NeuralArTS categorizes neural ops in a type system for NAS.
problem Manual optimization of search spaces for NAS is inefficient.
method Developed NeuralArTS, a type system for categorizing network ops.
result NeuralArTS can be applied to convolutional layers.
New algorithms improve contextual search in the presence of adversarial corruptions.
problem Improving search accuracy in dynamic pricing settings with corrupted responses.
method Two algorithms based on binary search and gradient descent methods.
result Achieve near-optimal regret in the absence of adversarial corruptions and gracefully degrade with corrupted agents.
A game theory study on optimal hiding and searching strategies in discrete locations.
problem Optimal hiding and searching strategies in a two-person zero-sum game between a hider and a searcher.
method Proved the existence of optimal strategies, developed an algorithm to compute them, and compared with a simple strategy.
result Optimal hiding strategy involves hiding in each location with nonzero probability, and optimal searching strategy can be constructed with up to n simple sequences.
This work benchmarks and theorizes robust NAS under adversarial training.
problem Lack of benchmark evaluations and theoretical guarantees for robust NAS architectures under adversarial training.
method Released a comprehensive data set and established a generalization theory using the neural tangent kernel.
result Established a generalization theory for robust NAS architectures under adversarial training.
Theory explains why DARTS favors deep architectures over shallow ones.
problem DARTS selects architectures with dominated skip connections, leading to performance degradation.
method Theoretical analysis of operations' effects on network optimization; introduces sparse binary gates and path-depth-wise regularization.
result Theoretical proof that architectures with more skip connections converge faster.
XNAS optimizes neural architecture search using expert advice theory.
problem Optimizing neural architecture selection with minimal regret.
method Uses prediction with expert advice theory and dynamic architecture wiping.
result Achieves optimal worst-case regret and state-of-the-art results.
New algorithms improve neural architecture search with faster convergence.
problem Improving efficiency and accuracy of neural architecture search.
method Geometry-aware gradient algorithms to optimize continuous relaxation of discrete search spaces.
result Exceeds state-of-the-art results on CIFAR and ImageNet benchmarks.
Modeling high-frequency speculative markets as auction search processes.
problem Understanding trading dynamics in high-frequency order-driven markets.
method Total order book model with diffusion-drift-reaction model, inspired by foraging and chemotaxis.
result Analytic and numerical analysis of trading performance in various search mechanisms.
This paper uses information theory to improve risk modeling in big data.
problem Insufficient application of information theory in actuarial science.
method Explores information theory to uncover performance limits of insurance big data systems.
result Guidance for risk modeling and actuarial pricing systems.
Agent finds hidden objects in DAGs, stopping and restarting.
problem Finding hidden objects in a directed acyclic graph.
method Sequential search-and-stop strategy, learning from unknown distributions.
result Near-optimal strategy for unknown distributions, maximizing object collection.
Study finds new knots that bound Möbius bands in 4D space.
problem Identifying knots that bound Möbius bands in 4D.
method Large-scale computational search with knot invariant obstructions.
result New examples of knots with non-orientable 4-genus equal to 1.
The paper analyzes Random Search and introduces BLiN-MOS for bandit learning.
problem Understanding and optimizing hyperparameter tuning in metric measure spaces.
method Introducing scattering dimension to quantify performance, and developing BLiN-MOS for bandit learning.
result Random Search converges to optimal values with specific rates in noise-free and noisy environments.
Bayesian optimization with Gaussian processes speeds up searches for stationary points.
problem Accelerating searches for stationary points on potential energy surfaces.
method Unified Bayesian optimization view using Gaussian process regression with derivative observations, inverse-distance kernels, and active learning.
result Surrogates can reduce the number of expensive electronic structure evaluations by an order of magnitude.
In Classical Knot Theory and in the new Theory of Quantum Invariants substantial effort was directed toward the search for unknotting moves on links. We solve, in this note, several classical problems concerning unknotting moves. Our approach uses a new concept, Burnside groups of links, which establishes unexpected re…
Algorithm finds knot friends in 3-manifolds.
problem Identifying knots that share similar 3-manifold complements.
method Developed an algorithm using SnapPy and Regina.
result Constructed a census of simple knots with friends.
This paper considers the problem of adaptively searching for an unknown target using multiple agents connected through a time-varying network topology. Agents are equipped with sensors capable of fast information processing, and we propose a decentralized collaborative algorithm for controlling their search given noisy…
GES algorithm improves consistency for nonparametric DAG models.
problem Consistent estimation of nonparametric DAG models.
method Greedy equivalence search with new consistency proof for nonparametric families.
result Consistency of GES for general nonparametric DAG models with smooth factorization.
JES optimizes expensive functions by considering joint entropy over input and output spaces.
problem Optimizing expensive functions with limited evaluations.
method Joint Entropy Search (JES) considers joint entropy over input and output spaces.
result JES outperforms other information-theoretic methods in Bayesian optimization.
Study investor attention using search volume data before and after mobile device popularity.
problem Accurately measure investor attention in a fast-paced market.
method Compare investor attention using search volume data before and after mobile device popularization.
result Investor attention measured using search volume data is more accurate and faster after mobile device popularization.
This paper analyzes in details the Batalin-Vilkovisky quantization procedure for BF theories on n-dimensional manifolds and describes a suitable superformalism to deal with the master equation and the search of observables. In particular, generalized Wilson loops for BF theories with additional polynomial B-interaction…
Suggests stopping criteria for feature selection using mutual information.
problem Automatic determination of optimal feature subset size and stopping criterion.
method Monitoring conditional mutual information (CMI) among groups of variables using Renyi's α-entropy.
result Easy to implement stopping criteria for feature selection.
New tools reveal simple structure in complex hyperparameter loss surfaces near optima.
problem Understanding the behavior of hyperparameter loss surfaces near model optima.
method Developed a novel technique based on random search to uncover asymptotic features of the loss surface.
result Random search within the asymptotic regime yields a new distribution with parameters defining the loss surface.
PAC-MCTS addresses biased search in LLM-guided planning by dynamically pruning.
problem Systematic biases in LLMs lead to inefficient and unsafe search in deep planning tasks.
method Formulates node expansion as BAI under bounded bias, derives sample complexity bounds, and proposes PAC-MCTS for dynamic confidence bounds.
result PAC-MCTS improves robustness and efficiency by up to 78% fewer API evaluations and 3x higher sample efficiency.
The problem of content search through comparisons has recently received considerable attention. In short, a user searching for a target object navigates through a database in the following manner: the user is asked to select the object most similar to her target from a small list of objects. A new object list is then p…
We develop a description of higher gauge theory with higher groupoids as gauge structure from first principles. This approach captures ordinary gauge theories and gauged sigma models as well as their categorifications on a very general class of (higher) spaces comprising presentable differentiable stacks, as e.g. orbif…
A common belief in model-free reinforcement learning is that methods based on random search in the parameter space of policies exhibit significantly worse sample complexity than those that explore the space of actions. We dispel such beliefs by introducing a random search method for training static, linear policies for…
rpForests uses random projections to speed up kNN search.
problem Efficient kNN search in high-dimensional data.
method Random projection forests aggregating results from multiple random projection trees.
result rpForests achieves fast decay in missing rate and kNN distance discrepancy.
ITCA optimizes label combination for ambiguous outcomes in multi-class classification.
problem Ambiguous outcome labels in real-world datasets hinder accurate multi-class classification.
method Information-theoretic classification accuracy (ITCA) and search strategies (greedy, breadth-first) guide label combination.
result ITCA improves prediction accuracy and identifies ambiguous labels across diverse applications.
ForestDSH hashes improve nearest neighbor search in high-dimensional data.
problem High-dimensional classification and nearest neighbor search.
method Distribution-sensitive hashing using a forest of decision trees.
result ForestDSH hashes outperform LSH and state-of-the-art methods in speed and accuracy.
We introduce a method that uses the Cauchy-Crofton formula and a new curvature formula from integral geometry to reweight the sampling probabilities of Metropolis-within-Gibbs algorithms in order to increase their convergence speed. We consider algorithms that sample from a probability density conditioned on a manifold…
We consider two active binary-classification problems with atypical objectives. In the first, active search, our goal is to actively uncover as many members of a given class as possible. In the second, active surveying, our goal is to actively query points to ultimately predict the proportion of a given class. Numerous…
Recently proposed models which learn to write computer programs from data use either input/output examples or rich execution traces. Instead, we argue that a novel alternative is to use a glass-box loss function, given as a program itself that can be directly inspected. Glass-box optimization covers a wide range of pro…
Learning-based BnB improves maximum common subgraph search efficiency.
problem Finding maximum common subgraphs efficiently.
method Inspired by reinforcement learning, a heuristic to reach tree leaves early.
result Significantly reduces search tree size and outperforms existing BnB algorithms.
Fast algorithm for braid group Hecke representation, applied to knot invariants.
problem Computing topological invariants of knots efficiently.
method Representation-theoretic approach to braid group, leveraging quantum topology.
result Fast algorithm for Hecke representation of braid group, finding non-trivial braids.
In this paper we address three main objections of behavioral finance to the theory of rational finance, considered as anomalies the theory of rational finance cannot explain: Predictability of asset returns, The Equity Premium, (The Volatility Puzzle. We offer resolutions of those objections within the rational finance…
Adaptive sampling theory has shown that, with proper assumptions on the signal class, algorithms exist to reconstruct a signal in Rd with an optimal number of samples. We generalize this problem to the case of spatial signals, where the sampling cost is a function of both the number of samples taken and t…
Why does Deep Learning work? What representations does it capture? How do higher-order representations emerge? We study these questions from the perspective of group theory, thereby opening a new approach towards a theory of Deep learning. One factor behind the recent resurgence of the subject is a key algorithmic step…
Braid theory optimizes neural network structures.
problem Optimizing the architecture of neural networks.
method Using braid theory to describe and construct neural network structures.
result Braid-based networks outperform other architectures in classification tasks.
AI methods broaden signal discovery in scientific data.
problem Limited coverage of possible signals in model-dependent searches.
method Model-agnostic AI strategies for broad exploration.
result Enhanced discovery potential in experimental science.
The standard L-BFGS method relies on gradient approximations that are not dominated by noise, so that search directions are descent directions, the line search is reliable, and quasi-Newton updating yields useful quadratic models of the objective function. All of this appears to call for a full batch approach, but sinc…
Research on knots and their 4-manifold covers.
problem Determining if knots are slice.
method Analyzing double branched covers and computer searches.
result Methods to decide if knots are slice.
A new language for neural architecture search decouples search spaces and algorithms.
problem Current neural architecture search methods are limited to specific use-cases and lack general-purpose constructs.
method Proposes a formal language for encoding search spaces over general computational graphs, allowing modular, composable, and reusable encodings.
result The language enables easy experimentation with different search spaces and algorithms without reinventing the wheel.
This paper compares Grid Search, Random Search, and Genetic Algorithm for NAS.
problem Hyperparameter optimization for neural architecture search.
method Comparison of Grid Search, Random Search, and Genetic Algorithm.
result Genetic Algorithm outperforms Grid Search and Random Search in terms of accuracy and execution time.
This paper demonstrates the flaws of co-persistence theory proposed by Bollerslev and Engle (1993) which cause the theory can hardly be applied. With the introduction of the half-life of decay coefficient as the measure of the persistence, and both the weak definition of persistence and co-persistence in variance, this…
A framework uses POMDPs to assess hierarchical clustering quality.
problem No agreed methodology for evaluating hierarchical clustering without ground-truth labels.
method Modelled as a POMDP, assessing search support for hierarchical structures.
result Proposes a novel measure for assessing hierarchical clustering quality.
Efficient search methods can outperform random search on challenging tasks.
problem Comparing the performance of efficient and random search methods in neural architecture search.
method Comparison of weight sharing and random search methods on progressively larger search spaces for image classification and detection.
result Efficient search methods can provide substantial gains over random search on large, realistic tasks.
A new framework generates large hierarchical search spaces for neural architectures.
problem Discovering neural architectures from simple blocks is hard.
method Context-free grammars for a unified, scalable search space.
result Efficiently searches over complete architectures, outperforming existing methods.
MICO uses mutual information co-training to improve selective search efficiency.
problem Efficiently search and route unseen queries in large-scale search systems.
method Mutual Information Co-training framework for selective search with minimal supervision.
result Significantly improves performance on multiple metrics of selective search.