arXiv:2412.20802v3 Announce Type: replace Abstract: Recommender systems are widely used in the digital landscape to match users with content fitting their preferences. However, growing concerns about…
arXiv:2606.11865v2 Announce Type: replace Abstract: Conformal Bayes combines Bayesian posterior predictives with conformal calibration to produce prediction sets that are both statistically valid and geometrically efficient.…
arXiv:2606.28301v1 Announce Type: cross Abstract: Inference-time scaling is a promising paradigm to improve generative models, especially when outputs must satisfy structural constraints or optimize downstream rewards.…
arXiv:2606.27462v1 Announce Type: new Abstract: The global minimum-variance portfolio (GMVP) is the canonical decision built from an estimated covariance matrix, yet covariance estimators are universally evaluated…
arXiv:2606.27685v1 Announce Type: new Abstract: Pervasive data contamination -- stemming from measurement errors, outliers, or adversarial corruption -- has motivated the development of robust statistical methods.…
arXiv:2606.27954v1 Announce Type: new Abstract: Score-based generative models and Langevin samplers rely on estimating the score function $nabla_xlog p_t(x)$ of a forward diffusion. Classically this is…
arXiv:2606.28309v1 Announce Type: new Abstract: Binary classification from positive-only samples is a variant of PAC learning in which the learner receives i.i.d. samples from the positive…
arXiv:2606.28123v1 Announce Type: cross Abstract: Last-iterate convergence and generalization guarantees in first-order convex learning hinge on the monotonicity of the update operator. While linear averaging preserves…
arXiv:2606.27997v1 Announce Type: cross Abstract: Benchmarks of machine learning models often include many datasets, making evaluation expensive. For efficiency, it is preferable to perform evaluations on…
arXiv:2606.28242v1 Announce Type: cross Abstract: Understanding how performance scales jointly with model size and data is a central problem in modern machine learning. Existing theoretical works…
arXiv:2606.28228v1 Announce Type: cross Abstract: Causal representation learning for time series has developed strong identifiability results in discrete-time latent causal models, but identifiability in continuous-time latent…
arXiv:2102.09235v3 Announce Type: replace-cross Abstract: Recent studies revealed the mathematical connection between deep neural networks (DNNs) and dynamic systems. However, the specific dynamics that DNNs, especially…
arXiv:2505.13809v5 Announce Type: replace-cross Abstract: Off-policy evaluation (OPE) constructs confidence intervals for the value of a target policy using data generated under a different behavior policy.…
arXiv:2502.06577v3 Announce Type: replace-cross Abstract: Causal knowledge can be used to support decision-making problems. This has been recognized in the causal bandits literature, where a causal…
arXiv:2602.10714v2 Announce Type: replace-cross Abstract: Preconditioning is a common method applied to modify Markov chain Monte Carlo algorithms with the goal of making them more efficient.…
arXiv:2603.17381v4 Announce Type: replace-cross Abstract: AI coding agents, general purpose assistants that write and execute code, make empirical specification search fast and cheap, but they also…
arXiv:2606.27467v1 Announce Type: new Abstract: Molecule generation methods that leverage generative models have been successfully applied to drug discovery. However, they often require extensive pre-training, suffer…
arXiv:2606.27762v1 Announce Type: new Abstract: Unconstrained single-objective numerical optimization requires a careful balance among global exploration, late-stage exploitation, and function-evaluation efficiency. This paper presents DE-2LS, a…
arXiv:2606.27764v1 Announce Type: new Abstract: Constrained single-objective numerical optimization requires a careful balance among feasibility, objective convergence, and computational efficiency under a fixed function-evaluation budget. This…
arXiv:2606.28109v1 Announce Type: new Abstract: Traditional meta-heuristics often rely on fixed population sizes, manually chosen search scales, and externally attached parameter-control modules. This paper presents the…
arXiv:2606.27783v1 Announce Type: cross Abstract: Continuous attractor neural networks (CANNs) are the canonical computational framework for how the brain encodes continuous variables such as spatial position,…
arXiv:2606.27946v1 Announce Type: cross Abstract: Recent breakthroughs in synaptic-resolution network connectomics have revealed that brain circuits feature fine-scale structural connectivity, such as pairs of correlated synaptic…
arXiv:2606.27892v1 Announce Type: cross Abstract: Wearable devices and Internet of Things (IoT) sensors require on-sensor processing of biosignals and environmental data, including computationally demanding operations such…
arXiv:2511.07836v4 Announce Type: replace-cross Abstract: The curse of dimensionality remains a persistent challenge in modern optimization problems. Expanding the search space into higher dimensions exponentiates the…