Ioannis Antonoglou

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. CoRR Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy P. Lillicrap, Jean-Baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, Ioannis Antonoglou, Rohan Anil, Sebastian Borgeaud, Andrew M. Dai, Katie Millican, Ethan Dyer, Mia Glaese, Thibault Sottiaux, Benjamin Lee, Fabio Viola, Malcolm Reynolds, Yuanzhong Xu, James Molloy, Jilin Chen, Michael Isard, Paul Barham, Tom Hennigan, Ross McIlroy, Melvin Johnson, Johan Schalkwyk, Eli Collins, Eliza Rutherford, Erica Moreira, Kareem Ayoub, Megha Goel, Clemens Meyer, Gregory Thornton, Zhen Yang, Henryk Michalewski, Zaheer Abbas, Nathan Schucher, Ankesh Anand, Richard Ives, James Keeling, Karel Lenc, Salem Haykal, Siamak Shakeri, Pranav Shyam, Aakanksha Chowdhery, Roman Ring, Stephen Spencer, Eren Sezener, et al.
2022	A*	`conf`	Planning in Stochastic Environments with a Learned Model. ICLR Ioannis Antonoglou, Julian Schrittwieser, Sherjil Ozair, Thomas K. Hubert, David Silver
2021	A*	`conf`	Learning and Planning in Complex Action Spaces. ICML Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Mohammadamin Barekatain, Simon Schmitt, David Silver
2021	J	`jnl`	Learning and Planning in Complex Action Spaces. CoRR Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Mohammadamin Barekatain, Simon Schmitt, David Silver
2021	—	`conf`	Machine Translation Decoding beyond Beam Search. EMNLP (1) Rémi Leblond, Jean-Baptiste Alayrac, Laurent Sifre, Miruna Pislar, Jean-Baptiste Lespiau, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals
2021	J	`jnl`	Machine Translation Decoding beyond Beam Search. CoRR Rémi Leblond, Jean-Baptiste Alayrac, Laurent Sifre, Miruna Pislar, Jean-Baptiste Lespiau, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals
2021	A*	`conf`	Online and Offline Reinforcement Learning by Planning with a Learned Model. NeurIPS Julian Schrittwieser, Thomas Hubert, Amol Mandhane, Mohammadamin Barekatain, Ioannis Antonoglou, David Silver
2021	J	`jnl`	Online and Offline Reinforcement Learning by Planning with a Learned Model. CoRR Julian Schrittwieser, Thomas Hubert, Amol Mandhane, Mohammadamin Barekatain, Ioannis Antonoglou, David Silver
2021	A*	`conf`	Vector Quantized Models for Planning. ICML Sherjil Ozair, Yazhe Li, Ali Razavi, Ioannis Antonoglou, Aäron van den Oord, Oriol Vinyals
2021	J	`jnl`	Vector Quantized Models for Planning. CoRR Sherjil Ozair, Yazhe Li, Ali Razavi, Ioannis Antonoglou, Aäron van den Oord, Oriol Vinyals
2020	J	`jnl`	Causally Correct Partial Models for Reinforcement Learning. CoRR Danilo J. Rezende, Ivo Danihelka, George Papamakarios, Nan Rosemary Ke, Ray Jiang, Theophane Weber, Karol Gregor, Hamza Merzic, Fabio Viola, Jane Wang, Jovana Mitrovic, Frederic Besse, Ioannis Antonoglou, Lars Buesing
2020	J	`jnl`	Mastering Atari, Go, chess and shogi by planning with a learned model. Nat. Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy P. Lillicrap, David Silver
2020	A*	`conf`	Monte-Carlo Tree Search as Regularized Policy Optimization. ICML Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos
2020	J	`jnl`	Monte-Carlo Tree Search as Regularized Policy Optimization. CoRR Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos
2019	J	`jnl`	Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model. CoRR Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy P. Lillicrap, David Silver
2018	J	`jnl`	Bayesian Optimization in AlphaGo. CoRR Yutian Chen, Aja Huang, Ziyu Wang, Ioannis Antonoglou, Julian Schrittwieser, David Silver, Nando de Freitas
2018	A*	`conf`	Learning to Search with MCTSnets. ICML Arthur Guez, Theophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, David Silver
2018	J	`jnl`	Learning to Search with MCTSnets. CoRR Arthur Guez, Théophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, David Silver
2017	J	`jnl`	Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. CoRR David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy P. Lillicrap, Karen Simonyan, Demis Hassabis
2017	J	`jnl`	Mastering the game of Go without human knowledge. Nat. David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy P. Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, Demis Hassabis
2016	A*	`conf`	A Test of Relative Similarity For Model Selection in Generative Models. ICLR (Poster) Wacha Bounliphone, Eugene Belilovsky, Matthew B. Blaschko, Ioannis Antonoglou, Arthur Gretton
2016	J	`jnl`	Fast Non-Parametric Tests of Relative Dependency and Similarity. CoRR Wacha Bounliphone, Eugene Belilovsky, Arthur Tenenhaus, Ioannis Antonoglou, Arthur Gretton, Matthew B. Blaschko
2016	J	`jnl`	Mastering the game of Go with deep neural networks and tree search. Nat. David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis
2016	A*	`conf`	Prioritized Experience Replay. ICLR (Poster) Tom Schaul, John Quan, Ioannis Antonoglou, David Silver
2015	J	`jnl`	Human-level control through deep reinforcement learning. Nat. Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis
2014	—	`conf`	Unit Tests for Stochastic Optimization. ICLR Tom Schaul, Ioannis Antonoglou, David Silver
2013	J	`jnl`	Playing Atari with Deep Reinforcement Learning. CoRR Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin A. Riedmiller

"""APK Code Analysis Extractor.

Decompiles and disassembles APK DEX bytecode at the method level,
producing per-method content and reference records analogous to
the Binary Ninja code_binja_* tables.

Uses androguard + JADX + apktool to replicate Binary Ninja analysis
depth for Android applications.
"""

import gc
import hashlib
import inspect
import logging
import os
import threading
import time
from datetime import datetime, timezone
from typing import Any, Dict, Optional

from redb.extractors.decompiler.apk.analyzer import APKCodeAnalyzer
from redb.extractors.enum import Tag
from redb.extractors.extractor import Extractor


class DecompileAPK(Extractor):
    """APK code analysis extractor — produces multi-table ClickHouse export.

    Follows the same pattern as DecompileBinja for consistency.
    """

    def __init__(
        self,
        filepath,
        log,
        exporters=None,
        index_prefix=None,
        known_benign=False,
        known_malicious=False,
        filetype=None,
        decompile_modules=None,
    ):
        super().__init__(
            filepath,
            log,
            exporters,
            index_prefix,
            known_benign=known_benign,
            known_malicious=known_malicious,
        )
        self.log.debug(inspect.currentframe().f_code.co_name)
        self.analysis_results = None
        self.analyzer = None
        self.filetype = filetype or "apk"
        self.decompile_modules = decompile_modules or {"all"}

        try:
            self.APK_DECOMPILE_TIMEOUT = int(
                os.getenv("APK_DECOMPILE_TIMEOUT", "600")
            )
        except ValueError:
            self.log.warning(
                "Invalid APK_DECOMPILE_TIMEOUT value, using default of 600 seconds"
            )
            self.APK_DECOMPILE_TIMEOUT = 600

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.cleanup_run()

    def calculate_md5(self, input_str):
        """Calculate MD5 hash of a string."""
        return hashlib.md5(input_str.encode("utf-8")).hexdigest()

    def cleanup_run(self):
        """Clean up after analysis."""
        try:
            if self.analyzer:
                self.analyzer.cleanup()
                self.analyzer = None
            gc.collect()
        except Exception as e:
            self.log.error(f"Error in cleanup: {e}")

    def analyze_apk(self) -> Optional[Dict[str, Any]]:
        """Run APK code analysis and return results."""
        self.log.debug("Starting APK code analysis")
        try:
            self.analyzer = APKCodeAnalyzer(
                filepath=self.filepath,
                timeout=self.APK_DECOMPILE_TIMEOUT,
                log=self.log,
                decompile_modules=self.decompile_modules,
            )
            results = self.analyzer.extract()
            return results
        except Exception as e:
            self.log.error(f"Error in APK code analysis: {e}")
            import traceback
            self.log.error(f"Traceback: {traceback.format_exc()}")
            return None
        finally:
            self.cleanup_run()

    def extract(self):
        """Extract and process all analysis results.

        Uses daemon thread with timeout, same pattern as DecompileBinja.
        """
        self.log.debug(inspect.currentframe().f_code.co_name)

        extraction_completed = False
        extraction_result = False
        extraction_error = None

        def do_extraction():
            nonlocal extraction_completed, extraction_result, extraction_error
            try:
                results = self.analyze_apk()
                if not results:
                    extraction_result = False
                else:
                    self.analysis_results = results
                    self.analysis_results["sha256"] = self.sha256
                    self.analysis_results["sha1"] = self.sha1
                    self.analysis_results["md5"] = self.md5
                    extraction_result = True
            except Exception as e:
                extraction_error = e
                extraction_result = False
            finally:
                extraction_completed = True

        extraction_thread = threading.Thread(target=do_extraction)
        extraction_thread.daemon = True
        extraction_thread.start()

        start_time = time.time()
        while (
            not extraction_completed
            and (time.time() - start_time) < self.APK_DECOMPILE_TIMEOUT
        ):
            time.sleep(1)

        if not extraction_completed:
            self.log.error(
                f"APK extraction timed out after {self.APK_DECOMPILE_TIMEOUT} seconds"
            )
            self.cleanup_run()
            return None

        if extraction_error:
            self.log.error(f"Error in APK extraction: {extraction_error}")
            return None

        return self.analysis_results if extraction_result else None

    def prepare_export_data(self, exporter_type: str) -> Any:
        """Prepare data for database export."""
        self.log.debug(inspect.currentframe().f_code.co_name)
        if not self.analysis_results:
            return None

        if exporter_type == "ClickHouseExporter":
            now = datetime.now(timezone.utc)
            export = {"multi_table": True}

            # Table 1: Decompiled method content
            if self.analysis_results.get("decompiled_content"):
                export["decompiled_content"] = {
                    "table": "code_apk_decompiled_methods_content",
                    "data": [
                        [
                            f["decompiled_method_hash"],
                            f["decompiled_method"],
                            f.get("decompiled_method_type", "UNKNOWN"),
                            1 if f.get("decompiled_has_string_encryption") else 0,
                            1 if f.get("decompiled_has_reflection_calls") else 0,
                            1 if f.get("decompiled_excessive_goto_count") else 0,
                            now,
                        ]
                        for f in self.analysis_results["decompiled_content"]
                    ],
                    "column_names": [
                        "decompiled_method_hash",
                        "decompiled_method",
                        "decompiled_method_type",
                        "decompiled_has_string_encryption",
                        "decompiled_has_reflection_calls",
                        "decompiled_excessive_goto_count",
                        "analysis_date",
                    ],
                    "column_type_names": [
                        "FixedString(64)",
                        "String",
                        "Enum8('USER'=1, 'LIBRARY'=2, 'UNKNOWN'=5)",
                        "UInt8",
                        "UInt8",
                        "UInt8",
                        "DateTime64(3, 'UTC')",
                    ],
                }

            # Table 2: Decompiled method references
            if self.analysis_results.get("decompiled_refs"):
                export["decompiled_refs"] = {
                    "table": "code_apk_decompiled_methods_references",
                    "data": [
                        [
                            self.analysis_results["sha256"],
                            f["decompiled_method_hash"],
                            f.get("smali_method_hash"),
                            f.get("decompiled_class_name", ""),
                            f.get("decompiled_method_name", ""),
                            f.get("decompiled_method_signature", ""),
                            f.get("decompiled_method_prototype", ""),
                            f.get("functions_caller", []),
                            f.get("functions_call", []),
                            now,
                        ]
                        for f in self.analysis_results["decompiled_refs"]
                    ],
                    "column_names": [
                        "sha256",
                        "decompiled_method_hash",
                        "smali_method_hash",
                        "decompiled_class_name",
                        "decompiled_method_name",
                        "decompiled_method_signature",
                        "decompiled_method_prototype",
                        "functions_caller",
                        "functions_call",
                        "analysis_date",
                    ],
                    "column_type_names": [
                        "FixedString(64)",
                        "FixedString(64)",
                        "Nullable(FixedString(64))",
                        "LowCardinality(String)",
                        "LowCardinality(String)",
                        "String",
                        "String",
                        "Array(String)",
                        "Array(String)",
                        "DateTime64(3, 'UTC')",
                    ],
                }

            # Table 3: Smali method content
            if self.analysis_results.get("smali_content"):
                export["smali_content"] = {
                    "table": "code_apk_smali_methods_content",
                    "data": [
                        [
                            f["smali_method_hash"],
                            f["smali_method"],
                            f.get("smali_method_type", "UNKNOWN"),
                            f.get("smali_instructions_count", 0),
                            f.get("smali_register_count", 0),
                            1 if f.get("smali_has_string_encryption") else 0,
                            1 if f.get("smali_has_reflection_calls") else 0,
                            1 if f.get("smali_excessive_goto_count") else 0,
                            f.get("smali_flattened_score", 0.0),
                            f.get("smali_mba_score", 0.0),
                            now,
                        ]
                        for f in self.analysis_results["smali_content"]
                    ],
                    "column_names": [
                        "smali_method_hash",
                        "smali_method",
                        "smali_method_type",
                        "smali_instructions_count",
                        "smali_register_count",
                        "smali_has_string_encryption",
                        "smali_has_reflection_calls",
                        "smali_excessive_goto_count",
                        "smali_flattened_score",
                        "smali_mba_score",
                        "analysis_date",
                    ],
                    "column_type_names": [
                        "FixedString(64)",
                        "String",
                        "Enum8('USER'=1, 'LIBRARY'=2, 'UNKNOWN'=5)",
                        "UInt32",
                        "UInt16",
                        "UInt8",
                        "UInt8",
                        "UInt8",
                        "Float64",
                        "Float64",
                        "DateTime64(3, 'UTC')",
                    ],
                }

            # Table 4: Smali method references
            if self.analysis_results.get("smali_refs"):
                export["smali_refs"] = {
                    "table": "code_apk_smali_methods_references",
                    "data": [
                        [
                            self.analysis_results["sha256"],
                            f["smali_method_hash"],
                            f.get("decompiled_method_hash"),
                            f.get("smali_class_name", ""),
                            f.get("smali_method_name", ""),
                            f.get("smali_method_signature", ""),
                            now,
                        ]
                        for f in self.analysis_results["smali_refs"]
                    ],
                    "column_names": [
                        "sha256",
                        "smali_method_hash",
                        "decompiled_method_hash",
                        "smali_class_name",
                        "smali_method_name",
                        "smali_method_signature",
                        "analysis_date",
                    ],
                    "column_type_names": [
                        "FixedString(64)",
                        "FixedString(64)",
                        "Nullable(FixedString(64))",
                        "LowCardinality(String)",
                        "LowCardinality(String)",
                        "String",
                        "DateTime64(3, 'UTC')",
                    ],
                }

            # Table 5: Method similarity metrics (content-based fuzzy matching)
            if self.analysis_results.get("similarity_metrics"):
                export["method_similarity_metrics"] = {
                    "table": "code_apk_method_similarity_metrics",
                    "data": [
                        [
                            f["smali_method_hash"],
                            f.get("ssdeep_smali"),
                            f.get("tlsh_smali"),
                            f.get("ssdeep_smali_normalized"),
                            f.get("tlsh_smali_normalized"),
                            f.get("minhash", []),
                            now,
                        ]
                        for f in self.analysis_results["similarity_metrics"]
                    ],
                    "column_names": [
                        "smali_method_hash",
                        "ssdeep_smali",
                        "tlsh_smali",
                        "ssdeep_smali_normalized",
                        "tlsh_smali_normalized",
                        "minhash",
                        "analysis_date",
                    ],
                    "column_type_names": [
                        "FixedString(64)",
                        "Nullable(String)",
                        "Nullable(FixedString(72))",
                        "Nullable(String)",
                        "Nullable(FixedString(72))",
                        "Array(UInt8)",
                        "DateTime64(3, 'UTC')",
                    ],
                }

            # Table 5b: CFG method features (structural/topological)
            if self.analysis_results.get("cfg"):
                export["cfg_methods"] = {
                    "table": "code_apk_cfg_methods",
                    "data": [
                        [
                            cfg["smali_method_hash"],
                            cfg["cfg_topology_hash"],
                            cfg["block_count"],
                            cfg["edge_count"],
                            cfg.get("cfg_instructions_count", 0),
                            cfg.get("call_count", 0),
                            cfg["cyclomatic_complexity"],
                            cfg.get("loop_count", 0),
                            cfg.get("max_depth", 0),
                            cfg.get("max_fan_out", 0),
                            cfg.get("md_index_topdown", 0),
                            cfg.get("md_index_bottomup", 0),
                            cfg.get("prime_product_smali", 0),
                            cfg.get("cfg_feature_tlsh"),
                            cfg.get("wl_minhash", []),
                            cfg.get("bb_features", []),
                            cfg.get("cfg_adjacency", []),
                            now,
                        ]
                        for cfg in self.analysis_results["cfg"]
                        if cfg is not None
                    ],
                    "column_names": [
                        "smali_method_hash",
                        "cfg_topology_hash",
                        "block_count",
                        "edge_count",
                        "cfg_instructions_count",
                        "call_count",
                        "cyclomatic_complexity",
                        "loop_count",
                        "max_depth",
                        "max_fan_out",
                        "md_index_topdown",
                        "md_index_bottomup",
                        "prime_product_smali",
                        "cfg_feature_tlsh",
                        "wl_minhash",
                        "bb_features",
                        "cfg_adjacency",
                        "analysis_date",
                    ],
                    "column_type_names": [
                        "FixedString(64)",
                        "FixedString(16)",
                        "UInt16",
                        "UInt16",
                        "UInt32",
                        "UInt16",
                        "UInt16",
                        "UInt16",
                        "UInt16",
                        "UInt16",
                        "UInt64",
                        "UInt64",
                        "UInt64",
                        "Nullable(FixedString(72))",
                        "Array(UInt8)",
                        "Array(Array(UInt16))",
                        "Array(UInt32)",
                        "DateTime64(3, 'UTC')",
                    ],
                }

            # Table 6: Strings — reuse code_binja_strings_raw for cross-format correlation
            # DEX strings are MUTF-8; string_raw = string since no encoding difference
            if self.analysis_results.get("strings"):
                export["strings_raw"] = {
                    "table": "code_binja_strings_raw",
                    "data": [
                        [
                            self.analysis_results["sha256"],
                            s["string"],
                            s["string"],  # string_raw = string (MUTF-8 decoded to UTF-8)
                            s.get("string_encoding", "UTF8"),
                            s.get("string_offset", 0),
                            s.get("string_length", len(s["string"])),
                            s.get("string_length", len(s["string"])),  # string_raw_length = string_length
                            s.get("string_entropy", 0.0),
                        ]
                        for s in self.analysis_results["strings"]
                    ],
                    "column_names": [
                        "sha256",
                        "string",
                        "string_raw",
                        "string_encoding",
                        "string_offset",
                        "string_length",
                        "string_raw_length",
                        "string_entropy",
                    ],
                    "column_type_names": [
                        "FixedString(64)",
                        "String",
                        "String",
                        "LowCardinality(String)",
                        "UInt64",
                        "UInt32",
                        "UInt32",
                        "Float32",
                    ],
                }

            # Table 7: Analysis errors
            if self.analysis_results.get("analysis_errors"):
                export["analysis_errors"] = {
                    "table": "code_apk_analysis_errors",
                    "data": [
                        [
                            self.analysis_results["sha256"],
                            f.get("class_name"),
                            f.get("method_name"),
                            f.get("error_location", "unknown"),
                            f.get("error_message", ""),
                            f.get("error_type", "unknown"),
                            self.calculate_md5(
                                f"{f.get('error_message', '')}"
                                f"{f.get('class_name', '')}"
                                f"{f.get('method_name', '')}"
                                f"{f.get('error_location', 'unknown')}"
                            ),
                            "new",
                            now,
                        ]
                        for f in self.analysis_results["analysis_errors"]
                    ],
                    "column_names": [
                        "sha256",
                        "class_name",
                        "method_name",
                        "error_location",
                        "error_message",
                        "error_type",
                        "error_hash",
                        "status",
                        "analysis_date",
                    ],
                    "column_type_names": [
                        "FixedString(64)",
                        "Nullable(String)",
                        "Nullable(String)",
                        "LowCardinality(String)",
                        "Nullable(String)",
                        "Nullable(String)",
                        "FixedString(32)",
                        "Enum8('new'=1, 'investigating'=2, 'fixed'=3, 'wontfix'=4)",
                        "DateTime64(3, 'UTC')",
                    ],
                }

            return export

        return None

    def tag(self) -> str:
        """Return the tag for this extractor."""
        return Tag.APK_DECOMPILED.value

    def get_clickhouse_table(self) -> str:
        """Not used directly as we're handling multiple tables."""
        pass