Explore Our Research

Our Machine Learning Research Team dives deep into the prevailing questions, trends and challenges that define machine learning today. Discover our latest insights, often published in collaboration with university labs and across industry-leading conferences.


ICLR 2024

VQ-TR: Vector Quantized Attention for Time Series Forecasting

We augment the attention mechanism by quantizing the query vectors to obtain a novel attention block for forecasting.


Journal of Computation and Graphical Statistics

Structural Discovery With Partial Ordering Information for Time-Dependent Data With Convergence Guarantees

Built upon existing work, this paper proposes an ADMM-based algorithm that handles the estimation of a linear SEM, in the presence of partial ordering information known as apriori.



Accelerating Approximate Thompson Sampling With Underdamped Langevin Monte Carlo

We found that approximate Thompson sampling with underdamped Langevin Monte Carlo is more sample efficient.


STOC 2024

Listing Cliques From Smaller Cliques

Explore our study centered on finding an output-sensitive listing of k-cliques in networks. 


UAI 2024

On Convergence of Federated Averaging Langevin Dynamics

We propose federated averaging Langevin algorithm (FA-LD) for uncertainty quantification with distributed clients and studied the convergence in convex scenarios.


UAI 2024 (Oral)

Reflected Schrödinger Bridge for Constrained Generative Modeling

We introduce the Reflected Schrodinger Bridge algorithm: an entropy-regularized optimal transport approach tailored for generating data within diverse bounded domains.



Graph Partitioning with a Move Budget

Approximation algorithms for k-partitioning when there is an initial partitioning of the network and want to achieve a "good" partitioning while moving as few nodes as possible.



Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes

We provide a framework for analyzing neural network architectures, such as the transformer, within the context of stochastic processes.


TMLR 2024

A VAE-based Framework for Learning Multi-Level Neural Granger-Causal Connectivity

We consider the problem of estimating neural Granger causality in the presence of entity-specific heterogeneity.


IEEE Transactions on Signal Processing 2024

A Communication-Efficient Algorithm for Federated Multilevel Stochastic Compositional Optimization

We consider the multilevel stochastic composite optimization problem in a distributed setting.


ICML 2024

Variational Schrödinger Diffusion Models

This paper pioneers the exploration of the ADAM alternative to SGD, a vital step for more transport-efficient diffusion models.


TMLR 2023

Learning To Abstain in the Presence of Uninformative Data

When the majority of the data is highly noisy, we propose a model for selecting data that is predictable, learnable and informative.


ICLR 2023 Workshop

On the Existence of a Trojaned Twin Model

We build a theoretic framework for UAP-based backdoor attacks. This paper proposes the concept of a Trojan twin model and a practical heuristic algorithm.


AAAI 2023

Non-Reversible Parallel Tempering for Deep Posterior Approximation

The popular cosine learning rate is a special case of non-reversible parallel tempering.


ICML 2023

Modeling Temporal Data as Continuous Functions With Stochastic Process Diffusion

Here we augment generative diffusion with stochastic processes, which allows us to model irregular time sequences. 


ICML 2023

Provably Convergent Schrödinger Bridge With Applications to Probabilistic Time Series Imputation

In this paper, we propose a time series imputation and prediction model based on conditional generative AI and Schrodinger bridge SDE. 



Risk Bounds for Aleatoric Uncertainty Recovery

We provide risk bounds for learning the data dependent variance function in a hetereoscedastic regression setting.


UAI 2023

Short-Term Temporal Dependency Detection Under Heterogeneous Event Dynamic With Hawkes Processes 

Here we propose a modified Hawkes process model that can better handle the unobserved background dynamics and artifacts.


UAI 2023

Inference and Sampling of Point Processes From Diffusion Excursions 

This paper proposes a new point process framework modeling arrival times through latent diffusion processes.


UAI 2023

In- or Out-of-Distribution Detection via Dual Divergence Estimation 

For Out-of-Distribution (OOD) detection, we propose a principled yet simple approach of (empirically) estimating KL-divergence, in its dual form, between the training and test sets. 


UAI 2023

Information Theoretic Clustering via Divergence Maximization Among Clusters 

For principled clustering with minimal apriori assumption, we propose to maximize the Kullback-Leibler divergence in its dual form between the underlying data distributions associated to clusters. 


NeurIPS 2023

Nearly Tight Bounds for Differentially Private Multiway Cut

We develop the first differentially private min s-t cut algorithm with tight approximation guarantees.


NeurIPS 2023

Topology-Aware Uncertainty for Image Segmentation

We propose a framework for modeling the uncertainty of the existence of topological structure.


ICLR 2023

Learning to Segment From Noisy Annotations: A Spatial Correction Approach

In this paper, we propose a novel Markov model for segmentation noisy annotations that encodes both spatial correlation and bias. 


International Journal of Forecasting

A Multi-Task Encoder-Dual-Decoder Framework for Mixed Frequency Data Prediction

This paper develops a neural network-based approach that handles forecasting and nowcasting in a unified fashion.


Short version is accepted at ICML 2023 Workshop

Reflected Schrödinger Bridge for Constrained Generative Modeling

We introduce the Reflected Schrodinger Bridge algorithm: an entropy-regularized optimal transport approach for generating data within diverse bounded domains.


UAI 2022

Estimating Transfer Entropy Under Long-Ranged Dependencies

Here we estimate transfer entropy directly from conditional likelihoods computed in-sample using any timeseries forecaster trained per maximum likelihood principle. 


ICLR 2022

Interacting Contour Stochastic Gradient Langevin Dynamics

Interacting parallel stochastic gradient Langevin dynamics can be faster than the single long chain alternative with the same computational budget.


SIAM Journal on Optimization 2022

General Feasibility Bounds for Sample Average Approximation via Vapnik-Chervonenkis Dimension

This work improves the current understanding regarding the feasibility of sample average approximation solutions when solving stochastic programming problems without recourse.


UAI 2022

Stability of SGD: Tightness Analysis and Improved Bounds 

We construct a lower bound to examine the tightness of the existing theoretical results in the literature on generalization of Stochastic Gradient Descent.


IROS 2022

Scalable Safety-Critical Policy Evaluation With Accelerated Rare Event Sampling

We propose a policy evaluation based on adaptive sampling techniques for agents to evaluate and act on rare event, especially regarding safety constraints.



A Manifold View of Adversarial Risk

We propose a new angle for decomposing adversarial risk into two orthogonal terms. Such a decomposition provides new directions for improving the robustness of a model.


NeurIPS 2021

Topological Detection of Trojaned Neural Networks 

A trojaned neural network has a Trojan behavior related shortcut in its neuron graph. Such structural information is extracted with tools from algebraic topology.


COLING 2020, Workshop on Financial Narrative Processing and MultiLing Financial Summarization

Information Extraction From Federal Open Market Committee Statements

We present a novel approach to unsupervised information extraction by identifying and extracting relevant concept-value pairs from textual data.