the add-remove model.
problem Estimating variance and covariance under differential privacy in the add-remove model.
method Developed mechanisms based on the Bézier mechanism, a novel moment-release framework.
result Proved minimax optimality of the Bézier-based estimator in the high-privacy regime and demonstrated its better utility in instance-wise analysis.
Survey on assessing and improving classifier calibration for better decision making.
problem Ensuring classifiers correctly quantify prediction uncertainty.
method Overview of principles, methods, and evaluation metrics for calibration.
result New methods and extensions from binary to multiclass settings.
Last SGD iterate bounds for overparameterized linear regression.
problem Analyzing the last iterate risk bounds of SGD with decaying stepsize for overparameterized linear regression.
method Problem-dependent analysis of last iterate risk bounds of SGD with geometrically decaying stepsize.
result Proved nearly matching upper and lower bounds on the excess risk for last iterate SGD with geometrically decaying stepsize.
This research improves deep neural network calibration using a new loss function.
problem Improving probability calibration in deep neural networks.
method Introduces Focal Calibration Loss (FCL) to minimize Euclidean norm and penalize calibration error.
result FCL achieves state-of-the-art performance in both calibration and accuracy metrics.
GD outperforms ridge regression and SGD in linear regression problems.
problem Comparing the risks of GD, ridge regression, and SGD in linear regression problems.
method Instance-wise finite-sample risk analysis of GD, ridge regression, and SGD.
result GD outperforms ridge regression and is incomparable with SGD in some cases.
Simple method for estimating missing panel data entries with confidence intervals.
problem Estimating missing values in panel data with staggered adoption.
method Simple matrix algebra and singular value decomposition for estimation, with data-driven confidence intervals.
result Confidence intervals match non-asymptotic lower bounds, proving instance optimality.
MET learns tabular data representations without data augmentations.
problem Lack of effective self-supervised learning methods for tabular data.
method Reconstruction-based approach using masked encoding, with separate representations for each coordinate and adversarial reconstruction loss.
result MET achieves state-of-the-art performance on five diverse tabular datasets, improving up to 9% over current methods.
The paper offers a method to create prediction sets with uncertainty control.
problem Calibrating and communicating uncertainty in machine learning predictions.
method Distribution-free, risk-controlling prediction sets using a holdout set to calibrate set sizes.
result Explicit finite-sample guarantees for error control in various machine learning tasks.
New method clusters disease subtypes from model explanations.
problem Discovering disease subtypes in noisy, high-dimensional data.
method Train classifier, extract explanations, cluster in explanation space.
result Cluster analysis on model explanations outperforms classical methods.
This paper analyzes multi-pass SGD for least squares, improving generalization bounds.
problem Improving generalization bounds for multi-pass SGD in the least squares problem.
method Develops an instance-dependent excess risk bound for least squares in the interpolation regime.
result SGD performs worse than GD instance-wise but saves computational time.
Paper proposes a method to train robust neural networks without labeled data.
problem Training robust neural networks without class labels.
method Adversarial contrastive learning framework using unlabeled data.
result Robust Contrastive Learning (RoCL) achieves comparable robust accuracy to supervised methods and significantly improved robustness.
Efficiently refits black box predictions with wild refitting method.
problem Computing high-probability upper bounds on prediction errors.
method Three-step procedure: residuals, symmetrization, and solving a modified prediction problem.
result Wild refitting provides an upper bound on prediction error with high probability.
A new recursive mixture estimation algorithm improves VAE inference efficiency and accuracy.
problem Inaccurate posterior approximation in traditional VAEs.
method Recursive mixture estimation algorithm using functional gradient approach for iterative component selection.
result Significantly higher test data likelihood compared to state-of-the-art methods on benchmark datasets.
In this paper, we propose an extension to an existing algorithm (instance-MIR) which tackles the multiple instance regression (MIR) problem, also known as distribution regression. The MIR setting arises when the data is a collection of bags, where each bag consists of several instances which correspond to the same and …
New method solves matrix completion problems to certifiable optimality.
problem Certifying optimality in low-rank matrix completion.
method Disjunctive branch-and-bound scheme for convex relaxation.
result Decreases optimality gap by two orders of magnitude.
Paper analyzes GLM-tron for high-dimensional ReLU regression, providing upper and lower bounds.
problem Learning a single ReLU neuron in high-dimensional settings with overparameterization.
method Perceptron-type algorithm GLM-tron, with finite-sample analysis.
result Sharp characterization of high-dimensional ReLU regression problems via GLM-tron, contrasting with SGD.
Paper tackles cross-granularity few-shot learning with meta-embedder.
problem Few-shot learning with coarse labels and fine-grained testing.
method Meta-embedder that optimizes visual and semantic discrimination across coarse and fine classes.
result Meta-embedder achieves effective cross-granularity few-shot classification.
Improved algorithm for logistic bandits with better regret bounds.
problem Understanding the impact of non-linearity in logistic bandits.
method Introducing a new algorithm and providing refined analysis.
result Improved regret bounds scaling as O ~ ( d T / κ ) \tilde{\mathcal{O}}(d\sqrt{T/κ}) O ~ ( d T / κ ) in most favorable cases. A new algorithm reduces regret in bandit problems with adversarial corruptions.
problem Optimizing decision-making in bandit problems with variable uncertainties and adversarial interference.
method Proposes HCW-GLB-OMD, an OMD-based estimator with Hessian-based confidence weights for robustness.
result Achieves instance-wise minimax optimality with a κ κ κ -factor in the corruption term. We study the combinatorial pure exploration problem Best-Set in stochastic multi-armed bandits. In a Best-Set instance, we are given n n n arms with unknown reward distributions, as well as a family F \mathcal{F} F of feasible subsets over the arms. Our goal is to identify the feasible subset in F \mathcal{F} F with the maxi…
Discovering imaging biomarkers for autism spectrum disorder (ASD) is critical to help explain ASD and predict or monitor treatment outcomes. Toward this end, deep learning classifiers have recently been used for identifying ASD from functional magnetic resonance imaging (fMRI) with higher accuracy than traditional lear…
In the classical best arm identification (Best- 1 1 1 -Arm) problem, we are given n n n stochastic bandit arms, each associated with a reward distribution with an unknown mean. We would like to identify the arm with the largest mean with probability at least 1 − δ 1-δ 1 − δ , using as few samples as possible. Understanding the sample c…