arXiv research

Paper extends knowledge gradient for preferential BO, overcoming computational challenges.

This paper studies Bayesian ranking and selection (R&S) problems with correlated prior beliefs and continuous domains, i.e. Bayesian optimization (BO). Knowledge gradient methods [Frazier et al., 2008, 2009] have been widely studied for discrete R&S problems, which sample the one-step Bayes-optimal point. When used ove…

A new method for efficient computation of Knowledge Gradient in Bayesian optimization.

Continual learning aims to enable machine learning models to learn a general solution space for past and future tasks in a sequential manner. Conventional models tend to forget the knowledge of previous tasks while learning a new task, a phenomenon known as catastrophic forgetting. When using Bayesian models in continu…

Improved knowledge gradient (iKG) outperforms the original KG algorithm in best arm identification problems.

In many applications of black-box optimization, one can evaluate multiple points simultaneously, e.g. when evaluating the performances of several different neural network architectures in a parallel computing environment. In this paper, we develop a novel batch Bayesian optimization algorithm --- the parallel knowledge…

A new method for optimizing black-box problems with constraints.

Gradient boosting can be seen as Gaussian process inference.

Gradient rollback explains neural models by identifying influential training examples.

Refines neural network predictions using background knowledge for improved accuracy.

In this work, we present direction-of-arrival (DoA) estimation algorithms based on the Krylov subspace that effectively exploit prior knowledge of the signals that impinge on a sensor array. The proposed multi-step knowledge-aided iterative conjugate gradient (CG) (MS-KAI-CG) algorithms perform subtraction of the unwan…

AdaS adapts SGD learning rate based on knowledge gain metrics.

In smooth strongly convex optimization, knowledge of the strong convexity parameter is critical for obtaining simple methods with accelerated rates. In this work, we study a class of methods, based on Polyak steps, where this knowledge is substituted by that of the optimal value, $f_*$. We first show slightly improved …

K-priors enable quick adaptation with minimal retraining.

A new method for faster multi-objective optimization by evaluating objectives separately.

This paper distills financial indicators into neural networks to reduce noise and improve accuracy.

Develops a simulation-based method to translate expert knowledge into prior distributions for Bayesian models.

We propose a sequential learning policy for noisy discrete global optimization and ranking and selection (R\&S) problems with high dimensional sparse belief functions, where there are hundreds or even thousands of features, but only a small portion of these features contain explanatory power. We aim to identify the spa…

Transformer-based method for causal discovery with prior knowledge integration.

We propose gradient adversarial training, an auxiliary deep learning framework applicable to different machine learning problems. In gradient adversarial training, we leverage a prior belief that in many contexts, simultaneous gradient updates should be statistically indistinguishable from each other. We enforce this c…

We consider the problem of sequentially making decisions that are rewarded by "successes" and "failures" which can be predicted through an unknown relationship that depends on a partially controllable vector of attributes for each instance. The learner takes an active role in selecting samples from the instance pool. T…

Gradient descent recovers low-rank matrices from corrupted measurements with double over-parameterization.

Improved scalable machine learning under heavy-tailed data.

SLED improves factuality in LLMs without external knowledge.

New bounds derived for KG algorithm's performance in finite time.

Meta-learning approach improves object detection on new classes.

Study examines explainable machine learning for monotonic models, finding Integrated gradients better for strong monotonicity.

Looped Transformers learn to implement multi-step gradient descent for in-context learning.

A method learns matrix factorization from diverse matrices and applies the knowledge to unseen matrices.

Bayesian optimization tackles non-convex, two-stage stochastic problems efficiently.

In order to learn quickly with few samples, meta-learning utilizes prior knowledge learned from previous tasks. However, a critical challenge in meta-learning is task uncertainty and heterogeneity, which can not be handled via globally sharing knowledge among tasks. In this paper, based on gradient-based meta-learning,…

New probabilistic approaches offer recourse recommendations even when causal models are imperfect.

Bayesian optimization methods improved for min max optimization problems.

Knowledge Distillation (KD) is a model-agnostic technique to improve model quality while having a fixed capacity budget. It is a commonly used technique for model compression, where a larger capacity teacher model with better quality is used to train a more compact student model with better inference efficiency. Throug…

Prior domain knowledge can greatly help to learn generative models. However, it is often too costly to hard-code prior knowledge as a specific model architecture, so we often have to use general-purpose models. In this paper, we propose a method to incorporate prior knowledge of feature relations into the learning of g…

Proposes a method to estimate causal effects over a range of DAGs, addressing uncertainty in prior knowledge.

Many continuous control tasks have bounded action spaces. When policy gradient methods are applied to such tasks, out-of-bound actions need to be clipped before execution, while policies are usually optimized as if the actions are not clipped. We propose a policy gradient estimator that exploits the knowledge of action…

Policy-gradient method controls multiple non-cohesive targets.

We give a simple proof that the Frank-Wolfe algorithm obtains a stationary point at a rate of $O(1/\sqrt{t})$ on non-convex objectives with a Lipschitz continuous gradient. Our analysis is affine invariant and is the first, to the best of our knowledge, giving a similar rate to what was already proven for projected gra…

We consider sequential decision making problems for binary classification scenario in which the learner takes an active role in repeatedly selecting samples from the action pool and receives the binary label of the selected alternatives. Our problem is motivated by applications where observations are time consuming and…

Two BO methods improve reliability optimization for rare failures.

New GPU algorithm boosts machine learning with larger datasets.

We analyze a fast incremental aggregated gradient method for optimizing nonconvex problems of the form $\min_x \sum_i f_i(x)$. Specifically, we analyze the SAGA algorithm within an Incremental First-order Oracle framework, and show that it converges to a stationary point provably faster than both gradient descent and s…

We consider the problem of estimating the parameters of the covariance function of a Gaussian process by cross-validation. We suggest using new cross-validation criteria derived from the literature of scoring rules. We also provide an efficient method for computing the gradient of a cross-validation criterion. To the b…

Bayesian optimisation tackles expensive black-box functions with constraints.

SWIFT method speeds up Heston model calibration for European options.

A novel efficient method for computing the Knowledge-Gradient policy for Continuous Parameters (KGCP) for deterministic optimization is derived. The differences with Expected Improvement (EI), a popular choice for Bayesian optimization of deterministic engineering simulations, are explored. Both policies and the Upper …

Adam converges with high probability under unconstrained non-convex smooth stochastic optimizations.