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Neural video compression has recently demonstrated significant potential to compete with conventional video codecs in terms of rate-quality performance. These learned video codecs are however associated with various issues related to decoding complexity (for autoencoder-based methods) and/or system delays (for implicit neural representation (INR) based models), which currently prevent them from being deployed in practical applications. In this paper, targeting a practical neural video codec, we propose a novel INR-based coding framework, PNVC, which innovatively combines autoencoder-based and overfitted solutions. Our approach benefits from several design innovations, including a new structural reparameterization-based architecture, hierarchical quality control, modulation-based entropy modeling, and scale-aware positional embedding. Supporting both low delay (LD) and random access (RA) configurations, PNVC outperforms existing INR-based codecs, achieving nearly 35\%+ BD-rate savings against HEVC HM 18.0 (LD) - almost 10\% more compared to one of the state-of-the-art INR-based codecs, HiNeRV and 5\% more over VTM 20.0 (LD), while maintaining 20+ FPS decoding speeds for 1080p content. This represents an important step forward for INR-based video coding, moving it towards practical deployment. The source code will be available for public evaluation.

AAAI 2025

PNVC: Towards Practical INR-based Video Compression

low level physics based vision

technical paper

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



While machine learning (ML) models of today have the potential to be useful in many societal applications, they also harbor the potential for great harm, be it perpetuating biases or compromising privacy. To prevent these harms, many (evolving) regulatory guardrails have been put in place; for instance European Union's GDPR and Biden's Executive Order which demand explainability, privacy, fairness and so on from models deployed in societal applications. Yet, most technical solutions in the Trustworthy ML literature which claim to meet these regulatory requirements are brittle and often fail at the task in hand. To this end, my research aims to make the field of Trustworthy ML reliable using mainstay concepts of Measurement, Mitigation and Maintenance. With these concepts, I develop end-to-end solutions for trustworthy ML by (1) exploring the limitations of existing approaches and (2) providing principled novel solutions exploiting interconnections with cryptography.

The Mainstays of Trustworthy Machine Learning

Integrating Large Language Models (LLMs) into software engineering unlocks new opportunities to automate manual processes but raises challenges around reliability, safety, and scalability. My research centers on this synergy, with two key objectives: first, harnessing LLMs to solve software engineering tasks traditionally dependent on labor-intensive, domain-specific methods, and second, applying robust software engineering principles to improve LLM safety and performance. This dual focus creates a powerful feedback loop, where LLMs drive innovation while engineering rigor ensures these systems meet the high standards required for real-world applications.

Advancing Intelligent Software Development and Trustworthy Models Through the Synergy of Software Engineering and LLMs

While advances in machine learning and the expansion of massive datasets have significantly improved predictive accuracy, the translation of these predictions into actionable decisions—alongside a robust understanding of associated risks—remains underexplored. My research focuses on developing methodology and theory in data-driven decision-making and uncertainty quantification that effectively address core data challenges. This paper presents two connected pillars of my research: data-driven contextual optimization, uncertainty quantification and reduction.

Statistical Methodologies for Decision-Making and Uncertainty Reduction in Machine Learning

Economic growth and development require a consistent supply of energy. Energy which has mainly been supplied from fossil fuels. The impacts of these on the environment such as global warming, raised an alarm on their use. As a result other sources of energy such as wind energy are used as alternatives for electricity production. Wind energy assessment nevertheless faces barriers due to its stochastic nature. This later creates various regimes, which traditional models can't always fit thereby producing poor estimates. In this work, we aim to use Large Language Models (LLMs) to predict the wind potential in a given location. Through this approach, we aim at lifting the barrier on energy problems in developing countries by providing knowledge on the state of wind energy in given locations.

Leveraging Large Language Models for Wind Energy Assessment

Deploying machine learning (ML) models in high-stakes domains such as healthcare and autonomous systems requires reliable uncertainty quantification (UQ) to ensure safe and accurate decision-making. 
Conformal prediction (CP) offers a robust, distribution-agnostic framework for UQ, providing valid prediction sets that guarantee a specified coverage probability. 
However, existing CP methods are often limited by assumptions that are violated in real-world scenarios, such as non-i.i.d. data, and by a lack of integration with modern machine learning workflows, particularly in large generative models. 
This research aims to address these limitations by advancing CP techniques to operate effectively in non-i.i.d. settings, improving predictive efficiency without sacrificing theoretical guarantees, and integrating CP directly into model training processes. 
These developments will enhance the practical applicability of CP for a wide range of ML tasks, enabling more reliable and interpretable models in high-stakes applications.

Reliable Uncertainty Quantification in Machine Learning via Conformal Prediction

Efficient human-agent collaboration requires understanding each other’s capabilities and establishing appropriate reliance. My thesis focuses on optimizing performance in mixed-initiative settings, where humans and agents dynamically contribute to decisions and actions. I first explore key factors shaping human reliance on decision-support agents, then examine how agents can model this reliance to initiate actions. My proposed work aims to enable agents to jointly provide decision and action support in multi-objective tasks, using bi-directional communication to enhance collaboration.

Adaptive Agents for Mixed-Initiative Human-AI Collaborations

Despite significant advancements in solving Markov Decision Processes (MDPs) and Simple Stochastic Games (SGs), scalability remains a challenge due to the exponential growth of their state spaces. This thesis aims to push the boundaries of state-of-the-art methods by tackling this issue using 1) explainability and 2) exploiting the model structure. First, we introduce the *1-2-3-Go* approach, which learns explainable policies from small MDP models and generalizes them to larger instances, improving scalability in MDPs. We then extend *Optimistic Value Iteration (OVI)* and *Sound Value Iteration (SVI)*—originally designed for MDPs—to SGs, improving efficiency in adversarial settings. Finally, we aim to exploit the *explainable policy representations* and the *model structure* to enhance both scalability and interpretability in SGs. This thesis contributes to both theoretical advancements and practical solutions for decision-making systems under uncertainty.

Scalable Solutions for Decision-Making Systems Using Explainable Policy Representations

Autonomous robots are becoming more versatile and widespread in our daily lives. From autonomous vehicles to companion robots for senior care, these human-centric systems must demonstrate a high degree of reliability in order to build trust and, ultimately, deliver social value. How safe is safe enough for robots to be wholeheartedly trusted by society? Is it sufficient if an autonomous vehicle can avoid hitting a fallen cyclist 99.9% of the time? What if this rate can only be achieved by the vehicle always stopping and waiting for the human to move out of the way? I argue that, for trustworthy deployment of robots in human-populated space, we need to complement standard statistical methods with clear-cut robust safety assurances under a vetted set of operation conditions. We need runtime learning to minimize the robot’s performance loss during safety-enforcing maneuvers by reducing its inherent uncertainty induced by its human peers, for example, their intent (does a human driver want to merge, cut behind, or stay in the lane?) or response (if the robot comes closer, how will the human react?). We need to close the loop between the robot’s learning and decision-making so that it can optimize efficiency by anticipating how its ongoing interaction with the human may affect the evolving uncertainty, and ultimately, its long-term performance.

From Gambits to Assurances: Game-Theoretic Integration of Safety and Learning for Interactive Robotics

Mechanistic interpretability is one of the most exciting and important research programs in current AI. My aim is to build some philosophical foundations for the program, along with setting out some concrete challenges and assessing progress to date. I will argue for the importance of propositional interpretability, which involves interpreting a system’s mechanisms and behavior in terms of propositional attitudes: attitudes (such as belief, desire, or subjective probability) to propositions (e.g. the proposition that it is hot outside). Propositional attitudes are the central way that we interpret and explain human beings and they are likely to be central in AI too. A central challenge is what I call thought logging: creating systems log all of the relevant propositional attitudes in an AI system over time. I will examine currently popular methods of interpretability (such as probing, sparse auto-encoders, and chain of thought methods) as well as philosophical methods of interpretation (including psychosemantics and representation theorems) to assess their strengths and weaknesses as methods of propositional interpretability.

PNVC: Towards Practical INR-based Video Compression

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