A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Novel method reduces costly model evaluations in inference problems.
Novel approach for estimating conditional expectations using Bayesian quadrature.
We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained. This occurs for example when complex simulator-based statistical models are fitted to data, and synthetic likelihood (SL) method is used to form the noisy log-likelihood estimates using computationally costly …
New models can't beat existing ones, so debiasing methods only slightly reduce needed labels.
Bayesian approach improves Shapley value estimation efficiency.
Survey of Monte Carlo methods for noisy, costly densities in reinforcement learning and ABC.
New method optimizes costly functions with unknown costs and budget constraints.
Study evaluates new models using human feedback from another model.
Develops a method to find costly high-confidence errors in black box models.
LLMs help less-resourced researchers access costly data.
The optimization of expensive-to-evaluate black-box functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences. The combinatorial explosion of the search space and costly evaluations pose challenges for current techniques in discrete optimization and machine …
Evaluating novel contextual bandit policies using logged data is crucial in applications where exploration is costly, such as medicine. But it usually relies on the assumption of no unobserved confounders, which is bound to fail in practice. We study the question of policy evaluation when we instead have proxies for th…
Fast covariance calculation is required both for SLAM (e.g.~in order to solve data association) and for evaluating the information-theoretic term for different candidate actions in belief space planning (BSP). In this paper we make two primary contributions. First, we develop a novel general-purpose incremental covaria…
Proposes a new method to selectively access privileged information in reinforcement learning.
Off-policy evaluation (OPE) in reinforcement learning allows one to evaluate novel decision policies without needing to conduct exploration, which is often costly or otherwise infeasible. We consider for the first time the semiparametric efficiency limits of OPE in Markov decision processes (MDPs), where actions, rewar…
This paper proposes a new randomized strategy for adaptive MCMC using Bayesian optimization. This approach applies to non-differentiable objective functions and trades off exploration and exploitation to reduce the number of potentially costly objective function evaluations. We demonstrate the strategy in the complex s…
New methods combine low and high-fidelity data for accurate surrogate modeling.
LLM evaluation suffers from systematic biases and lacks reliable positive judgments.
This study benchmarks likelihood-free inference methods for models with heavy-tailed or discrete data.
OPERA blends multiple OPE estimators to evaluate new policies offline.
Bayesian optimization (BO) aims to minimize a given blackbox function using a model that is updated whenever new evidence about the function becomes available. Here, we address the problem of BO under partially right-censored response data, where in some evaluations we only obtain a lower bound on the function value. T…
Study shows data attribution methods are sensitive to hyperparameters, making tuning costly.
Off-policy evaluation (OPE) in both contextual bandits and reinforcement learning allows one to evaluate novel decision policies without needing to conduct exploration, which is often costly or otherwise infeasible. The problem's importance has attracted many proposed solutions, including importance sampling (IS), self…
Evaluates local explanations using white-box models and log odds ratios.
Active learning optimizes correlation clustering by querying the most informative pairwise comparisons.
The problem of optimizing unknown costly-to-evaluate functions has been studied for a long time in the context of Bayesian Optimization. Algorithms in this field aim to find the optimizer of the function by asking only a few function evaluations at locations carefully selected based on a posterior model. In this paper,…
New framework evaluates MIA without retraining, addressing biases.
ACE improves counterfactual explanations with fewer model queries.
Proposes a method for obtaining interval bounds in off-policy evaluation.
Large uncertainties in many phenomena have challenged decision making. Collecting additional information to better characterize reducible uncertainties is among decision alternatives. Value of information (VoI) analysis is a mathematical decision framework that quantifies expected potential benefits of new data and ass…
New algorithm MTMC reduces MCMC evaluation costs.
Many objective Bayesian optimization tackles redundant objectives in expensive black-box functions.
Efficiently evaluate generative models at the prompt level using tensor factorization.
This study uses OPE methods to quickly assess auction policies.
The performance of optimizers, particularly in deep learning, depends considerably on their chosen hyperparameter configuration. The efficacy of optimizers is often studied under near-optimal problem-specific hyperparameters, and finding these settings may be prohibitively costly for practitioners. In this work, we arg…
Stochastic computation graphs (SCGs) provide a formalism to represent structured optimization problems arising in artificial intelligence, including supervised, unsupervised, and reinforcement learning. Previous work has shown that an unbiased estimator of the gradient of the expected loss of SCGs can be derived from a…
Regulations impose idiosyncratic capital and funding costs for holding derivatives. Capital requirements are costly because derivatives desks are risky businesses; funding is costly in part because regulations increase the minimum funding tenor. Idiosyncratic costs mean no single measure makes derivatives martingales f…
FastSHAP speeds up Shapley value estimation for black-box models.
New method optimizes costly evaluations in Bayesian optimization.
Proposes an alternative method for quantifying uncertainty in complex models.
Generative AI reduces IR evaluation costs but introduces errors; this work provides reliable CIs.
This paper focuses on the problem of determining as large a region as possible where a function exceeds a given threshold with high probability. We assume that we only have access to a noise-corrupted version of the function and that function evaluations are costly. To select the next query point, we propose maximizing…
R-AutoEval+ improves model evaluation efficiency and reliability using adaptive synthetic data.
Paper proposes efficient algorithms for bandit problems with costly sampling.
Bayesian Deep Learning tackles inverse problems with neural networks and approximate computations.
We present an approach to adaptively utilize deep neural networks in order to reduce the evaluation time on new examples without loss of accuracy. Rather than attempting to redesign or approximate existing networks, we propose two schemes that adaptively utilize networks. We first pose an adaptive network evaluation sc…
For an autonomous agent, executing a poor policy may be costly or even dangerous. For such agents, it is desirable to determine confidence interval lower bounds on the performance of any given policy without executing said policy. Current methods for exact high confidence off-policy evaluation that use importance sampl…
Approximate Bayesian computation (ABC) is a method for Bayesian inference when the likelihood is unavailable but simulating from the model is possible. However, many ABC algorithms require a large number of simulations, which can be costly. To reduce the computational cost, Bayesian optimisation (BO) and surrogate mode…