Machine Learning

CCL, a novel probabilistic framework, uses continuous contrastive learning to excel in long-tailed semi-supervised recognition, surpassing prior state-of-the-art methods by over 4%.

Boost continual learning efficiency with CLFD: a novel frequency domain approach that improves accuracy by up to 6.83% and slashes training time by 2.6x on edge devices!

Contextual MNL bandits are revolutionized with general value functions, offering enhanced algorithms for stochastic and adversarial settings, surpassing previous results in accuracy and efficiency.

Contextual Bilevel Reinforcement Learning (CB-RL) tackles real-world strategic decision-making where optimal policies depend on environmental configurations and exogenous events, proposing a stochasti…

CAMS, a novel contextual active model selection algorithm, minimizes labeling costs by strategically selecting pre-trained models and querying labels for data points, achieving significant improvement…

Context-Aware Testing (CAT) revolutionizes ML model testing by using contextual information to identify relevant failures, surpassing traditional data-only methods.

Constrained Latent Action Policies (C-LAP) revolutionizes offline reinforcement learning by jointly modeling state-action distributions, implicitly constraining policies to improve efficiency and redu…

Constrained diffusion models, trained via a novel dual approach, achieve optimal trade-offs between data fidelity and user-defined distribution constraints, enabling fairer and more controlled data ge…

Constant Acceleration Flow (CAF) drastically accelerates image generation in diffusion models by leveraging a constant acceleration equation, outperforming state-of-the-art methods in both speed and q…

CMPE: a new conditional sampler for SBI, achieves fast few-shot inference with an unconstrained architecture, outperforming current state-of-the-art algorithms on various benchmarks.

Data connectivity profoundly shapes implicit regularization in matrix factorization for matrix completion, transitioning from low nuclear norm to low rank solutions as data shifts from disconnected to…

CRFed, a novel federated learning framework, uses diffusion-based data harmonization and confusion-resistant strategies to significantly boost accuracy and robustness in non-IID data scenarios.

Conformalized Time Series with Semantic Features (CT-SSF) significantly improves time-series forecasting by dynamically weighting latent semantic features, achieving greater prediction efficiency whil…

This paper introduces Selective Conformal P-Value (SCPV), a novel method for controlling FDR in conformalized multiple testing after data-dependent selection, offering a unified theoretical framework …

Conformal prediction empowers robust credal set predictions, handling aleatoric and epistemic uncertainties in classification, guaranteed to be valid with high probability!

RC3P, a novel algorithm, significantly reduces prediction set sizes in class-conditional conformal prediction while guaranteeing class-wise coverage, even on imbalanced datasets.

Confident-NPG-CMDP: First primal-dual algorithm achieving polynomial sample complexity for solving constrained Markov decision processes (CMDPs) using function approximation and local access model.

Boost multi-class classifier calibration by cleverly transforming the problem into a single binary calibration task!

CondTSF: One-line plugin for time series forecasting dataset condensation, boosting performance at low condensation ratios.

Con4m, a novel consistency learning framework, leverages contextual information to effectively classify segmented time series with inconsistent boundary labels and varying durations of classes, signif…