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.
Paper introduces MPES for top-k ranking BO with preferential observations.
Beam search improves UQ in LLMs by reducing duplicates and variance.
SJS model predicts label shifts in multinomial datasets.
We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of t…
Entropy Search (ES) and Predictive Entropy Search (PES) are popular and empirically successful Bayesian Optimization techniques. Both rely on a compelling information-theoretic motivation, and maximize the information gained about the of the unknown function; yet, both are plagued by the expensive computatio…
JES optimizes expensive functions by considering joint entropy over input and output spaces.
The paper develops approximations for Pearson's chi-square statistic and applies them to confidence intervals.
To model categorical response variables given their covariates, we propose a permuted and augmented stick-breaking (paSB) construction that one-to-one maps the observed categories to randomly permuted latent sticks. This new construction transforms multinomial regression into regression analysis of stick-specific binar…
The scientific method relies on the iterated processes of inference and inquiry. The inference phase consists of selecting the most probable models based on the available data; whereas the inquiry phase consists of using what is known about the models to select the most relevant experiment. Optimizing inquiry involves …
We propose the nuclear norm penalty as an alternative to the ridge penalty for regularized multinomial regression. This convex relaxation of reduced-rank multinomial regression has the advantage of leveraging underlying structure among the response categories to make better predictions. We apply our method, nuclear pen…
Sparse multinomial logistic regression for multiclass classification with feature selection.
The paper proves asymptotic normality for multinomial logistic regression on null covariates.
Bayesian search optimizes exploration of feasible solutions under expensive constraints.
VES-Gamma adapts EI using information-theoretic principles.
This work presents PESMOC, Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints, an information-based strategy for the simultaneous optimization of multiple expensive-to-evaluate black-box functions under the presence of several constraints. PESMOC can hence be used to solve a wide range…
We develop parallel predictive entropy search (PPES), a novel algorithm for Bayesian optimization of expensive black-box objective functions. At each iteration, PPES aims to select a batch of points which will maximize the information gain about the global maximizer of the objective. Well known strategies exist for sug…
We propose a novel neural architecture search algorithm via reinforcement learning by decoupling structure and operation search processes. Our approach samples candidate models from the multinomial distribution on the policy vectors defined on the two search spaces independently. The proposed technique improves the eff…
Protein Thoughts interprets protein interactions with clear reasoning, improving prediction accuracy.
A new method for multi-objective Bayesian optimization using entropy search and variational lower bound maximization.
Random forest models predict CLABSI risk in hospital admissions, with static models performing similarly to dynamic ones.
Paper proposes a policy-search algorithm to learn entropy-maximizing exploration policies in reward-free environments.
Unified framework connects EI and information-theoretic acquisition functions.
Parallel BO method for multi-objective optimization with constraints.
This paper studies an entropy-based multi-objective Bayesian optimization (MBO). The entropy search is successful approach to Bayesian optimization. However, for MBO, existing entropy-based methods ignore trade-off among objectives or introduce unreliable approximations. We propose a novel entropy-based MBO called Pare…
This article proposes Multinomial Probit Bayesian Additive Regression Trees (MPBART) as a multinomial probit extension of BART - Bayesian Additive Regression Trees (Chipman et al (2010)). MPBART is flexible to allow inclusion of predictors that describe the observed units as well as the available choice alternatives. T…
Proposes a method to use external machine-learning predictions in multinomial logistic regression.
Study improves materials discovery for high-entropy alloys using sparse linear models.
LES optimizes designs by sampling descent sequences, achieving strong sample efficiency.
New algorithms improve MPBART for HIV patient data.
A new algorithm learns MAGs from data more efficiently using entropy.
Trust-region methods have yielded state-of-the-art results in policy search. A common approach is to use KL-divergence to bound the region of trust resulting in a natural gradient policy update. We show that the natural gradient and trust region optimization are equivalent if we use the natural parameterization of a st…
Unknown constraints arise in many types of expensive black-box optimization problems. Several methods have been proposed recently for performing Bayesian optimization with constraints, based on the expected improvement (EI) heuristic. However, EI can lead to pathologies when used with constraints. For example, in the c…
We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research.We introduce a generative model with multinomial lik…
Improving Bayesian Optimization via Training-Aware Conditional Diffusion Models
RES improves robustness in Bayesian optimization.
The relaxed maximum entropy problem is concerned with finding a probability distribution on a finite set that minimizes the relative entropy to a given prior distribution, while satisfying relaxed max-norm constraints with respect to a third observed multinomial distribution. We study the entire relaxation path for thi…
Contextual policy search allows adapting robotic movement primitives to different situations. For instance, a locomotion primitive might be adapted to different terrain inclinations or desired walking speeds. Such an adaptation is often achievable by modifying a small number of hyperparameters. However, learning, when …
Efficiently models categorical data with low to medium class overlap, improving accuracy over standard distributions.
In this paper, we provide two new stable online algorithms for the problem of prediction in reinforcement learning, \emph{i.e.}, estimating the value function of a model-free Markov reward process using the linear function approximation architecture and with memory and computation costs scaling quadratically in the siz…
We propose a penalized likelihood method that simultaneously fits the multinomial logistic regression model and combines subsets of the response categories. The penalty is non differentiable when pairs of columns in the optimization variable are equal. This encourages pairwise equality of these columns in the estimator…
A framework for efficient multi-objective optimization using entropy search.
Paper tests for time-varying entropy in stock prices, finding periods of inefficiency.
MESMOC optimizes constrained multi-objective problems efficiently.
Hybridizes CEM and gradient descent for efficient model-predictive control.
NES improves robust optimization with noisy inputs.
As datasets capturing human choices grow in richness and scale -- particularly in online domains -- there is an increasing need for choice models that escape traditional choice-theoretic axioms such as regularity, stochastic transitivity, and Luce's choice axiom. In this work we introduce the Pairwise Choice Markov Cha…
Improves neural network search in combinatorial spaces of mathematical symbols.
Improved MESMOC+ optimizes constrained multi-objective problems efficiently.