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.
This paper defends SVMs against poisoning attacks using DBSCAN and hardness proofs.
Learning shrinks hard tail, improving inference performance.
We consider an agent's uncertainty about its environment and the problem of generalizing this uncertainty across observations. Specifically, we focus on the problem of exploration in non-tabular reinforcement learning. Drawing inspiration from the intrinsic motivation literature, we use density models to measure uncert…
Novelty search in low-dimensional space improves sample efficiency in exploration tasks.
Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This article discusses where links have been and should be established, introducing key concepts along the way. It argues that the hard …
In distributional reinforcement learning (RL), the estimated distribution of value function models both the parametric and intrinsic uncertainties. We propose a novel and efficient exploration method for deep RL that has two components. The first is a decaying schedule to suppress the intrinsic uncertainty. The second …
Inference-Time Scaling can be extended to domains prone to systematic failure using intrinsic statistics.
Research aims to bridge statistical learning to causal models in AI.
Study on predicting sequences with Gaussian constraints, linking to intrinsic volumes and metric complexity.
Researchers compute Bayes error for classification models using normalizing flows.
Paper shows hard computational limits for invariant causal prediction.
Many recently trained neural networks employ large numbers of parameters to achieve good performance. One may intuitively use the number of parameters required as a rough gauge of the difficulty of a problem. But how accurate are such notions? How many parameters are really needed? In this paper we attempt to answer th…
A grand challenge in reinforcement learning is intelligent exploration, especially when rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard-exploration domains: Montezuma's Revenge and Pitfall. On both games, current RL algorithms perform poorly, even those with intrinsic motivation, whic…
BYOL-Explore learns to explore visually-rich environments by predicting world dynamics.
New polynomial-time solutions found for training ReLU networks, mirroring Max-Cut complexity.
Curiosity-Critic improves world model training by focusing on cumulative prediction error.
Our work is a simple extension of the paper "Exploration by Random Network Distillation". More in detail, we show how to efficiently combine Intrinsic Rewards with Experience Replay in order to achieve more efficient and robust exploration (with respect to PPO/RND) and consequently better results in terms of agent perf…
New method uses hindsight to make exploration robust in stochastic environments.
The paper explores conditions for topological rigidity in quotients of the Davis complex.
Agent learns directed exploration policies to improve performance in hard games.
New algorithm learns sparse GLMs for binary outcomes efficiently.
Analyzing large volumes of high-dimensional data is an issue of fundamental importance in data science, molecular simulations and beyond. Several approaches work on the assumption that the important content of a dataset belongs to a manifold whose Intrinsic Dimension (ID) is much lower than the crude large number of co…
As reinforcement learning (RL) achieves more success in solving complex tasks, more care is needed to ensure that RL research is reproducible and that algorithms herein can be compared easily and fairly with minimal bias. RL results are, however, notoriously hard to reproduce due to the algorithms' intrinsic variance, …
By simulating the easy-to-hard learning manners of humans/animals, the learning regimes called curriculum learning~(CL) and self-paced learning~(SPL) have been recently investigated and invoked broad interests. However, the intrinsic mechanism for analyzing why such learning regimes can work has not been comprehensivel…
We show that log-periodic power-law (LPPL) functions are intrinsically very hard to fit to time series. This comes from their sloppiness, the squared residuals depending very much on some combinations of parameters and very little on other ones. The time of singularity that is supposed to give an estimate of the day of…
We introduce an exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed. The bonus is the error of a neural network predicting features of the observations given by a fixed randomly initialized neural network. We also introduce a method …
Introduces intrinsic Hopf-Lax semigroup linking to intrinsic slope.
New budget quantifies drift in closed-loop learning, improving reproducibility.
Study reveals adversarially robust domain adaptation is harder to generalize across domains.
Paper proves hardness of learning various complex models under local pseudorandom generators.
This work connects hardness of approximation and learning.
The classification of multi-class microarray datasets is a hard task because of the small samples size in each class and the heavy overlaps among classes. To effectively solve these problems, we propose novel Error Correcting Output Code (ECOC) algorithm by Enhance Class Separability related Data Complexity measures du…
Study on hard Legendrian unknots using normal rulings.
Survey of intrinsically linked or knotted graphs.
The paper studies properties of intrinsically Lipschitz constants in metric spaces.
Moving between 3-manifold triangulations is NP-hard
The paper provides results regarding the computational complexity of hybrid system identification. More precisely, we focus on the estimation of piecewise affine (PWA) maps from input-output data and analyze the complexity of computing a global minimizer of the error. Previous work showed that a global solution could b…
Recently the GAN generated face images are more and more realistic with high-quality, even hard for human eyes to detect. On the other hand, the forensics community keeps on developing methods to detect these generated fake images and try to guarantee the credibility of visual contents. Although researchers have develo…
Hard instances, which require a long time for a specific algorithm to solve, help (1) analyze the algorithm for accelerating it and (2) build a good benchmark for evaluating the performance of algorithms. There exist several efforts for automatic generation of hard instances. For example, evolutionary algorithms have b…
We introduce new sufficient conditions for intrinsic knotting and linking. A graph on n vertices with at least 4n-9 edges is intrinsically linked. A graph on n vertices with at least 5n-14 edges is intrinsically knotted. We also classify graphs that are 0, 1, or 2 edges short of being complete partite graphs with respe…
Tackles the computational hardness of HPC detection, conjecturing equivalence to PC detection.
A method for self-supervised representation learning in partially observable environments.
New graph shows edge deletion/contraction doesn't always result in intrinsically linked graphs.
New method estimates intrinsic dimensionality using angles, not distances.
A directed graph is if every embedding of that graph contains a non-split link , where each component of is a consistently oriented cycle in . A is a directed graph where each pair of vertices is connected by exactly one directed edge. We consider intr…
We classify graphs that are 0, 1, or 2 edges short of being complete partite graphs with respect to intrinsic linking and intrinsic knotting. In addition, we classify intrinsic knotting of graphs on 8 vertices. For graphs in these families, we verify a conjecture presented in Adams' "The Knot Book": If a vertex is remo…
Three hard diagrams of the unknot require extra crossings to simplify.
We study the fundamental tradeoffs between computational tractability and statistical accuracy for a general family of hypothesis testing problems with combinatorial structures. Based upon an oracle model of computation, which captures the interactions between algorithms and data, we establish a general lower bound tha…