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.
LES optimizes designs by sampling descent sequences, achieving strong sample efficiency.
CAGES optimizes expensive RL problems by efficiently learning gradients from multiple sources.
VES-Gamma adapts EI using information-theoretic principles.
JES optimizes expensive functions by considering joint entropy over input and output spaces.
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…
We introduce a novel Entropy-driven Monte Carlo (EdMC) strategy to efficiently sample solutions of random Constraint Satisfaction Problems (CSPs). First, we extend a recent result that, using a large-deviation analysis, shows that the geometry of the space of solutions of the Binary Perceptron Learning Problem (a proto…
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 …
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.
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…
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…
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…
Direct contextual policy search methods learn to improve policy parameters and simultaneously generalize these parameters to different context or task variables. However, learning from high-dimensional context variables, such as camera images, is still a prominent problem in many real-world tasks. A naive application o…
RES improves robustness in Bayesian optimization.
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 …
An algorithmic limit of compressed sensing or related variable-selection problems is analytically evaluated when a design matrix is given by an overcomplete random matrix. The replica method from statistical mechanics is employed to derive the result. The analysis is conducted through evaluation of the entropy, an expo…
A framework for efficient multi-objective optimization using entropy search.
MESMOC optimizes constrained multi-objective problems efficiently.
Study improves materials discovery for high-entropy alloys using sparse linear models.
NES improves robust optimization with noisy inputs.
Improves neural network search in combinatorial spaces of mathematical symbols.
This paper studies a classic maximum entropy sampling problem (MESP), which aims to select the most informative principal submatrix of a prespecified size from a covariance matrix. MESP has been widely applied to many areas, including healthcare, power system, manufacturing and data science. By investigating its Lagran…
Improved MESMOC+ optimizes constrained multi-objective problems efficiently.
A new acquisition function RMES improves Bayesian optimization performance.
DE-QT detects optimal Q-learning stopping points.
Bayesian optimization (BO) is a model-based approach to sequentially optimize expensive black-box functions, such as the validation error of a deep neural network with respect to its hyperparameters. In many real-world scenarios, the optimization is further subject to a priori unknown constraints. For example, training…
Hybridizes CEM and gradient descent for efficient model-predictive control.
Contemporary global optimization algorithms are based on local measures of utility, rather than a probability measure over location and value of the optimum. They thus attempt to collect low function values, not to learn about the optimum. The reason for the absence of probabilistic global optimizers is that the corres…
An energy based approach for stabilizing a mechanical system has offered a simple yet powerful control scheme. However, since it does not impose such strong constraints on parameter space of the controller, finding appropriate parameter values for an optimal controller is known to be hard. This paper intends to generat…
This paper generalizes BO uncertainty measures using decision-theoretic entropies.
SymCircuit learns PC structure via entropy-regularized RL, improving inference efficiency and accuracy.
A new method for multi-objective Bayesian optimization using entropy search and variational lower bound maximization.
Framework uses optimal transport for neural architecture search.
Bayesian search optimizes exploration of feasible solutions under expensive constraints.
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…
Proposes integrating global and local entropy for more reliable LLMs.
The paper extends Perelman's theorems on Ricci flow entropy.
TES optimizes black-box functions efficiently with minimal approximations.
We calculate the volume entropy of local Hermitian symmetric spaces of noncompact type in terms of its invariant , , .
Improved MORE algorithm reduces regret in black-box optimization and RL tasks.
GIBBON unifies Bayesian optimization for various problem types.
Combines global and local search for efficient global optimization with Gaussian processes.
We prove minimal entropy rigidity for complete, finite volume manifolds locally isometric to a product of rank one symmetric spaces of dimension at least 3: the locally symmetric metric uniquely minimizes (normalized) entropy among all Riemannian metrics. The corresponding theorem is true for maps into these spaces as …
Local gaps in Ricci shrinkers depend only on dimension.
Improving Bayesian Optimization via Training-Aware Conditional Diffusion Models
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…
In quadruped gait learning, policy search methods that scale high dimensional continuous action spaces are commonly used. In most approaches, it is necessary to introduce prior knowledge on the gaits to limit the highly non-convex search space of the policies. In this work, we propose a new approach to encode the symme…