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 studies pseudo-projective tensors on warped products.
We develop a probabilistic framework for sequential random projection.
New insights into continual learning for deep models, showing convergence issues but local linear solutions.
A novel online GP model captures long-term memory in sequential data.
We propose a method for non-projective dependency parsing by incrementally predicting a set of edges. Since the edges do not have a pre-specified order, we propose a set-based learning method. Our method blends graph, transition, and easy-first parsing, including a prior state of the parser as a special case. The propo…
Product models of low dimensional experts are a powerful way to avoid the curse of dimensionality. We present the ``under-complete product of experts' (UPoE), where each expert models a one dimensional projection of the data. The UPoE is fully tractable and may be interpreted as a parametric probabilistic model for pro…
PDTS improves robustness in sequential decision-making.
The abstract discusses financial irreversibility using quantum mechanics and projective geometry.
HiPPO framework optimizes memory compression for sequential data.
We present an information-theoretic framework for sequential adaptive compressed sensing, Info-Greedy Sensing, where measurements are chosen to maximize the extracted information conditioned on the previous measurements. We show that the widely used bisection approach is Info-Greedy for a family of -sparse signals b…
This paper speeds up iterative GP inference with warm starting.
A new method converts neural networks to function space for scalable sequential learning.
Dynamic treatment effects estimated over time using covariate balancing.
Enhances multi-project scheduling with multiple priority rules.
Improves inference-time alignment for diffusion models without updating weights.
New method for sequential probability assignment reduces regret using contextual Shtarkov sums.
Imitation Learning is a sequential task where the learner tries to mimic an expert's action in order to achieve the best performance. Several algorithms have been proposed recently for this task. In this project, we aim at proposing a wide review of these algorithms, presenting their main features and comparing them on…
We study the impact of learning on the optimal policy and the time-to-decision in an infinite-horizon Bayesian sequential decision model with two irreversible alternatives, exit and expansion. In our model, a firm undertakes a small-scale pilot project so as to learn, via Bayesian updating, about the project\textquoter…
We study the value of information in sequential compressed sensing by characterizing the performance of sequential information guided sensing in practical scenarios when information is inaccurate. In particular, we assume the signal distribution is parameterized through Gaussian or Gaussian mixtures with estimated mean…
Warm-start strategies speed up GP inference by 19x.
Seq2Tens uses tensors to efficiently represent sequences, improving performance on time series and video tasks.
This paper simplifies OPE in large state spaces using state abstractions.
When applying principal component analysis (PCA) for dimension reduction, the most varying projections are usually used in order to retain most of the information. For the purpose of anomaly and change detection, however, the least varying projections are often the most important ones. In this article, we present a nov…
The paper proposes a method to monitor deep learning predictions for retraining, reducing costs.
Algorithm identifies best arm in combinatorial bandits with semi-bandit feedback.
We study sequential change-point detection procedures based on linear sketches of high-dimensional signal vectors using generalized likelihood ratio (GLR) statistics. The GLR statistics allow for an unknown post-change mean that represents an anomaly or novelty. We consider both fixed and time-varying projections, deri…
We study the behavior of the Kähler-Ricci flow on some Fano bundle which is a trivial bundle on one Zariski open set. We show that if the fiber is blown up at one point or some weighted projective space blown up at the orbifold point and the initial metric is in a suitable kähler class, then the fibers…
Study optimizes zero-order strongly convex function minimization with higher order smoothness.
The problem of minimizing a continuously differentiable convex function over an intersection of closed convex sets is ubiquitous in applied mathematics. It is particularly interesting when it is easy to project onto each separate set, but nontrivial to project onto their intersection. Algorithms based on Newton's metho…
New method reduces memory usage for high-dimensional variable selection.
Generative adversarial nets (GANs) and variational auto-encoders have significantly improved our distribution modeling capabilities, showing promise for dataset augmentation, image-to-image translation and feature learning. However, to model high-dimensional distributions, sequential training and stacked architectures …
We study an online multi-task learning setting, in which instances of related tasks arrive sequentially, and are handled by task-specific online learners. We consider an algorithmic framework to model the relationship of these tasks via a set of convex constraints. To exploit this relationship, we design a novel algori…
End-to-end autonomous driving perception learns latent features for better performance.
Gated recurrent neural networks have achieved remarkable results in the analysis of sequential data. Inside these networks, gates are used to control the flow of information, allowing to model even very long-term dependencies in the data. In this paper, we investigate whether the original gate equation (a linear projec…
New method uses diffusion models to generate proteins with specific motifs.
Study efficient sequential evaluation of large language models using historical data.
Camera and lidar are important sensor modalities for robotics in general and self-driving cars in particular. The sensors provide complementary information offering an opportunity for tight sensor-fusion. Surprisingly, lidar-only methods outperform fusion methods on the main benchmark datasets, suggesting a gap in the …
Develops an online method for solving constrained optimization problems with debiasing techniques.
A new framework predicts hidden Markov model regimes online.
In this paper, we propose a method for image-set classification based on convex cone models, focusing on the effectiveness of convolutional neural network (CNN) features as inputs. CNN features have non-negative values when using the rectified linear unit as an activation function. This naturally leads us to model a se…
Majorizing measures control sequential complexities for online learning.
The paper investigates various generalizations of semisymmetric and pseudosymmetric manifolds.
Contextual bandits with linear payoffs, which are also known as linear bandits, provide a powerful alternative for solving practical problems of sequential decisions, e.g., online advertisements. In the era of big data, contextual data usually tend to be high-dimensional, which leads to new challenges for traditional l…
Neural networks are achieving state of the art and sometimes super-human performance on learning tasks across a variety of domains. Whenever these problems require learning in a continual or sequential manner, however, neural networks suffer from the problem of catastrophic forgetting; they forget how to solve previous…
New method uses few instruments to estimate complex causal effects.
In this note, we introduce a new type of warped products called as sequential warped products to cover a wider variety of exact solutions to Einstein's equation. First, we study the geometry of sequential warped products and obtain covariant derivatives, curvature tensor, Ricci curvature and scalar curvature formulas. …
Study finds conditions for certain warped product manifolds to be quasi-Einstein.
A new model for sequential memory using temporal predictive coding.