Control, learning, and energy

Research

Reliable decisions under uncertainty

My research asks how learning can improve decisions when predictions, models, and information are imperfect. The work connects mathematical guarantees with control, AI agents, and the operation of energy infrastructure.

Papers Google Scholar

A connected research agenda

Good predictions create opportunities. Reliable algorithms make them useful.

Information, trust, and physical constraints connect these research themes.

Research papers

2026AAAI

Adaptive Riemannian Graph Neural Networks

Xudong Wang, Chris Ding, Tongxin Li, Jicong Fan

ARGNN learns a node specific Riemannian metric instead of imposing one fixed curvature on an entire graph. A diagonal metric parameterization and geometric regularization support efficient training, with convergence analysis and experiments on homophilic and heterophilic graphs.

Information Paper
How can a graph model adapt to different local geometries? information theory graph learning sample complexity compressed sensing graph neural networks Riemannian geometry
2026Applied Energy

Counterfactual load forecasting with LLM-structured events and representation learning

Yujie Chen, Yifei Gao, Runyao Yu, Yuhe Wu, Guangyu Wang, Yue Chen, Tongxin Li

NACF turns news into structured treatments and estimates load trajectories under alternative event conditions. Reweighting and representation balancing address observed confounding. Experiments examine factual accuracy and interpretable demand perturbations without claiming that unobserved counterfactual outcomes can be directly validated.

Energy Agents Paper
How might electricity demand change under a different news event? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization large language models LLM agents contextual control world models reinforcement learning dueling bandits
2026ACM e-Energy

Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning

Xudong Wang, Guoming Tang, Junyu Xue, Srinivasan Keshav, Tongxin Li, Chris Ding

DualNILM jointly recognizes appliance states and identifies energy injected behind the meter. Its transformer architecture combines temporal learning tasks to separate consumption from injections, with evaluation on measured and synthesized datasets.

Energy Information Paper
Can appliances be identified when solar and storage obscure meter readings? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization information theory graph learning sample complexity compressed sensing graph neural networks Riemannian geometry
2026AISTATS

Leveraging Machine-Learned Advice in Strategic Interactions with No-Regret Learners

Tinashe Handina, Tongxin Li, Kishan Panaganti, Eric Mazumdar, Adam Wierman

A measure of advice quality connects simulators and payoff predictions to strategic performance. The paper establishes benefits of reliable advice for approximate Stackelberg play and limitations on simultaneously exploiting accurate advice and protecting against inaccurate advice.

Games and equity Predictions Paper
How useful is imperfect advice against an adaptive opponent? game theory learning in games Bayesian games Stackelberg strategies equity public models learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization
2026IEEE PES International Meeting

PEARL: A Physics-Enhanced Adaptive Residual Learning Framework for PV Modeling

Yikai Lu, Yujie Chen, Tongxin Li

PEARL separates a physical photovoltaic simulator from a learned residual correction. A controlled benchmark compares several machine learning architectures and a coupled modeling baseline, examining accuracy while retaining the physical model as an interpretable reference.

Energy Predictions Paper
Can learned residuals improve physical models of solar generation? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization
2026POMACS

Prediction-Specific Design of Learning-Augmented Algorithms

Sizhe Li, Nicolas Christianson, Tongxin Li

Strong optimality refines the usual consistency and robustness tradeoff to account for each prediction. A bilevel optimization framework constructs algorithms for ski rental and one max search, with case studies in power management and trading.

Predictions Paper
Can an algorithm be optimal for the particular prediction it receives? learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization
2026CSEE JPES

Quantum Learning and Estimation for Coordinated Operation between Distribution Networks and Energy Communities

Yingrui Zhuang, Lin Cheng, Yuji Cao, Tongxin Li, Ning Qi, Yan Xu, Yue Chen

A hybrid quantum learning model estimates energy community responses to prices, while quantum amplitude estimation supports uncertainty calculations. Numerical studies assess accuracy and resource requirements. Reported timing advantages assume ideal quantum devices rather than deployed hardware.

Energy Information Paper
Can quantum methods assist coordination with limited local information? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization information theory graph learning sample complexity compressed sensing graph neural networks Riemannian geometry
2026IEEE Control Systems Letters

VigilMPC: Certified Online Tube Model Predictive Control for Safe Real-Time Neuromodulation

Yu Mao, Zhichao Liang, Junxiang Zhang, Quanying Liu, Tongxin Li

VigilMPC accepts a predictor update only when a backup plan remains feasible under tightened constraints. Recursive feasibility and constraint satisfaction follow under the tube MPC assumptions. Evaluation uses simulated neural dynamics and a switching benchmark, rather than clinical deployment.

Control Predictions Paper
Can a predictive controller safely update its learned dynamics online? model predictive control MPC stability recursive feasibility distributed control system level synthesis learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization
2026ICML

World Models in Pieces: Structural Certification for General Agents

Yikai Lu, Yifei Wu, Xinyu Lu, Tongxin Li

Structural certification links performance on compositional goals to local guarantees on world model transitions. The results identify reliable pieces of a model without requiring universal competence, and establish limits on the precision of such guarantees.

Agents Information Paper
Which parts of an agent's world model can support reliable planning? large language models LLM agents contextual control world models reinforcement learning dueling bandits information theory graph learning sample complexity compressed sensing graph neural networks Riemannian geometry
2025ACL Findings

Beyond Numeric Rewards: In-Context Dueling Bandits with LLM Agents

Fanzeng Xia, Hao Liu, Yisong Yue, Tongxin Li

The study identifies a gap between quick preference discovery and sustained exploitation by language agents. LEAD combines LLM reasoning with dueling bandit algorithms to obtain weak and strong regret guarantees, with evaluations under noisy and adversarial prompts.

Agents Predictions Paper
Can language agents learn reliably from pairwise preferences? large language models LLM agents contextual control world models reinforcement learning dueling bandits learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization
2025IEEE Transactions on Transportation Electrification

Hierarchical Game for Coupled Power System With Energy Sharing and Transportation System

Dongxiang Yan, Tongxin Li, Changhong Zhao, Han Wang, Yue Chen

A hierarchical game couples an energy sharing mechanism with transportation decisions and power network constraints. The paper establishes properties of the energy sharing equilibrium and reformulates the coupled problem for numerical solution.

Energy Games and equity Paper
How do charging, traffic, and local energy trading influence one another? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization game theory learning in games Bayesian games Stackelberg strategies equity public models
2025CDC

INSTRUCT MPC: A Human-LLM-in-the-Loop Framework for Context-Aware Control

Ruixiang Wu, Jiahao Ai, Tongxin Li

A language to distribution module converts contextual instructions into disturbance predictions for MPC. The framework closes the loop between instructions, predictions, and control, with a regret analysis for linear dynamics under the stated training assumptions.

Agents Control Energy Paper
How can human instructions improve predictive control? large language models LLM agents contextual control world models reinforcement learning dueling bandits model predictive control MPC stability recursive feasibility distributed control system level synthesis AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization
2025ACM e-Energy

Learning-Augmented Online Control for Decarbonizing Water Infrastructures

Jianyi Yang, Pengfei Li, Tongxin Li, Adam Wierman, Shaolei Ren

LAOC restricts online decisions to safe action sets while exploiting learned predictions for pump scheduling. Analysis characterizes the relationship between safety requirements and operating costs, with a building water supply case study.

Energy Control Predictions Paper
Can learned pump control reduce carbon costs while meeting safety constraints? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization model predictive control MPC stability recursive feasibility distributed control system level synthesis learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization
2025ACM e-Energy demo

Open In-Context Energy Management Platform

Yikai Lu, Tinko Sebastian Bartels, Ruixiang Wu, Fanzeng Xia, Xudong Wang, Yifei Wu, Haoxiang Yang, Tongxin Li

OpenCEM presents a platform design connecting energy time series, events, human context, and simulation. An on campus solar and battery installation motivates evaluation of context sensitive control. The paper describes planned data and API capabilities.

Agents Energy Control Paper
What would a shared benchmark for contextual energy management provide? large language models LLM agents contextual control world models reinforcement learning dueling bandits AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization model predictive control MPC stability recursive feasibility distributed control system level synthesis
2025IEEE Transactions on Transportation Electrification

Out-of-Distribution-Aware Electric Vehicle Charging

Tongxin Li, Chenxi Sun

OOD Charging adapts an awareness radius using temporal difference errors to balance learned scheduling with a robust baseline. Evaluation on ACN data examines behavioral changes during COVID 19 and the resulting distribution shift.

Energy Predictions Control Paper
How should charging schedules respond when user behavior changes? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization model predictive control MPC stability recursive feasibility distributed control system level synthesis
2025CDC

PredSLS: A System-Level Framework for Distributed Predictive Control

Yifei Wu, Jing Yu, Tongxin Li

PredSLS integrates forecasts directly into system level synthesis with locality constraints. Temporal decay of prediction effects supports finite horizon approximations, while spatial and temporal decomposition enable scalable computation across a network.

Control Predictions Paper
How can distributed controllers use predictions with limited communication? model predictive control MPC stability recursive feasibility distributed control system level synthesis learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization
2025NeurIPS

Reinforcement Learning with Imperfect Transition Predictions: A Bellman-Jensen Approach

Chenbei Lu, Zaiwei Chen, Tongxin Li, Chenye Wu, Adam Wierman

A Bayesian value function and Bellman Jensen gap quantify the value of imperfect transition forecasts. BOLA separates offline value learning from online adaptation, with sample efficiency analysis and experiments in synthetic environments and wind energy storage control.

Predictions Agents Energy Paper
How can reinforcement learning use imperfect forecasts beyond one step? learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization large language models LLM agents contextual control world models reinforcement learning dueling bandits AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization
2025Environmental Data Science

Robust machine-learned algorithms for efficient grid operation

Nicolas Christianson, Christopher Yeh, Tongxin Li, Mehdi Hosseini, Mahdi Torabi Rad, Azarang Golmohammadi, Adam Wierman

RobustML combines a learned dispatch algorithm with a bound on worst case cost. A combined cycle cogeneration case study examines dispatch and ramping costs as renewable penetration increases, including performance under distribution shift.

Energy Predictions Control Paper
Can learned dispatch policies remain reliable under distribution shift? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization model predictive control MPC stability recursive feasibility distributed control system level synthesis
2025IEEE Energy Sustainability Magazine

Robust Solar Hosting Capacity for Pasadena's Decarbonization: A Mathematical Program for Robustness Against Uncertainty

Han Xu, Yiheng Xie, Wenqi Cui, Tongxin Li, Steven Low

The Pasadena case study formulates solar hosting capacity planning as a robust optimization problem. It connects uncertainty in renewable generation and demand to distribution network constraints and practical choices along a municipal decarbonization pathway.

Energy Paper
How much distributed solar can a grid accommodate under uncertainty? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization
2024IEEE Transactions on Industrial Informatics

A Data-Driven Approach for Optimizing Early-Stage Electric Vehicle Charging Station Placement

Chenxi Sun, Tongxin Li, Xiaoying Tang

Two proximity models represent user satisfaction with station locations. Population, travel, road network, and point of interest data inform a submodular optimization problem, solved approximately with a greedy method and evaluated in Haikou.

Energy Paper
How can early charging infrastructure encourage electric vehicle adoption? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization
2024ICML

Building Socially-Equitable Public Models

Yejia Liu, Jianyi Yang, Pengfei Li, Tongxin Li, Shaolei Ren

The Equitable Objective trains a public predictive model around the outcomes of heterogeneous downstream agents. A policy gradient method and theoretical analysis study a more equitable distribution of performance, beyond optimizing prediction accuracy alone.

Games and equity Predictions Paper
Who benefits when many decision makers share one predictive model? game theory learning in games Bayesian games Stackelberg strategies equity public models learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization
2024NeurIPS

Disentangling Linear Quadratic Control with Untrusted ML Predictions

Tongxin Li, Hao Liu, Yisong Yue

DISC learns confidence in predictions of latent disturbance components. Competitive analysis covers linear and more general mixing functions, showing how online confidence adaptation can exploit accurate forecasts while maintaining protection against large errors.

Predictions Control Paper
Can a controller learn which components of a forecast to trust? learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization model predictive control MPC stability recursive feasibility distributed control system level synthesis
2024NeurIPS

Safe Exploitative Play with Untrusted Type Beliefs

Tongxin Li, Tinashe Handina, Shaolei Ren, Adam Wierman

The paper formalizes a risk and opportunity tradeoff for agents with uncertain beliefs about other players. Upper and lower bounds characterize the Pareto frontier in normal form and stochastic Bayesian games, with numerical illustrations of the tradeoff.

Games and equity Predictions Paper
How much risk comes from exploiting a predicted opponent type? game theory learning in games Bayesian games Stackelberg strategies equity public models learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization
2023NeurIPS

Anytime-Competitive Reinforcement Learning with Policy Prior

Jianyi Yang, Pengfei Li, Tongxin Li, Adam Wierman, Shaolei Ren

ACRL constrains cumulative costs relative to a policy prior at each round, rather than only in expectation over episodes. The analysis establishes cost guarantees and regret relative to the constrained optimum, with experiments in carbon aware computing.

Agents Predictions Control Paper
Can reinforcement learning enforce cost protection throughout an episode? large language models LLM agents contextual control world models reinforcement learning dueling bandits learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization model predictive control MPC stability recursive feasibility distributed control system level synthesis
2023NeurIPS

Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions

Tongxin Li, Yiheng Lin, Shaolei Ren, Adam Wierman

Q value predictions expose more information than an opaque policy recommendation. For a single trajectory MDP, the analysis characterizes consistency and robustness tradeoffs and shows how structured advice improves the guarantees available from a robust baseline.

Predictions Agents Paper
Does the structure of advice improve robust decision making? learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization large language models LLM agents contextual control world models reinforcement learning dueling bandits
2023IEEE Open Journal of Control Systems

Certifying Black-Box Policies With Stability for Nonlinear Control

Tongxin Li, Ruixiao Yang, Guannan Qu, Yiheng Lin, Adam Wierman, Steven H. Low

Simply blending two stabilizing policies can cause instability. An adaptive confidence policy instead uses approximate model information to certify stability, with competitive guarantees under bounded nonlinearity and evaluations on Cart Pole and charging under distribution shift.

Control Predictions Energy Paper
How can model based advice stabilize a learned nonlinear policy? model predictive control MPC stability recursive feasibility distributed control system level synthesis learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization
2022NeurIPS

Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity

Yiheng Lin, Yang Hu, Guannan Qu, Tongxin Li, Adam Wierman

A general analysis pipeline converts perturbation bounds for finite horizon control into dynamic regret bounds for MPC. It handles prediction errors in costs, dynamics, and disturbances, and extends the analysis to constrained and nonlinear settings under suitable regularity conditions.

Control Predictions Paper
How do forecast errors translate into regret for MPC? model predictive control MPC stability recursive feasibility distributed control system level synthesis learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization
2022POMACS

Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions

Tongxin Li, Ruixiao Yang, Guannan Qu, Guanya Shi, Chenkai Yu, Adam Wierman, Steven Low

A confidence parameter governs how much a linear quadratic controller trusts disturbance predictions. Competitive bounds describe the tradeoff between accurate and inaccurate advice, and a self tuning policy adapts confidence online using observed prediction quality.

Predictions Control Paper
Can predictive control balance consistency and robustness automatically? learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization model predictive control MPC stability recursive feasibility distributed control system level synthesis
2022Electric Power Systems Research

Towards balanced three-phase charging: Phase optimization in adaptive charging networks

Zixin Ye, Tongxin Li, Steven Low

PXA solves a relaxation of the phase optimization problem, with convergence guarantees under specified conditions. Its MPC implementation updates decisions online. Experiments use adaptive charging network data to examine energy delivery, charging costs, and phase balance.

Energy Control Paper
How should electric vehicle demand be assigned across electrical phases? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization model predictive control MPC stability recursive feasibility distributed control system level synthesis
2021SmartGridComm

Data-driven Electric Vehicle Charging Station Placement for Incentivizing Potential Demand

Chenxi Sun, Tongxin Li, Xiaoying Tang

A proximity based satisfaction model turns station placement into monotone submodular maximization. A greedy algorithm has a provable approximation ratio, with a Haikou case study using population, travel, and point of interest data.

Energy Paper
Where should a city place charging stations with a limited budget? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization
2021IEEE Transactions on Power Systems

Decentralized Provision of Renewable Predictions Within a Virtual Power Plant

Yue Chen, Tongxin Li, Changhong Zhao, Wei Wei

A decentralized prediction provision scheme lets consumers obtain local forecasts and exchange summaries with a virtual power plant. The analysis studies social surplus, convergence, and the demand gap relative to centralized information sharing.

Energy Games and equity Predictions Paper
Can local renewable forecasts support decentralized market coordination? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization game theory learning in games Bayesian games Stackelberg strategies equity public models learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization
2021POMACS

Information Aggregation for Constrained Online Control

Tongxin Li, Yue Chen, Bo Sun, Adam Wierman, Steven Low

Entropic aggregation summarizes a local controller's feasible actions for a remote decision maker. Penalized Predictive Control can approach the regret of full information control under causal invariance and a sufficiently long prediction window.

Control Information Energy Paper
How much feasibility information must a local controller share? model predictive control MPC stability recursive feasibility distributed control system level synthesis information theory graph learning sample complexity compressed sensing graph neural networks Riemannian geometry AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization
2021IEEE Transactions on Smart Grid

Learning-Based Predictive Control via Real-Time Aggregate Flexibility

Tongxin Li, Bo Sun, Yue Chen, Zixin Ye, Steven H. Low, Adam Wierman

Maximum entropy feedback summarizes the feasible actions of controllable loads. Reinforcement learning approximates this feedback for Penalized Predictive Control, reducing information and computation requirements. Charging data demonstrates the coordination approach.

Control Energy Agents Paper
How can an aggregator communicate flexibility in real time? model predictive control MPC stability recursive feasibility distributed control system level synthesis AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization large language models LLM agents contextual control world models reinforcement learning dueling bandits
2020Electric Power Systems Research

Classification of electric vehicle charging time series with selective clustering

Chenxi Sun, Tongxin Li, Steven H. Low, Victor O. K. Li

An iterative procedure extracts and clusters the tail portions of charging curves despite missing observations, variable lengths, scheduling effects, and measurement noise. Experiments on ACN data illustrate how these patterns can support useful charging models.

Energy Information Paper
What battery behavior can be learned from imperfect charging records? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization information theory graph learning sample complexity compressed sensing graph neural networks Riemannian geometry
2020POMACS

Competitive Algorithms for the Online Multiple Knapsack Problem with Application to Electric Vehicle Charging

Bo Sun, Ali Zeynali, Tongxin Li, Mohammad Hajiesmaili, Adam Wierman, Danny H. K. Tsang

A general fractional multiple knapsack model captures assignment and rate constraints. An online primal dual algorithm obtains near optimal competitive guarantees and is evaluated using charging traces. This work provides an online allocation foundation without requiring learned predictions.

Predictions Energy Paper
How should limited resources be allocated to requests arriving online? learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization
2020IEEE TSIPN

Learning Graphs From Linear Measurements: Fundamental Trade-Offs and Applications

Tongxin Li, Lucien Werner, Steven H. Low

Bounds for noisy and noiseless graph recovery connect sparsity, graph distributions, and linear measurements. A three stage recovery scheme and practical algorithm are studied on canonical graphs and electrical grid admittance matrices.

Information Energy Paper
What determines the sample complexity of network reconstruction? information theory graph learning sample complexity compressed sensing graph neural networks Riemannian geometry AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization
2020ACM e-Energy

ORC: An Online Competitive Algorithm for Recommendation and Charging Schedule in Electric Vehicle Charging Network

Bo Sun, Tongxin Li, Steven H. Low, Danny H. K. Tsang

ORC jointly recommends stations and prices while scheduling charging for sequential arrivals. A customizable value function supports competitive guarantees despite uncertainty in arrivals and user choices. Experiments compare operator revenue with benchmark scheduling methods.

Energy Predictions Paper
How can charging recommendations and schedules be coordinated online? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization
2019CDC

Learning Graph Parameters from Linear Measurements: Fundamental Trade-offs and Application to Electric Grids

Tongxin Li, Lucien Werner, Steven H. Low

Information theoretic bounds relate measurement requirements to graph complexity and recovery error. A recovery algorithm estimates topology and parameters from linear measurements, with demonstrations on graph families and electrical grid test cases.

Information Energy Paper
How many measurements are needed to recover a network? information theory graph learning sample complexity compressed sensing graph neural networks Riemannian geometry AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization
2018ITW

Maximum Likelihood Upper Bounds on the Capacities of Discrete Information Stable Channels

Tongxin Li

The paper derives a maximum likelihood upper bound using optimality conditions for discrete information stable channels. It recovers capacity for selected memoryless channels and develops a counting based bound for the binary deletion channel.

Information Paper
How can channel capacity be bounded through maximum likelihood? information theory graph learning sample complexity compressed sensing graph neural networks Riemannian geometry
2018ISIT

Quadratically Constrained Channels with Causal Adversaries

Tongxin Li, Bikash Kumar Dey, Sidharth Jaggi, Michael Langberg, Anand D. Sarwate

Capacity is characterized through a sequence of optimization problems under quadratic power constraints. Analytical and numerical bounds show that nonuniform power allocation can outperform uniform allocation in certain signal to noise regimes.

Information Paper
What rates are possible when a jammer observes transmissions causally? information theory graph learning sample complexity compressed sensing graph neural networks Riemannian geometry
2018IEEE DSW

Robust and Consistent Clustering Recovery via SDP Approaches

Chenxi Sun, Tongxin Li, Victor O. K. Li

A semidefinite programming approach recovers clustering structure in the presence of noise and outliers. The analysis gives conditions for exact recovery and consistency in a Gaussian similarity model, supported by synthetic experiments.

Information Paper
Can clusters be recovered from noisy similarities without knowing their number? information theory graph learning sample complexity compressed sensing graph neural networks Riemannian geometry
2016SPAWC

Fundamental limits and achievable strategies for low energy compressed sensing with applications in wireless communication

Tongxin Li, Mayank Bakshi, Pulkit Grover

A communication complexity model measures the energy required to move information during support recovery. Upper and lower bounds show how adaptive message scheduling can reduce decoding energy, with an application to multiuser detection in wireless systems.

Information Paper
What energy is required to decode a sparse signal? information theory graph learning sample complexity compressed sensing graph neural networks Riemannian geometry
2014ISIT

Group Testing with Prior Statistics

Tongxin Li, Chun Lam Chan, Wenhao Huang, Tarik Kaced, Sidharth Jaggi

Adaptive and nonadaptive testing algorithms exploit heterogeneous prior probabilities of defective items. Under the stated independence and sparsity assumptions, their measurement requirements approach information theoretic lower bounds up to explicit constant factors.

Information Predictions Paper
How can prior probabilities reduce the number of group tests? information theory graph learning sample complexity compressed sensing graph neural networks Riemannian geometry learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization

Connections across the work

Conceptual links between research themes, grounded in the papers below.

PredictionsControl

From prediction error to control performance

Trust adaptation in linear quadratic control and perturbation bounds for MPC connect the quality of a forecast to the cost of acting on it. Nonlinear certification adds stability to this picture.

AgentsControl

From language context to constrained actions

INSTRUCT MPC translates human instructions into disturbance predictions. World model certification and VigilMPC study complementary ways of deciding which learned models can support reliable planning and safe updates.

PredictionsAgents

Algorithms guide learned decision makers

Q value advice and imperfect transition forecasts provide structured information for reinforcement learning. LEAD applies a related principle by supporting language agents with dueling bandit algorithms.

ControlEnergy

Guarantees meet physical infrastructure

Aggregate flexibility communicates feasible actions for coordinated charging. Robust dispatch and safe water control connect performance guarantees to operational costs and physical constraints.

AgentsEnergy

Context becomes an energy system input

OpenCEM motivates shared contextual energy benchmarks. INSTRUCT MPC uses instructions for control, while counterfactual load forecasting represents news events as interventions on electricity demand.

Games and equityEnergy

Coordination through information and incentives

Virtual power plants use renewable predictions to coordinate consumers. Energy sharing and transportation games model how charging demand, prices, and network constraints interact.

InformationEnergy

Recovering structure from limited measurements

Graph recovery estimates electrical network parameters. Charging curve clustering and energy disaggregation recover useful structure from observed behavior and aggregate meter readings.

Open research directions

Questions motivated by this body of work. These are directions to explore, rather than established results.

Open question

Certified contextual control

Can contextual and world model updates be certified before they affect a physical system?

Connect language conditioned predictions with local world model guarantees and feasibility checks. An open challenge is preserving safety when both the context and the learned dynamics change during operation.

Starting points

Open question

Prediction quality as a decision resource

Which prediction errors actually matter for the decision being made?

Move from a single forecast accuracy score to guarantees that reflect the prediction, the task, and the affected transitions. This suggests adaptive confidence allocation across sources, horizons, and latent disturbances.

Starting points

Open question

Coordinated low carbon infrastructure

Can distributed energy and water resources adapt together under changing conditions?

Combine local feasibility summaries with distributed predictive control and contextual energy models. Open questions include communication limits, distribution shift, and guarantees across coupled physical networks.

Starting points

Open question

Accountable learning across agents

How should shared models balance efficiency, strategic risk, and equity?

Connect the downstream objectives of public models to robust strategic decision making. A key challenge is evaluating advice when its benefits and failures are distributed unevenly across participating agents.

Starting points