arXiv research

Model stitching compares neural representations, revealing insights not captured by CKA.

This paper challenges the conventional wisdom about temporal difference learning's superiority in stitching experience.

Improves RL from historical data by stitching trajectories.

Many real-world applications require robust algorithms to learn point processes based on a type of incomplete data --- the so-called short doubly-censored (SDC) event sequences. We study this critical problem of quantitative asynchronous event sequence analysis under the framework of Hawkes processes by leveraging the …

Recovering manifold geometry from geodesic intersections.

A new approach selects tuning parameters for embedding methods.

Detect spacetime curvature without rulers and clocks in 3D.

Task loss matching misrepresents similarity between neural network layers.

The paper proposes a learning-theoretic perspective on representation alignment.

Improves BC policies by generating new plausible trajectories.

A new method for learning gradient flows from population dynamics.

A parametric point process model is developed, with modeling based on the assumption that sequential observations often share latent phenomena, while also possessing idiosyncratic effects. An alternating optimization method is proposed to learn a "registered" point process that accounts for shared structure, as well as…

This work presents a new robust PCA method for foreground-background separation on freely moving camera video with possible dense and sparse corruptions. Our proposed method registers the frames of the corrupted video and then encodes the varying perspective arising from camera motion as missing data in a global model.…

Research on manifold learning within a density ridge estimation framework has shown great potential in recent work for both estimation and de-noising of manifolds, building on the intuitive and well-defined notion of principal curves and surfaces. However, the problem of unwrapping or unfolding manifolds has received r…

In this paper, we identify an interesting kind of error in the output of Unsupervised Neural Machine Translation (UNMT) systems like \textit{Undreamt}(footnote). We refer to this error type as \textit{Scrambled Translation problem}. We observe that UNMT models which use \textit{word shuffle} noise (as in case of Undrea…

Proposes a continuous, differentiable model from local adaptive models.

Policy analysts wish to visualize a range of policies for large simulator-defined Markov Decision Processes (MDPs). One visualization approach is to invoke the simulator to generate on-policy trajectories and then visualize those trajectories. When the simulator is expensive, this is not practical, and some method is r…

Let $M_1$ and $M_2$ be two $n$-dimensional smooth manifolds with boundary. Suppose we glue $M_1$ and $M_2$ along some boundary components (which are, therefore, diffeomorphic). Call the result $N.$ If we have a group $G$ acting continuously on $M_1,$ and also acting continuously on $M_2,$ such that the actions are comp…

This paper generalizes graph representation for diverse data types.

This work presents a novel approach for robust PCA with total variation regularization for foreground-background separation and denoising on noisy, moving camera video. Our proposed algorithm registers the raw (possibly corrupted) frames of a video and then jointly processes the registered frames to produce a decomposi…

The deep layers of modern neural networks extract a rather rich set of features as an input propagates through the network. This paper sets out to harvest these rich intermediate representations for quantization with minimal accuracy loss while significantly reducing the memory footprint and compute intensity of the DN…

Study links in knitted textiles using knot theory.

Classification is the most important process in data analysis. However, due to the inherent non-convex and non-smooth structure of the zero-one loss function of the classification model, various convex surrogate loss functions such as hinge loss, squared hinge loss, logistic loss, and exponential loss are introduced. T…

Extends FC-RAG to anytime-valid sequential coverage for language model swarms.

This paper improves level generation using VAEs for coherent, logically following segments.

Linear-Core Surrogates combine fast optimization and statistical efficiency in classification and structured prediction.

Many signal processing algorithms break the target signal into overlapping segments (also called windows, or patches), process them separately, and then stitch them back into place to produce a unified output. At the overlaps, the final value of those samples that are estimated more than once needs to be decided in som…

CitySim dataset captures vehicle trajectories for safety research.

As Super-Resolution (SR) has matured as a research topic, it has been applied to additional topics beyond image reconstruction. In particular, combining classification or object detection tasks with a super-resolution preprocessing stage has yielded improvements in accuracy especially with objects that are small relati…

In several natural language tasks, labeled sequences are available in separate domains (say, languages), but the goal is to label sequences with mixed domain (such as code-switched text). Or, we may have available models for labeling whole passages (say, with sentiments), which we would like to exploit toward better po…

Bayesian RL enhances LLMs to reflectively explore and correct errors.

Defines a bundle map for currents on manifolds using higher covariant derivatives.

Improves local learning models for complex feature extraction.

Local semi-supervised method improves brain tissue classification in child MRI.

This thesis tackles data imperfections in ML, proposing methods to prevent discrimination and spurious feature learning.

This study compares mtl architectures for renewable power generation forecasting.

Paper tackles identifying an odd arm in a multi-armed bandit with restless Markov processes and trembling hand.

A new method prioritizes and recycles experiences for better reinforcement learning.

This project combines recent advances in experience replay techniques, namely, Combined Experience Replay (CER), Prioritized Experience Replay (PER), and Hindsight Experience Replay (HER). We show the results of combinations of these techniques with DDPG and DQN methods. CER always adds the most recent experience to th…

ReaPER improves learning efficiency by prioritizing reliable experiences.

This paper describes an improvement in Deep Q-learning called Reverse Experience Replay (also RER) that solves the problem of sparse rewards and helps to deal with reward maximizing tasks by sampling transitions successively in reverse order. On tasks with enough experience for training and enough Experience Replay mem…

Unified model optimizes experiment performance and reduces duration.

An important component of many Deep Reinforcement Learning algorithms is the Experience Replay which serves as a storage mechanism or memory of made experiences. These experiences are used for training and help the agent to stably find the perfect trajectory through the problem space. The classic Experience Replay howe…

New method designs experiments robustly for nonlinear estimation, improving parameter knowledge.

Experience reuse is key to sample-efficient reinforcement learning. One of the critical issues is how the experience is represented and stored. Previously, the experience can be stored in the forms of features, individual models, and the average model, each lying at a different granularity. However, new tasks may requi…

Optimal tests developed for sequential experiments with asymptotic properties.

Bayesian optimization for long-term outcomes using fast and slow experiments.

Two methods estimate effect size for online experiments, improving accuracy and efficiency.